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10,019
|
https://devpost.com/software/promob
|
We met during the YES Climathon, we shared our experiences and points of view and we came up with a unique and sustainable idea to address mobility issue, considering both Environmental and Public Health related aspects!
Built With
creativity
|
ProMob
|
An application dedicated to professionals to offer transport alternatives that would be safer, easier, and reduce air pollution.
|
['Ali Khalife', 'Manon Dangelser', 'Asem Alsofiany', 'Marco Iseppon', 'José Ángel Sanz del Real']
|
[]
|
['creativity']
| 16
|
10,019
|
https://devpost.com/software/brokinggreen
|
We built a prototype for a funding platform targeted towards green MSMEs. Our team members worked in that field and were first hand witnesses to the funding challenges these companies undergo. Today with the current crisis theses have been further exacerbated. We hope to alleviate that pressure on companies essential to a more sustainable society.
|
BrokingGreen
|
Channeling private capital into green MSME's
|
['Oscar Pitcho']
|
[]
|
[]
| 17
|
10,019
|
https://devpost.com/software/the-green-forest-crowdfunding-loans-for-sme
|
We are a group of strategist, product, data scientist and engineer who are keen to support hard-hit sectors (especially Small Medium Enterprises) who are struggling to go through COVID-19. Faced with government and banks bureaucracy, SMEs find their liquidity drying up to continue with their operations. There's an available supply from individual lenders who are keen to help their local businesses, and hence the birth of The Green Forest.
The Green Forest - supporting Small and Medium Enterprises sustainably going through COVID-19.
|
The Green Forest
|
Crowdfunding Sustainable Loans for SME
|
['Katherine Elbert', 'Giovanni Borghi', 'Gustavo Alejandro', 'Laurence Watson']
|
[]
|
[]
| 18
|
10,019
|
https://devpost.com/software/crop-e
|
Inspiration
Promote sustainable and local agriculture
What it does
Helps build a connection between farmers and their costumers
How we built it
Group discussions, great coaching and support
Challenges we ran into
Define the main problem and work as a team in the selected goal
Accomplishments that we're proud of
Teamwork, teamwork, teamwork
What we learned
Synergy is much better! Learning by doing..
What's next for Crop-e
Hope a lot!!! There's a lot more to be done. But GOGOGOATS are ready and very excited :)
Try it out
www.canva.com
|
Crop-E
|
Improvements in food supply chain and strengthen local sustainable agriculture
|
['Evgenia Vamvakousi', 'Giulia Giaganini', 'Riccardo Olivi', 'Michele Lometti']
|
[]
|
[]
| 19
|
10,019
|
https://devpost.com/software/susiety
|
The Birth of Susiety
Through the 2020 Covid crisis, the awareness for a more sustainable lifestyle has icreased with the urban population. However, living sustainably is far from easy and simple. We wanted to provide a means for everyone to get access to their local sustainable resources and projects. We focus on the most impactful secotrs, food and fashion as a proof of concept.
|
Susiety
|
Susiety promotes sustainable consumption by connecting young city dwellers and their local comminuties.
|
['Yannick Hollenweger']
|
[]
|
[]
| 20
|
10,019
|
https://devpost.com/software/tripfix
|
Covid-19 impacts our mobility choices. We looked how the opportunities from the Covid-19 crisis in transport can be used to create a more sustainable world. In order to do that, we started our journey to a solution that is innovative, has a high feasibility as well as a big impact.
|
TriPfix
|
Application to change travel behavior in a sustainable way
|
['Anne-Claire Legon']
|
[]
|
[]
| 21
|
10,019
|
https://devpost.com/software/e-meeting-a-sustainability-report-for-e-life-apps
|
Inspiration
What it does
How I built it
Challenges I ran into
Accomplishments that I'm proud of
What I learned
What's next for E-Meeting: A Sustainability Report for E-life Apps
Our group's interest was the urban environment. Now with Coronacrisis we are doing a lot more activities online, such as business meetings and pub quizzes via video calling apps. While this is very good for many urban problems such as traffic jams, many people don't realise it also comes at a significant environmental cost. We want to raise awareness and incentivise businesses in the ICT industry to become more sustainable.
|
E-Meeting: A Sustainability Report for E-life Apps
|
We are increasingly living online (e-life). The increased use of e.g. video calling apps has a large environmental footprint. We will raise awareness and spur businesses to become more sustainable.
|
['Gabriëlle Smith']
|
[]
|
[]
| 22
|
10,019
|
https://devpost.com/software/sustainable-and-clean-ppe-lm3uzf
|
Whole story:
Amid a global shortage of PPE, we have looked into the use of bioplastics as a viable and sustainable alternative.
Inspiration
Wanting to create a future where a low waste society is compatible with reducing the transmission of disease. To help medical professionals, currently working for a healthier society, make their lives and livelihoods safer.
What it does
Medical gloves made from compostable biopolymer materials with antimicrobial properties. These will limit the spread of pathogens whilst reducing medical waste and reducing carbon footprint of production.
How I built it
Researched the science of making our antiviral biopolymers for a PPE application including current products and reading the most recent papers. Researched the market to identify where to target our product.
Challenges I ran into
As a group we had a lot of ideas and lots of detailed discussion! The challenge was narrowing down the large field of existing research and formulating these ideas into a specific product.
Accomplishments that I'm proud of
Finding a creative solution to two huge problems, and making practical plan to implement our product in the real world.
What I learned
That there is so much science out there in the hope of creating a more sustainable world! Implementation is the next step but finding an appropriate niche and a suitable market for investment is the hard part.
What's next for Sustainable and clean PPE
Getting the product out there and having quantitative results of its effectiveness so it can be more successfully marketed to other high risk disease transmission areas such as airports. The same technology could be implemented into similar products such as foot covers or medical packaging. Then it all becomes about efficiency of production - making manufacture and disposal systems larger scale to maximise the output while minimising materials and transport.
|
Sustainable and clean PPE
|
Medical gloves made from compostable biopolymer materials with antimicrobial properties to replace less sustainable currently used products, such as Latex.
|
['Katie Lihou']
|
[]
|
[]
| 23
|
10,019
|
https://devpost.com/software/a-game-for-a-change
|
Inspiration: the increased use of the digital social media in a differentiated audience and the appeal of ludic education
What it does: it raises awareness on environmental issued while encouraging an active participation in creating possible solutions.
How I built it: we looked for data about the use of digital social channels and information about ludic education and existing games or challenges to give a concrete example of what we aim to create. We have all come up with different ideas thanks to our different background, age, experience, interests and knowledge.
Challenges I ran into: identify the way to implement our idea and the limited time.
Accomplishments that I'm proud of: the identification of a test market, the ability to collaborate and obtain results in only 48 hours and working with people who were complete strangers at first.
What I learned: I learned a lot not only about communication and ludic education, but also regarding team building and team work, and sustainable solutions of course.
What's next for A game for a change: identification of real stakeholders and definition of details to make it happen!
|
A game for a change
|
Covid-19 shifts the attention away from climate change, but has increased the use of digital channels. We'll use them to promote climate change awareness.
|
['Chiara Ferrari']
|
[]
|
[]
| 24
|
10,019
|
https://devpost.com/software/pandacademy
|
Devpost Template:
Inspiration
Better decision making, conscious citizens.
What it does
Interactively empowers electorate decision making with experts reliable and peer-reviewed opinion and works.
How I built it
IBM Bot that can be shared on different platforms. All-inclusive team brainstorming.
Challenges I ran into
Time constraint. Narrowing down the reach and the goal, not being too ambitious. Seeing a clear path from the beginning.
Accomplishments that I'm proud of
Team cohesion, prototyping building process.
What I learned
Teamwork can lighten up cloudy moments.
What's next for Pandacademy
Further development and content reach. Growing the community, stimulating the citizens sustainable knowledge.
Built With
ibm
ibm-watson
slack
Try it out
pandacademyworkspace.slack.com
|
Pandacademy
|
A conscious electorate empowers world change towards a sustainable future.
|
['Joana Amorim Freire', 'Elisabetta Maria Messina', 'Lorenzo Lugaresi', 'Agustin Musi', 'Lorenzo Pala']
|
[]
|
['ibm', 'ibm-watson', 'slack']
| 25
|
10,019
|
https://devpost.com/software/team-55-the-end-of-life-of-cloth-masks-8fn0zv
|
In the coming months, around 137,000,000 masks will be used per month by the French population, and will be then thrown away. Our solution aims at collecting and recycling these used cloth masks, through developing at the same time the textile waste industry.
|
Team 55 - The end of life of cloth masks
|
A story of how the Covid crisis could structure the future of the textile recycling industry in a European country
|
['Alix-Anne Paris']
|
[]
|
[]
| 26
|
10,019
|
https://devpost.com/software/benet
|
The Story
Created during the climathon 2020 our young and dynamic team based in Switzerland has built an innovative tool, aiming to construct together a more sustainable world.
We think that teleworking if balanced correctly can improve productivity, work-life balance, and reduce our ecological impact. Therefore we are developing a platform for guidance, relying on quantitative and qualitative analysis.
Our products will help your business to achieve a smart and effective transition towards the perfect equilibrium. Moreover, we provide your managers with a unique tool to help them make the right decision to get the best productivity and cost-efficiency.
Built With
matlab
Try it out
benoittruc.wixsite.com
prezi.com
|
BeNET
|
Our solution assists you in achieving a smart transition towards the perfect teleworking equilibrium to obtain the best productivity, collaborator satisfaction and cost-efficiency.
|
['Etienne Droz', 'Benoît Truc']
|
[]
|
['matlab']
| 27
|
10,019
|
https://devpost.com/software/tackling-youth-unemployment-reimagining-the-egd
|
The present situation impacts minorities the most. We want a EU green deal that will take into account youth employment. We think that a good option could be to work on the present instruments as Youth Employment Initiative and Guarantee under the European Social Fund+,
What's next for Tackling Youth Unemployment: Reimagining the EGD
Built With
powerpoint
|
Tackling Youth Unemployment: Reimagining the EU Green Deal
|
#NotLeaveYouthBehind
|
['Giulia Persico', 'Violeta Balaguer García', 'Jean Haizmann', 'Sara Disconzi', 'Christianne Zakour']
|
[]
|
['powerpoint']
| 28
|
10,019
|
https://devpost.com/software/foodprint-4vr3dg
|
FOODPRINT helps you choose eco-friendly and affordable food in a quick and easy way
|
FOODPRINT
|
FOODPRINT helps you choose eco-friendly and affordable food in a quick and easy way
|
['Anne Uildriks']
|
[]
|
[]
| 29
|
10,020
|
https://devpost.com/software/instaplant
|
Logo
tech stack
python running on RPI
Inspiration
Coronavirus has threatened not only our immediate livelihood, it has also heavily impacted our food supply chain. Inspired by being stuck indoors, the recent advances in image-based phenotyping and the desire for a more healthy and sustainable lifestyle, we developed instaplant - the app for a growing need.
What it does
Instaplant connects to sensors that measure the critical inputs and observable outputs of your plant providing consumers with real-time data about their plants.
How we built it
We built the app with Flutter as the front-end with Google Cloud Platform serving as the backend. The RPi was programmed with python to pull data from the sensors, push images to the machine learning model and update the database.
Challenges I ran into (Gene)
The most challenging things is always the preparation. Give me 6 hrs to cut a tree and I will spend the first 4 sharpening the axe, said Abe Lincoln, and taking the time to learn that new program, prepare all files, running the hello worlds is what sharpening is all about. It's challenging when the timer is ticking and you seem like you have got nothing done!
Challenges I ran into (Harsimrat)
The hardest part of this project was to understand the Flutter layout given the timeframe of 24 hours. It does not have that steep of a learning curve, but it can take a while to understand the basics. We tried using the flutter BLoC library but we realised it was not necessary so I modified and created the user interface using Drawers, and Cards which are parts of the Material App library.
Accomplishments that I'm proud of
I think our team cooperation was what really made this Hack fun.
What I learned
I think we are always learning this - keep it simple, stupid.
What's next for instaplant
The next step is to validate this product in the market. Finish this MVP, win grants and investment, and prove that plant research at the distributed consumer level is the future of our health and stability.
Built With
firebase
firestore
flutter
python
raspberry-pi
Try it out
github.com
|
instaplant
|
planting 2.0
|
['Gene Yllanes', 'Harsimrat Singh Wadhawan']
|
['PixelogicDev FlutterThon Champions']
|
['firebase', 'firestore', 'flutter', 'python', 'raspberry-pi']
| 0
|
10,020
|
https://devpost.com/software/shopping-to-go
|
See all locations you've registered
Add lists to the locations to remind yourself of things!
Pick locations from a map!
Inspiration
I always run into this issue when out running errands. I need to go to three different stores to get everything I need, but only have one shopping list. What makes it even more difficult is when the first store doesn't have what you're looking for and trying to remember to look for it at the next store!
What it does
Shopping To Go allows you to register different locations from the google maps api and create different to-do-style lists ties to those locations. Based on your location data, you'll get reminded about the list you made while you're still at the store!
How I built it
It's built using flutter and dart. Some of the packages I used are google_maps_flutter, geolocator, flutter_local_notifications
Challenges I ran into
A big challenge for me was the time frame which this was built. It was build during a 24 hr "FlutterThon". Besides the time limit, integration with the google maps api and handling api keys were the biggest challenges.
Accomplishments that I'm proud of
I'm proud of the design. I exclusively work on the back-end side of things, so the design was a new challenge and I think it turned out pretty nice.
What I learned
I learned a lot about time management and api keys with this project. I also learned that I'm starting to feel comfortable with Flutter/Dart, and am needing to use documentation as a crutch less and less
What's next for Shopping To Go
Not exactly sure. I'll probably publish it once I understand the google maps api pricing model a bit more.
There's definitely a lot of jank and hacks in here. Still learning how to use Flutter and optimize for mobile. Regardless, I'm still really proud of where this app is and how it performs.
Built With
dart
flutter
location
Try it out
github.com
|
Remind Me At
|
Create Lists tied to locations and be reminded of them when you get there!
|
['Carl (TheCeeMachine)']
|
['Flutter Community Masters']
|
['dart', 'flutter', 'location']
| 1
|
10,020
|
https://devpost.com/software/geecorn
|
Spinners for wait time or load time
Image Upload Screen
Register Screen
Start Swiping!
Firebase Authentication
Skill limit
Add the skills you are looking to learn with someone
Find the profile that matches
Add your Skills!
Firebase Setup
Swiping Screen
Introduction
GeeCorn
Github Repo
Youtube Video
A tinder for Geeks to meet and make use of this Lockdown in a productive way.
Gee-Corn is a Flutter based mobile app which allows geeks all around the world to discover their partners with common interests and accordingly learn a new skill.
Project Components
Flutter
Firebase
The project is developed with the help of basic Flutter dependencies and firebase. Currently due to some issues in Firebase storage,
the local storage has been used.
App ScreenShots
Built With
dart
kotlin
objective-c
swift
Try it out
github.com
|
GeeCorn
|
A tinder for Geeks to meet and make use of this Lockdown in a productive way.
|
['Hemant Jain', 'Shivam Sahil']
|
['Have You Tried Flutter?']
|
['dart', 'kotlin', 'objective-c', 'swift']
| 2
|
10,020
|
https://devpost.com/software/study-app-cwxy9u
|
Inspiration
I have always struggled to find a Pomodoro study app that had all the features I wanted while still being free. Some were plagued with annoying ads, others required payment to access the full version. That's when I decided to build my own. I wanted it to be interactive, appealing to the eye, and easy to use which is why I decided to use flutter.
What it does
Users are able to customize how long they want their study intervals to last and how long they want their breaks to last. Smart Focus will then alternate between the two and notify when the user is supposed to be studying and when they can take a break. This helps instill a sense of urgency and in turn, helps the user focus on accomplishing their tasks instead of wasting time with distractions.
What I learned
I learned how to alternate and send information between pages in flutter. I also learned the importance of separate folders and classes which help keep code organized and reduce the amount of time spent debugging.
Built With
dart
flutter
Try it out
github.com
|
Smart Focus: Pomodoro Timer
|
Do more in less time with Smart Focus, a time management application to increase productivity and help you study using the Pomodoro technique.
|
['Mark Liu']
|
['Best App Overall']
|
['dart', 'flutter']
| 3
|
10,023
|
https://devpost.com/software/bunny-papr
|
Dr Kuo, in surgery with bunnyPAPR
BunnyPAPR in use (1 of 4)
BunnyPAPR in use (2 of 4)
BunnyPAPR, cost comparison
BunnyPAPR, $30, Hospital-grade protection
Dr Kuo, explaining bunnyPAPR, still shot
Safety Testing and Medical Technology Paper (March 2020) BunnyPAPR
Affordable and Scale Fast - Parts List - BunnyPAPR
Air flow diagram with filters, BunnyPAPR
BunnyPAPR in use (3 of 4)
BunnyPAPR in use (4 of 4)
End Screen with tagline, BunnyPAPR
BunnyPAPR
Video Overview
Watch the
5 minute video
for an overview.
Inspiration
In March 2020, a short three months ago, Dr Kuo released his design for the Bunny Science PAPR. It was meant to address the N95 shortage in hospitals, but hospital adoption was slow. We asked: For emergency situations, could a $2000 PAPR system be simplified to 1/10th the cost? The answer is yes, and the $30 bunnyPAPR was born.
What it does
The diagram below shows how it works. And one of the key things is that it can be made for $30 or less.
Contaminated air passes through the FDA-approved anesthesiology viral filter with sub-micron level capture.
Fan pulls in outside air. Circulates clean air. Provides positive air pressure.
Person exhales CO2 through a recommended surgical mask. [1]
CO2 is continuously vented out rear flutters valves or additional viral filters.
Fan is powered by USB battery pack in the user's pocket.
Depending on the use case, many of these parts can be decontaminated and re-used. Hence, the one-time cost of $30 is for a
reusable system
. [2]
Medical Testing
The Bunny Science PAPR has been tested by the inventor, Dr Kuo, an anesthesiologist from Seattle. In particular, it
passes the nebulized saccharin test (sometimes referred to as the QLFT for respirators)
passes monitoring of O2 and inspired/expired CO2
passes "stress tests" of deep and fast inhales and exhales.
It has been worn for 11hours straight. Dr Kuo has also worn it everyday he is in the hospital for the last 2 months.
Details of the Bunny Science PAPR are in the 14-page paper titled:
Pilot Evaluation of Oxygen and CO2 Safety of BunnyPAPR, A Prototypical Sealed, Compliant Volume Powered Air Purifying Respirator (SCV-PAPR)
How we built it
About 1000 engineering hours are in on the project.
Four out of five parts are commodity parts. We tested several dozen fans, filters, bags, and batteries to find the right balance.
Commodity Parts List (millions available)
Fan ($2 wholesale to $4-5 retail)
FDA-Approved Viral Filters ($1 retail in packs of 100qty)
Bags ($0.30 each, disposable)
USB Battery Pack ($5-$10)
The final parts are a head mounting system and airflow-connectors. These were prototyped using 3D-printing. See
https://www.bunnypapr.org/makers/3d-print-files
Challenges we ran into
The primary challenge was figuring out our target market. Initially, the focus was on the medical community and at-risk people. After all, they need protection as good or better than N95. However, given the Covid19 controversies around mask wearing mandates, hospital approvals, and cloth masks, we had to admit our error:
Not everyone who medically needs one will get one.
The hackathon overcame this challenge by rethinking the problem. "Who is most motivated (financially, medically, or behaviorally) to want bunnyPAPR?" We refocused on international aid (free distribution) and large industries (premium) where safety is part of the
license to operate
. Hence, we figured out the need for a "Freemium" business model described in the 5-minute video.
Other challenges included various R&D and scaling/sourcing challenges.
FANS
We bought over a dozen fans, many did not match the listed specs.
BAGS
We have spent approximately 125-200 hours on bags. Some bags are strong, but visibility is poor. Other bags have great visibility, but might tear easily.
INDIA
Our India partner (
link
has had a hard time sourcing the right bags and fans.)
Overall, we are approaching these issues methodically and finding adequate solutions. Importantly, we've been able to maintain our $30 target.
Accomplishments that we're proud of
All volunteer team. We run through a Discord/Slack chat server.
Doing all this fast! (2 months, with a flurry in the last 3 weeks).
Teamwork.
Integrating a diverse group of talented volunteers. From 16yo to 70+. Finding good, helpful people internationally.
Distributing 20+ BunnyPAPR's to people who have wanted to use them.
We have a list of over 30 requests that we are in the process of fulfilling.
What's next for BunnyPAPR
Finish "500plan" bunnyPAPR inventory. Make and distribute 500 internationally.
More user feedback, refine target market.
Seek "angel" investors for scaling to 2000-5000 and then 100,000
Explore target markets, like
NBA and other large markets,
direct to consumer, and
WHO, and international humanitarian aid (thanks to help of UCSF Hackathon Mentors)
Consider US medical markets and FDA Emergency Use Authorization (EUA) approval. Medical regulations are very complex.
Provisional patent submission
Contact Us
We can be contacted at
[email protected]
and
[email protected]
.
NOTES
[1] NOTE on surgical masks. In the "consumer" version, bunnyPAPR™ has viral filters on the intake
and
exhaust. Hence, surgical masks are not necessary. In one lower-cost "medical" version, bunnyPAPR™, has a viral filter on the intake only and a valve on the exhaust to prevent reverse flow. A surgical mask will capture any viral shedding from the wearer instead of an exhaust filter. This option is because many hospitals administrators will still require a surgical mask or N95. The reason is for legal/FDA-compliance reasons.
The decision of which version to use will ultimately be up to the hospital administrator and infectious disease control department.
[2] The decision on re-use and decontamination is a tricky one. Under normal circumstances of abundant filters, the viral filter would be replaced after each use, just like N95s. Under the Covid19 crisis, many hospitals are rationing and re-using N95 masks, sometimes using one-per-day or even one-per-week. If this is worn in a consumer setting (grocery shopping, not a hotspot), a set of 3 filters would probably last months to a year, depending on how often one goes out. If this is worn in a hospital setting in a Covid19 ward, it will depend on the availability of viral filters. One filter per day or several days is reasonable and similar to the N95 rationing protocols. A lot will depend on the infectious disease control departments in hospitals and national/international health authority guidance. Note: The bunnyPAPR system is not yet FDA-approved, but the viral filters are FDA-approved and routinely used during surgery.
Built With
anesthesiology
fan
papr
viralfilters
Try it out
bunnyPAPR.org
|
BunnyPAPR
|
Affordable Protection for COVID19 and Beyond
|
["Jaclyn N'Shaiha", 'Sara Koyama Hwong', 'HowardG Chong', 'Michael Noes']
|
['Syntegra sponsored Grand Prize']
|
['anesthesiology', 'fan', 'papr', 'viralfilters']
| 0
|
10,023
|
https://devpost.com/software/calm-on
|
Title Screen, Sign Up, Character Select
Flynn Screens
Aurora Screens
Sprite Screens
Achievement Screens
Inspiration
During COVID-19, we noticed an increase in online gaming with youth & young adults as well as an increase of negative mental health effects. Angela’s inspiration for the app stemmed from looking for a way to promote children’s mental health awareness through gaming. As a team, we wanted to communicate mental health strategies and mindful thinking in a way that was fun and easy to digest for the young audience.
What it does
The app aims to create a fun and interactive experience that can help youths (6-11) learn more about their feelings and mental health through different ACT/CBT techniques. It also includes an integrative AI chatbot function that allows users to freely express their thoughts. We feel that the production of this app would prove valuable for the youth during these times as it addresses the mental distress that children, who may not necessarily have an outlet, are feeling. We’re striving to do this through interactively educating the children on healthy coping techniques as well as maintaining a daily schedule to attain a sense of stability in these challenging times.
Challenges
Team communication and scheduling conflicts -
Team members were located in different time zones and had additional priorities further making it difficult to schedule meetings. Responsibilities and time commitment on this project were not communicated thoroughly, thus recruited team members were dropped in the middle of the project. Working in an interdisciplinary team proved challenging due to a lack of awareness of the design process. The team size may have also caused some of these problems as with a larger team communication becomes even more difficult.
Conducting research on children
It was difficult to recruit participants to conduct research on, especially with the quarantine restrictions. Ideally, we would’ve aimed to conduct in-person interviews with children in our research phase, however, due to restrictions, the bulk of our user data came from online surveys. We supplemented our findings with research from academic journals and other online resources.
Accomplishments that we're proud of
Team -
We collaborated together as four different teams: research, animation, design, and development. We conducted surveys with 60+ survey results, created 3 animated personalities, animated a backstory video, wrote and completed narrative scripts, began a user testing phase, and rapidly developed a react native web application from no background knowledge.
What we learned
Team -
Since we came from various backgrounds, we learned that it was very important to educate the team about the process and thoroughly explain the next steps so everybody was on the same page. It was important for us to assign different people tasks and deadlines which we found was very effective in keeping our team organized
Dev Team -
The project really pushed us to our limits as what we can and can not do within such a short period of time. The project was first introduced to the current technology team two weeks before the deadline. We were able to decide with precision what is plausible and possible for the application and what are some far-reaching requests. The final design was given to the dev team 5 days before the deadline. And as the team did not have any frontend experience, any code was an accomplishment for us.
What's next for "Calm On!"
Going forward, more user testing will be conducted on our designs and prototype in order to iterate and improve upon what we have so far. We would like to do a deeper exploration into the gamification of the app in order to encourage players to come back and check in often with the incentive of in-game rewards and customization. With more support, we will continue adding more character animations, mindfulness techniques, storylines, and other content in order to keep the app informative and exciting as time goes on. We will also look into the ways artificial intelligence could enhance the personalities and dialogue options of our characters. Though this app was designed with the intention that the children could use it by themselves or with a parent, we hope to potentially partner with therapists in the future in order to provide more personalized therapy experiences for their younger clients.
Built With
aftereffects
figma
react
Try it out
docs.google.com
|
"Calm On!"
|
A fun, interactive game for kids (ages 6-11) to learn about emotional intelligence and mental health. The ultimate solution towards children's education on mental health.
|
['Peter Hsiao', 'Andrew “AJ” Lopez', 'Chao Deng', 'Kasie Lee', 'Thomas Egan', 'Tony Vu', 'Riya Vaidya', 'Kaveri Bhargava', 'Farrah Wong', 'Jacqueline Louie', 'Srujana Munamala', 'Mandy Zi', 'Sarah Woldemariam', 'William Wu', 'Yazleen Reyes', 'Nathan Chung']
|
['UCSF LaunchPad sponsored Second Place Prize']
|
['aftereffects', 'figma', 'react']
| 1
|
10,023
|
https://devpost.com/software/textcovid
|
Underserved communities have been hit particularly hard by COVID-19. They are stricken by the dual-edged sword of higher case numbers and fewer resources, often due to poverty, America’s wealth inequality, and the deep roots of institutional racism. These are difficult problems to solve, but one problem we can address is access to the resources that do exist.
Our idea was inspired by the inequality of resource access in these times of crisis. Many vulnerable populations, from the elderly, to the homeless, to the technologically illiterate, to the low-income, have trouble getting access to the resources they need.
Often these resources are available, but if you’re an elderly person or one of the 15% of cell phone users who don’t have a smartphone, the new-fangled apps might be confusing to use. Online resources are great, but if you’re one of the 16 million people without internet access or even one of the 162.8 million people without high-speed internet, those resources may be hard to get to.
Our solution is beautiful in its simplicity. textCOVID, is simple to use and does exactly what it says it does, without any bells or whistles.
When users text textCOVID, they are directed to a menu that allows them to reply with a number, choosing between the following: up-to-date COVID-19 information (including number of COVID19 cases and spikes), locations of food pantries, testing locations, housing options, and unemployment resources. By optionally entering a zip code, resources will be specific to their location.
We use Twilio’s texting API and ngrok in order to set up an automated SMS server that receives texts and sends generated replies. Our demo version, as a minimum viable product, reads from a hard-coded csv file that contains various information for different zip codes. The general availability version of this product may see web-scraping of sites and resources online coupled with some natural language processing to automatically populate the csv database.
Built With
csv
python
twilio
|
textCOVID
|
textCOVID brings together COVID-19 resources into one privacy-forward, accessible, unified, and easy-to-use automated texting service.
|
['Kaylee Wedderburn-Pugh']
|
['UCSF LaunchPad sponsored Third Place Prize']
|
['csv', 'python', 'twilio']
| 2
|
10,023
|
https://devpost.com/software/binformed
|
Inspiration
binformed is a clinically-driven comprehensive desktop + mobile infectious disease, epidemic + pandemic management tool targeting suppression and containment of diseases such as COVID-19, built on state-of-the-art technologies. covidata is a complementary technology for advanced comparative analytics. These solutions stem from our clinical, informatics, technical and analytic frustration that we did not have the tools to combat this pandemic from the start and that we must have the tools to control this pandemic now, and any pandemics in the future.
What it does
binformed is an infectious disease (COVID-19) managment tool, including symptom checking, health and exposure risk assessment, contact tracing, test + disease management and advanced analytics. The system is designed with three core goals: a) alleviating public anxiety through the provision of open access to risk assessemtn; b) triage, close follow-up and tracking of symptomatic patients to appropriate isolation, testing or admission, based on severity; and c) the critical tools neccessary open schools, protect the public + workforces + open the economy safely. The system is state-of-the-art, massively scalable, cloud-based and has a clean and easy to use desktop and mobile UI/UX. The system has security, confidentiality and privacy built in and is HIPAA, CCPA, and GDPR compliant.
The focus of BInformed tool is the work we need to do to clinically assess, manage and contain COVID and protect health care providers, the public, students + workforces to the greatest extent possible:
• Symptom, Exposure and Health Risk Assessment – who is at risk, has been exposed or has symptoms we and they should be concerned about and how are their symptoms progressing today, tomorrow, and for the next 72h to 14 days? Data entry can be by an individual, family, health provider or direct monitoring of SaO2, HR, BP, Temp, RR. Reminders are text with triggers and escalation + triage.
• Triage Tool for testing, configurable for rapidly changing criteria - who should be tested, where should they go, when should they go?
• Touchless Testing, with absolutely minimal to no contact with health workers, at all locations, powered by text messaging, links and voice call as required. This is for safety, preservation of PPE and speed.
• Results Tracking of all tests, all results, all patients, integrated with symptom tracker – do negative cases become positive? Are positive cases symptomatic, and how are they progressing?
• Contact Tracking – who? How to contact them? Collect and track their symptoms and manage isolation.
• Symptom Triage, including to ED, with direct-admit to home (home isolation and telehealth homecare + monitoring), to a hospital bed (do not go to ED) or direct to the unit (or ED) if critical and requiring ventilation (BiPAP or intubation).
Additional features:
• Consent based isolation monitoring with geo-location tracking (with cell carriers).
• Interface with EMRs and EHRs.
• Support for additional testing, such as genetic testing.
• Digital contact (Bump) tracing with Dosimeter.
• Advanced analytics.
BInformed is a pandemic tool for real-time management, triage, tracking, testing and providing the greatest achievable level of exposure minimization to health care providers + the highest level of disease suppression + containment.
How we built it
BInformed is built on a highly scalable and secure HIPAA, CCPA, and GDPR compliant cloud-based architecture we have used to develop multiple health care and non-health care projects. It uses a NoSQL tagged JSON data architecture (MongoDB or DocumentDB), runs on Amazon AWS (can run on Google Cloud or Azure), and has a very clean Material UI/UX. All middleware is Java, desktop (browser) UI is Javascript and CSS, mobile is Swift (iOS) and Dart (Android).
Challenges we ran into
There is not enough time in the day or in our disparate schedules to do all we wish we could.
Accomplishments that we're proud of
This is a clinically focused tool targetting rapid workup, testing, and appropriate, safe triage of patients. It is designed with simple, easy to implement core health provider safety features and workflows.
What we learned
Everyone is chasing similar goals and coordination of goals is critical.
What's next for bInformed
We are working with potential partners on deployment. It is being offered as a public benefit (B-Corp),
Built With
amazon-web-services
api
dart
google-cloud
java
javascript
json
mongodb
restful
swift
Try it out
binformed.info
|
binformed | covidata
|
binformed is a clinically-driven comprehensive desktop + mobile infectious disease, epidemic + pandemic management tool targeting suppression and containment of diseases such as COVID-19.
|
['Siranush Manukyan', 'Susan Ip-Jewell', 'Ari Akerstein', 'Zille Huma', 'Leyla Mirmomen']
|
[]
|
['amazon-web-services', 'api', 'dart', 'google-cloud', 'java', 'javascript', 'json', 'mongodb', 'restful', 'swift']
| 3
|
10,023
|
https://devpost.com/software/carebuddy-0z9lpi
|
Inspiration
Unfortunately, the coronavirus pandemic has necessitated stringent visitation restrictions, thereby increasing the isolation of hospitalized patients. Video conferencing has provided partial compensation - but this solution does not leverage the full capabilities of modern technology.
What it does
The goal of this project is to develop a digital assistance platform that provides additional functionality to video conferencing. The objective is to empower patients to stay more connected with their family and friends, as well as improve communication with their health care team. Features of this digital assistant include:
Communication interface for friends / families to leave messages and videos that can be played at any time
Communication interface for patient / healthcare proxies to leave messages and/or questions for the healthcare providers
Updates related to discharge planning
Schedule of anticipated procedures, video chat calls, etc.
What's next for Carebuddy
Ongoing prototype development
Flowsheet
https://docs.google.com/presentation/d/19NNFWItFQ2HJzK-cf8GfIT56blOzSFuW8xov0HJkZb0/edit?usp=sharing
Business canvas
https://canvanizer.com/canvas/rN7HWOlxgpSbX
Team members
Design:
Jessica Arozqueta
Martin Kyalwazi
Mallory Shingle
Technical:
Stephanie Jue
Lakshmi Radhakrishnan
Clinical:
Seth Blumberg
Alissa Brandt
Nataliya Budanova
Deborah Ha
Molly Murphy
|
Carebuddy
|
Digital care to empower patients and help them connect with those they care about
|
['Seth Blumberg']
|
[]
|
[]
| 4
|
10,023
|
https://devpost.com/software/staystrong-by-deedalabs
|
Our awesome logo!
Doctors are always connected to their patient with a contact card. Phone, Chat and custom welcome message are easily include!
Doctors and patients can easily chat, send links and multimedia
In case of an emergency, the app always ensures patients can quickly call their local emergency number in just one tap
Doctors assign smart assessments that have conditional logic and clinical scoring
Based on a patient's answer our Smart Assessments modify themselves with follow up questions
Patient Vitals are their own data type and simple to interact with
We make data entry a breeze for patients - in any locale
We leverage smartphone sensors to capture additional data only if it will help with a diagnosis
Patients can be triaged by their clinical score based on their assigned Smart Assessments
Your patient's assessment number tells you a lot. Easily reach out to chat with them if you need a follow up
Our doctor dashboard makes virtually monitoring and triaging large numbers of patients possible
Understand a patient's likely outcome based on their pre-conditions
See trendlines for a patient's vitals as they submit them over time. See how they're progressing at a glance
Inspiration
At the height of this pandemic we came across a cable news interview with Dr. Giovanni Guaraldi of the University of Modena and Reggio Emilia. Things looked bleak in northern Italy. We wanted to help. We reached out to Dr. Guaraldi to better understand what doctors were facing and how we might be able to provide a solution. Doctors needed a way to monitor Covid+ patients at scale. There was simply no existing solution for this.
At the same time my own family was in need of my help. My parents were in Chicago, so I set off to help them while hatching a plan to build an application to help Covid patients around the world. I enlisted my close friend and startup confidant, Chris Anderson, and together we set off on the path to build a much needed, and brand new type of telemedicine application.
In honor of the Italians we named our product
"CelaFaremo"
which loosely translates to "Well get through this". We wanted our app to be memorable by the people using it, and we wanted its name to resonate with the spirit of the how we were all doing our part to face this crisis. We decided to keep this naming convention for the United States, and today our app's english name is simply:
stayStrong
.
What it does
stayStrong
connects Doctors to their patients in a very unique way. Doctors can assign fully customizable Smart Assessments to their patients. These assessments can help monitor any disease through unique scoring based on established clinical tests. Smart Assessments can also be set to be delivered at any frequency - once, daily, weekly, monthly - a doctor can easily assign single or multiple assessments to their patient in order to monitor various aspects of their COVID condition.
One-on-one video conferencing is not scalable in a pandemic, but doctor-patient communication is still vital. That's why we build in real-time chat into our app. Patients can still communicate important updates and changes to their conditions including uploading images or videos of something they are experiencing. The doctor dashboard makes messaging hundreds of patients fast and simple. In one hour a doctor can easily communicate with hundreds of patients instead of just one. Based on the Smart Assessment scoring a doctor can easily focus on the most critical patients first.
We also designed our products from the ground up for teams. That means that our product is perfect for the largest healthcare providers and insurers to even a single physician's practice.
Smart Assessments are designed to be translated into any language. That means a single mobile app can distribute a COVID assessment globally to anyone with access to an iPhone or Android smartphone. Doctors are free to publish their assessments and allow anyone in the world to use them for monitoring as well as collecting patient data.
Finally, data is valuable to researchers who are trying to understand as well as solve the mysteries of the COVID-19 virus. That's why we allow anonymized data to be collected and pooled for ethical analysis. Patients who opt into have their data anonymized are ensuring that we have a global dataset on how this disease is progressing around the world and how we might come together to stop it.
How we built it
We built this app using Kotlin, Swift, and Dart for the mobile app, and we used Vue, Javascript, and Google Cloud Services for the doctor's dashboard and system backend. Chris and I hired engineers from around the world to help us in any way possible. We would hire engineers for a short stint to help us with something we were stuck on. We were often stuck on things that were completely new to us such as the basics of building scalable and HIPAA compliant backend systems.
Challenges we ran into
Our initial assessments were hard coded into our mobile app. They were great, but whenever a doctor would need to update the scoring or order of questions it was quite difficult to update and release the new application. As patients used the app it was scary to imagine that all this work would have to go into updates that could potentially affect people who needed this life saving application. Our solution to this was to engineer a solution whereby the Smart Assessments were never hard coded into the application. Instead, we stored them on the server and taught our mobile app how to render every possible question type. Now, changes could be made by doctors at any time. Patients would instantly get an updated set of questions and the data sets would intelligently reflect the proper data that was pooled through all version of an assessment.
Accomplishments that we're proud of
We're most proud of our perseverance as well as being able to work with so many talented people around the world. We wouldn't have the product we have today without this team effort. We're also proud of pressing forward with some of the most challenging engineering tasks we've ever had to do with such few resources. We're also proud to have heard a call from a doctor in the field who needed help and been able to respond to that call on such short notice to provide what we hope will be a valuable humanitarian solution.
What we learned
We learned all the daily moving parts in building not only a technical solution for a humanitarian cause, but also all the things needed to build a proper startup in the healthcare industry. There were numerous privacy laws to familiarize ourselves with in America as well as in Italy. Navigating through HIPAA and GDPR was an adventure in learning in itself, and one that we are still learning more about everyday. We learned how fragile we are as well as how resilient we can be. We learned that hard work and building products to help others is something that provides us with a uniquely satisfied feeling in the world of startups.
What's next for stayStrong by deedaLabs
Thanks to the incredible support we've received during this hackathon, we're planning on publicly launching our
stayStrong
product in the days ahead. We will initially be working with healthcare providers in the United States as well as Italy. We've also decided to provide our
Smart Assessment
technology to all healthcare providers fighting COVID-19 for free. We've also developed a paid tier for our product that allows healthcare providers to easily monitor multiple diseases through our
Smart Assessments
.
Please feel free to keep up with our progress at:
stayStrong.app
deeda.com
Built With
dart
google
google-app-engine
google-cloud
javascript
kotlin
swift
Try it out
staystrong.app
|
stayStrong by deedaLabs
|
We connect doctors to their patients through unique iOS and Android smart-assessments that leverage smartphone sensors for large scale monitoring of COVID positive patients.
|
['Atif Khan', 'Chris Anderson']
|
[]
|
['dart', 'google', 'google-app-engine', 'google-cloud', 'javascript', 'kotlin', 'swift']
| 5
|
10,023
|
https://devpost.com/software/docme
|
GIF
DocMe App Screen Shot
Inspiration
Our inspiration comes from our motivation to broaden access to healthcare. Our idea stemmed from the questions: “What if you could monitor your health without the long waiting times, expensive wearables, and uncertainty we see in everyday clinics? What if you could monitor your health with something much simpler — perhaps even a selfie?” This sparked a valuable discussion that led to the creation of our product: DocMe.
By providing remote diagnostics and guiding clinical decision making through a smartphone lens, we would not only allow you to be more informed about your everyday health but also be able to conveniently connect you with your doctor.
What it does
DocMe is a preventative health technology putting healthcare in the hands of individuals. By utilising edge-AI and computer vision, DocMe is able to:
easily capture the user’s vital signs directly from a smartphone camera and immediately receive insights powered by accredited research
connect
users with
their
chosen medical professionals through a "privacy-first" telemedicine platform
(Coming Soon!)
.
DocMe gets to work by first scanning the user’s face through their smartphone camera. The app then extracts minuscule details and analyses them. Today, the application can analyse 68 different regions on a user's face using AI, and, by magnifying subtle movements of the user’s skin, DocMe can even extract early warning symptoms of disease.
Importantly, our solution can be achieved without requiring additional wearable components. Thus, our technology enables remote, frictionless vital sign monitoring and the results can be accessed in real time, thereby enhancing the speed of clinical responses. Moreover, clinically, it is valuable to add physiological information to the patient’s history and symptoms in order to guide personalised care. Our edge-AI model processes and stores user data on their device. Hence, DocMe doesn't require a steady mobile connectivity to use its self-assessment tool. [
At the moment, our demonstrator that uses a web server model due to building functionality from scratch in a narrow implementation time frame
] Additionally, users can choose to contribute to anonymised data based on the user profile to feed the research component of DocMe.
Finally, due to our edge AI processing and our software architecture, DocMe will be
GDPR
and
HIPPA
compliant.
How we built it
We used the React Native framework to build a robust mobile app in JavaScript that runs on both iOS and Android devices. To create a secure offline database on the user’s device, we integrated the realm.io platform into the app. We implemented a REST API to interface between the user’s device and our server backend. OpenCV will be used to analyse and calculate heart rate, respiratory rate, and more from just a selfie video. In addition, we took advantage of a multitude of React Native component libraries to construct the live video feed, video upload mechanism, login/signup form validation, app light/dark theme, and more.
Challenges
Given that our team spanned six time-zones, initial communication between all team-members seemed very difficult. Throughout any time of the day, it was a challenge to contact team-members at times appropriate to their time-zone. However, we soon realised how to leverage this into an advantage. We proceeded to organise teams and gain more information about available hours for our team members in order to make the most of everybody’s daytime. This way, we were able to get a multitude of assignments and objectives completed in an extremely efficient manner.
Accomplishments that we're proud of
We are very proud of the amount of work we were able to accomplish in a relatively short period of time. DocMe was able to develop a working prototype of the App, formalise a USP for the service, continue biomedical research efforts in the format of White Papers, and foster strategic, mutually-beneficial partnerships Dell Medical School and Diya Health all in a span of 3 weeks. We are also proud of producing a beta version of our application in the time of this hackathon, as DocMe is ready for beta testing on iOS and Android mobile devices (iPhone X and above). Finally, we are especially proud of our diverse, interdisciplinary team ranging from Malaysia to Cambridge to San Francisco for leveraging their unique skills to exceed our initial expectations and we could not be happier.
Next Step
Our next steps would be to solidify DocMe as a viable business, conduct trials for efficacy, improve our AI diagnostics, and add features to create a more user-friendly experience.
Built With
azure
opencv
python
react-native
realm.io
Try it out
DocMe.ai
rb.gy
|
DocMe
|
Using Computer Vision technology, DocMe makes healthcare preventative by monitoring key vital signs just from a selfie.
|
['Amartya Dave', 'Sarah Broderick', 'Prabhvir Marway', 'Doctor Amy Kerstein', 'William Chang', 'Pratyay Poddar', 'Veronika Koshkina', 'Vicky Feliren', 'Jye Quan Teh', 'Miro Svetlik', 'MikkaAlon', 'https://perfexia.health/docXme', 'Rishabh Gupta', 'Usama Safeer']
|
[]
|
['azure', 'opencv', 'python', 'react-native', 'realm.io']
| 6
|
10,023
|
https://devpost.com/software/primaltrack-two-way-app-platform-for-elderly-and-caregiver
|
Inspiration
Current healthcare systems are not prepared for a pandemic. So the questions are: How can we prepare for the next pandemic? How can we improve the current healthcare system to adapt?
Due to the highly infectious COVID-19 virus, many caregivers are unable to provide care or reach people who need care. The elderly and other care recipients are suffering from chronic diseases that require routine checkups. However this may not be as possible due to COVID-19 crisis and lockdown orders. Furthermore, unmanaged chronic diseases also increase the susceptibility of care recipients from suffering life-threatening complications from COVID-19 infection.
We envision that the future healthcare system should be both preventive and personalized. Our idea is to setup a platform to connect caregivers to elderly and care recipients that required care for chronic conditions, and monitor the health of elderly on a daily basis.
Our solution
Our solution is a two-way app platform for caregivers and seniors who required regular health check up. The system leverages AI technology to analyze data collected from facial recognition, speech recognition, wearable devices and/or IoT on a daily basis, and alert the caregivers if there is any identified risks. The platform also provides a way to facilitate communication between caregivers and care recipients, while aiding with health management to help alleviate caregiver stress.
Main features
Health data collection
Facial recognition
Speech recognition
Chatbot
Phone sensors
Wearable devices/sensors
Elderly focus
Voice control
AI Chatbot to stimulate human interactions
Enlarged text and other accessibility features
Reminder system
visual and sound alerts can be snoozed until the elderly login and complete the health monitoring daily
Data visualization for caregivers
Data analytics dashboard
Detailed health reports of elderly
Alert system for identified issues
Communication and reminders
How We built it
Software
This was an exciting project with many brains and skillset at work.
We preferred open source tools and platforms in different parts of the project.
Frontend Dev using Angular, FireBase Authentication.
Node Libraries Likes charts.js PWAs, BootStrap, Material Design, etc.
Hosting and CICD setups using Netlify and Heroku and GitHub.
Domain and SSL certificate from Namecheap and Let's Encrypt.
SQL DB connected to the app with Restful API.
Invision and Figma - UX/UI ProtoTypes and WireFrames
Slack for Internal Communications & Google Drive for Documents, Images, etc.
Google Colab notebooks to execute heavy GPU workloads and ML Algorithms.
Slidego, Powtoon and Toonly for Video and Pitch Decks.
Machine learning
We collected datasets from varies sources such as Kaggle, JAFFE and IMFDB and trained the machine learning model for a couple of tasks: the identification of emotions from facial expressions, identification of BMI from face images, identification of emotions from speech, and detection of falls from phone sensors. Determination of cardiovascular disease risk is also achieved by reviewing cohort studies and results in medical journals. After training the model, we deployed a demo of the emotion prediction model, BMI prediction model, and cardiovascular disease risk using Heroku service.
Challenges we ran into
It is difficult to find quality labelled data for training machine learning models, which in turn affects the accuracy rate. Given that this is a remote hackathon, we were also unable to test connection with wearables. While there is flexibility to use the app without external sensors, we plan to integrate with multiple wearable devices and platforms in the future.
What's next for miia
We are planning to bring the project to the next stage. Shoot us a message if you're interested!
Built With
angular.js
cicd
firebase
github
invision
ml
namecheap
netlify
pwa
python
restful
Try it out
miia.me
www.figma.com
github.com
github.com
emotionpredict.herokuapp.com
drive.google.com
drive.google.com
drive.google.com
|
miia
|
Digital health solution for the elderly and caregivers
|
['Vanessa Guillén', 'Deepesh Grover', 'Ava Chan', 'Rohail Khan', 'Chloe Chen', 'Parteek Chhabra', 'Rohan Pal', 'Alice Tang', 'Billy Zeng', 'Mudit Mittal', 'Amit Dandawate', 'Tanjim Azad', 'Andrew Chan', 'Megan Thong', 'Mohammad Yusuf Mulla', 'Ajifama Jobe', 'DANCIL-isecure Cecil', 'Janani Ram']
|
[]
|
['angular.js', 'cicd', 'firebase', 'github', 'invision', 'ml', 'namecheap', 'netlify', 'pwa', 'python', 'restful']
| 7
|
10,023
|
https://devpost.com/software/orpheus-knowledge-synthesis-for-covid-19-3uybpa
|
Orpheus screenshot
Orpheus Data Pipeline
Inspiration
Being able to synthesize and rapidly assimilate the exponentially growing biomedical knowledge is becoming an impossible task for scientists. These are either inherently unstructured and non-conducive to current computing paradigms or siloed into structured databases requiring specialized bioinformatics.
Despite the recent renaissance in unsupervised neural networks for deciphering unstructured natural languages and the availability of numerous bioinformatics resources, a holistic application for real-time synthesis of the scientific literature and seamless fusion with deep omic insights and real-world evidence has not been advanced. *This is causing severe slowdowns in identifying new targets in drug discovery research and also causing further slow-downs downstream.
This has become evident in pandemics like COVID-19 and we need a mechanism to be able to quickly synthesize, analyze and make inferences from the ever-growing body of biomedical literature and curated datasets available globally.
What it does
The Orpheus application parses, analyzes and extracts biomedical concepts from COVID-19 related scientific
literature.
Once it extracts out these concepts like Diseases, Proteins and Compounds it also infers implied relationships between these concepts based on the NLP based analysis of the scientific paper itself.
Using the inferred relationships of these extracted concepts it then builds a series of 'triples' from each paper.
These are then combined with other referential data sources to then infer confidence levels for each 'triple' or association.
Finally all of these inferred triples are then added into a graph database to build a probabilistic knowledge graph.
This graph is then exposed as an API that can be queried, searched and explored through an intuitive visual web-based graphical interface.
As new research on COVID-19 is published the underlying Orpheus pipeline can be re-run as a workflow to keep the graph updated.
How we built it
Orpheus supports visual triangulation of insights via statistical enrichments from curated collections of structured databases, with the diseases, biomolecules, drugs, and cells & tissues collections loaded by default.
The vectorization (converting texts to a numeric vector) that we will be using. We will be using not only word based vectorization schemes but also used network representations to infer and predict relationships between nodes and allow searching for concepts.
We then fused disparate data sources and extracted triples (assertions) into a probabilistic knowledge graph.
Challenges we ran into
Being able to correlated and normalize the CORD-19 dataset along with reference databases like Uniprot and Wikipedia amongst others
Being able to scale to be able to handle thousands of scientific papers on COVID-19
Being able to identify the right NLP models to be able extract the concepts needed
Being able to define the algorithm to infer relationships between concepts
What's next for Orpheus - Knowledge Synthesis for COVID-19
Identify and include additional datasources like CHEMBL and PDB DBs
Perform additional graph based data mining algorithms to reveal hidden links
The algorithms we will be running on the fused knowledge graph can enable us to infer/predict additional links in the graph and also to surface other applications inside it.For e.g. algorithms like pagerank but also other custom algorithms that can surface drug repurposing use-cases.
Add additional UI features to make it easier to search/explore and navigate the graph.
Built With
cytoscape
java
javascript
python
Try it out
orpheus.wisecube.ai
|
Orpheus - COVID-19 Research Knowledge Graph Application
|
Orpheus is a knowledge synthesis application that analyzes, extracts and visualizes COVID-19 literature and surfaces relations and insights as a knowledge graph.
|
['Vishnu Vettrivel', 'Alex Thomas', 'Nolan Gendron', 'Ruchika Bajaj', 'Teena Bajaj']
|
[]
|
['cytoscape', 'java', 'javascript', 'python']
| 8
|
10,023
|
https://devpost.com/software/infection-control-breathing-tube-holder-and-biteguard
|
Front View
Left View
Top View
Right View
Back View
Inspiration
Patients are getting sick because of bacterial pneumonias and oral care is hard because dirty breathing tube holders can not be replaced easily.
What it does
C-19 patients who get a bacteria pneumonia have a mortality rate of 96%
How we built it
We received extensive clinical input from Respiratory Therapists, pulmonologists, anesthesiologists, critical care doctors, nurses, and medical device manufacturers on how a better breathing tube holder should be made.
Challenges we ran into
It is difficult to design something that needs a large amount of input through a virtual platform.
Accomplishments that we're proud of
We created a better ETT holder that can be 3D printed and is highly scaleable.
What we learned
Iterations are key to reaching a concensus.
What's next for Infection Control Breathing tube holder and biteguard
We need to 3D print the device to reach a design freeze. As this is a class I device, it can be registered and manufactured quickly without FDA clearance.
Built With
autodesk-fusion-360
hardware
Try it out
autode.sk
|
Infection Control Breathing tube holder and bite-guard
|
1 in 7 C-19 Patients die of a bacterial pneumonia, we want to create a medical device to reduce the amount of bacteria that the patient is exposed too.
|
['Benjamin Wang', 'David Zalazar']
|
[]
|
['autodesk-fusion-360', 'hardware']
| 9
|
10,023
|
https://devpost.com/software/clipvent-f1yc26
|
Inspiration
We were inspired by an existing FDA approved automatic resuscitator device that was very simple but had mediocre manufacturability and lacked independent PEEP adjustment.
What it does
ClipVent is a pressure cycled ventilation device that operates in pressure control or pressure support mode. ClipVent also incorporates independent PEEP pressure adjustment and features extremely simple injection mold tooling design.
How I built it
Design of ClipVent was carried out using the OnShape online CAD design tool, and prototyped using Formlabs 3D printers.
Challenges I ran into
There are a couple of components that are challenging to prototype in-house on a limited budget. After several iterations we were able to produce functional components.
Accomplishments that I'm proud of
We have demonstrated a working prototype and are in discussions with potential distributors.
What's next for ClipVent
We are seeking funding to complete development of ClipVent and bring the device to market.
Built With
onshape
Try it out
pulmologic.com
|
ClipVent
|
ClipVent is a readily manufacturable pressure cycled ventilation device that includes PEEP adjustment.
|
[]
|
[]
|
['onshape']
| 10
|
10,023
|
https://devpost.com/software/computational-vaccine
|
Developing universal vaccines for coronaviruses will serve the long-term purpose of averting dire pandemics in future as such vaccines are effective against different types of coronaviruses that can cause widespread infections including COVID-19, SARS, MERS, etc. Computational molecular design and bioinformatics can accelerate the discovery of such vaccines several folds. Historically, both these powerful tools have been used separately for design. However, to leverage both the accuracy of molecular modeling techniques and the rapid predictive capability of bioinformatics tools, an adaptive feedback strategy between the two is necessary. We envision such a strategy for the discovery of universal vaccines that will involve identifying common motifs of proteins among the viruses and selecting the ones that will be effective in triggering the immune response against all coronaviruses; designing effective carrier to deliver such peptides. The integrated and automated workflow with feedback between different stages should also be scalable across volunteer computing platforms. With our expertise in molecular modeling (molecular simulations and computational chemistry), continuum modeling, and machine learning, we seek to partner with experts in experimental techniques, bioinformatics, and software development to realize the design framework. The added advantage is that the framework is not specific to coronaviruses and can be used for computational discovery of any types of vaccines.
What it does
High throughput epitope-antibody binding predictions with minimal user input
How I built it
Python and other open-source and free-to-use frameworks
Challenges I ran into
Accomplishments that I'm proud of
Minimal input, high throughput design
What I learned
Maximum pipeline automation is absolutely essential to make it easy for non-experts to use
What's next for Computational-Vaccine
Extend the framework by including other crucial components of vaccine design such as adjuvant and delivery system design
Built With
python
|
Computational-Vaccine
|
Automated pipeline for vaccine lead prediction.
|
['Ruchika Bajaj', 'Sriramvignesh Mani', 'Alex Thomas', 'Philip Alabi', 'Chang Woon Jang', 'Ashwin Ravichandran', 'Vishnu Vettrivel', 'Teena Bajaj']
|
[]
|
['python']
| 11
|
10,023
|
https://devpost.com/software/immunolynk
|
Cover
Cloud Infrastructure
Healthcare Administrator Interface - Dashboard View
Healthcare Administrator Interface - Employee Tracking View
Mobile Application - Sign Up Page
Mobile Application - Risk Assessment Questionnaire
Mobile Application - Symptom Survey
Mobile Application - Test Scanning Screen
Mobile Application - Calculation Splash Screen
Mobile Application - Sample "True Immunity" Score Screen
About
Antibody tests are the key -- but only with intelligent guidance.
Rapid antibody tests have the potential to
empower frontline healthcare workers
. With a simple finger-prick, they have the power to
identify immunity in just 15 minutes
. And combined with wide-scale deployment, they have the ability to deliver
crucial insights
to hospitals and governments in the battle against the pandemic.
But without the proper tools, this potential will remain untapped. We must overcome three key barriers to harness the power of immunity testing:
Uncertainty
- Hospitals must be strategic in their test deployment. Timing is critical -- if the test is performed too soon after exposure to the virus,
the results can be completely inaccurate
. And even with perfect timing, lateral flow immunoassays have a PPV of as low as 55%— a dangerous shortcoming if not augmented with data on individual exposure risk.
Disorganisation
- Health care workers will need to be retested at regular intervals. This is
essential to combat the scientific uncertainty surrounding sustained immunity
and the variable accuracy of current tests. An easy-to-implement, easy-to-use, and interoperable platform is vital for tracking this unprecedented volume of immunity tests.
Privacy Concerns
- Healthcare workers need to feel confident that their data is secure. Immunity results must not only be FDA and HIPAA-compliant, but stored by an entity that users can trust won’t use it for the wrong reasons — now and forever.
Introducing ImmunoLynk
Effortless + Trustless = Fearless.
Healthcare workers use our mobile app to take a photo of their immunity test results. Our image classification algorithm, built on Keras over a ResNet50 model, automatically reads the test result as accurately as a physician, removing any possible user interference. The corresponding photo, test result and metadata are stored securely on a corresponding IPFS Blockchain node, guaranteeing the test validity and immutability.
The virus can adapt—so can we.
Healthcare workers fill out a brief, but thorough, questionnaire about their potential exposure risk factors. Combined with daily symptom surveys and hyper-local prevalence data, our proprietary machine-learning algorithm determines the ideal time to administer an antibody test. The result is fed into a Bayesian regression model, resulting in a single, easy-to-understand “True Immunity” score. This multimodal data integration overcomes the inherent sensitivity and specificity limitations of immunoassays, creating a tailored diagnostic test capable of accurately conveying the amount of uncertainty. Best of all, all logs are immutable, distributed, and instantaneous — so healthcare workers can worry less about the privacy of our data, and more about conquering the crisis.
We scale with the pandemic.
As our network of healthcare workers and the number of completed antibody tests grow, so does the strength of our algorithms. Our blockchain operating expenses cap at the ultra-low-cost of $25/month per healthcare facility, regardless of the number of employees or tests. IPFS nodes can be deployed and run completely on cloud elastic containers, presenting no additional data transfer, data storage, or uptime cost fees. Every machine learning evaluation is processed through an optimized gateway server so requests are readily processed and delivered to the Blockchain.
Connecting the world so we can reconnect.
Widespread adoption of the ImmunoLynk platform can effectively construct a distributed worldwide research network, creating the largest-ever study on the time-course of COVID-19 and its respective antibodies. This could provide the key data necessary to intelligently lift quarantine restrictions alongside community immunity testing, as well as prepare for future pandemics.
Caring for healthcare.
Priority allocation of immunity tests to healthcare workers aligns with European Centre for Disease Prevention and Control (ECDC) and Centers for Disease Control (CDC) recommendations, and with ImmunoLynk's help, it can put health providers' minds at ease. They can care for patients, perform procedures, and return home to their loved ones with less worry about contracting or communicating the virus. Our U.S. Equal Employment Opportunity Commission (EEOC) and American Disabilities Act (ADA)-compliant dashboard software for administrators also permits easy-to-implement tracking of employee symptoms and immunity tests, allowing them to strategically tackle staffing shortages in the wake of looming surges.
What we have done over the past few weeks
By integrating two initially discrete teams based around the globe and collaborating across diverse areas of expertise, we developed a feasible solution for the problems presented above in the context of a realistic business plan. These teams initially connected as participants in the
MIT COVID-19 Hackathon
, where one was announced a
winner of the challenge
. This joint team was subsequently named the
first prize winner
in the
Lumiata COVID-19 Global AI Hackathon
.
So far, we have created a working prototype of (1) a mobile app dashboard connected to a gateway server that (2) leverages machine learning to (3) combine questionnaire answers, location data and antibody test results to provide users with a "True Immunity" score registered to IPFS Blockchain.
(1) - The mobile app uses Expo.io API with React components to interact with users. It consists of a login and registration page, a symptom and risk questionnaire, and finally a test scanning section. This
interface is currently functional
. It takes a photo of the test placed on a QR barcode, sends it to a Gateway server, and displays the processed test result with an access link to the blockchain data.
(2) - The gateway server was built using the
lightweight Flask framework
with an exposed REST API, which receives both images and data from two distinct endpoints. Images are then submitted through the Keras model and, ultimately, uploaded along with the result and metadata to the
IPFS Blockchain node through Infura's API
.
(3) - Our image processing pipeline was built with Tensorflow, Keras and OpenCV2 on the ResNet-50 architectural network. It was extensively optimized to achieve an
accuracy rate of 91.8% for reading & recognizing the test result (a rate either very near to or better than the human baseline)
for feeding forward to the Bayesian Hierarchical Model.
We have also partnered with healthcare providers to gain access to anonymized symptom and exposure risk questionnaire data linked to antibody test results. This allows us to now predict infection with some degree of certainty purely from questionnaire data.
Our solution’s impact on the crisis
Accelerates the antibody testing process.
The manufacturing capacity of inexpensive and accurate lateral flow immunoassays is increasing exponentially, shifting the bottleneck from test availability to test administration rate. ImmunoLynk enables decentralized testing, alleviating the burden on hospital departments and research facilities. Our effortless data collection platform is woven into a privacy-focused decentralized storage solution, the nature of which was
recently promoted by hundreds of scientists
and
endorsed by Apple and Google
. This data is impossible to alter, hack, or forge by any bad actors.
Helps answer the question: Do antibodies provide immunity?
Studies of the effects of direct exposure of people who have previously caught COVID-19
are being planned
, but are fraught with ethical concerns and high associated costs. By collecting data on healthcare workers, one of the highest risk populations, ImmunoLynk can assess whether there is a substantial reinfection rate and if higher levels of antibodies can reduce that rate. This is a key question that needs to be addressed prior to the implementation of any wide-scale immunity-based solutions.
Inform the immunity passport debate and provide solutions.
As we assess the protective effect of antibodies against reinfection, we can provide much-needed information on whether immunity passports would have a significant societal benefit. Our decentralized storage of information would also make passport data safe and immutable.
The value of our solution after the crisis
Our systemized data from tracking antibody levels & predicting the level of immunity will remain important for possible
future waves of COVID-19 over the coming months to years
. It will also help to determine if antibody count actually confers immunity and, if so, to what extent. Even as large scale vaccinations are rolled out, our solution could easily be deployed for individuals to assess their immune response.
ImmunoLynk is just as applicable to other pandemics
for which lateral flow immunoassays are widely used, such as malaria, hepatitis, and dengue. ImmunoLynk can also help
aid diagnosis of an active illness
if adapted to use antigen immunoassays. Both could even be incorporated into a single test.
At the US level, our use of decentralized technology is in line with the nature of the
The Cures Act
, which "aims to empower Americans with their health data, delivered conveniently to computers, cell phones, and mobile applications". We are actively pursuing integration with the standardized API denoted in The Cures Act. Moreover, it allows a platform for managers to silo their employee's healthcare information,
a requirement of the ADA
. This easy-to-deploy management of healthcare information is
massively scalable to outside industries
, such as nursing homes, factories, travel & hospitality, and so much more.
At the EU level, our use of decentralized technology will be instrumental in advancing the
EU’s health data sharing initiative.
The European Commission has previously recommended that, in order to make further progress in interoperability, developments in digital technologies such as artificial intelligence, high-performance computing, decentralized technologies, and cybersecurity solutions should be carried out, noting that it would
increase trust and general feelings of accountability in government
.
Globally, our approach of tapping into a constant stream of data will
greatly benefit research specifically linked to antibody testing (e.g. virology) and, more generally, immunity.
Considering healthcare workers experience the most regular exposure to COVID-19, they are arguably the best individuals for identifying correlations & trends in the study of immunity. Epidemiologists could also use our data for pandemic modeling (SIR & SEIR), allowing
better preparation for future pandemics
. Lastly, it could
propel future healthcare adoption of blockchain technology
— possibly dispelling the popular notion that it is only useful in cryptocurrency, financial technology, & supply chain management.
What's next?
We need more images of lateral flow immunoassays
to improve the accuracy of our image classification algorithm. We already have thousands of images of lateral flow immunoassays, but more images of positive results and of assays from different manufacturers will help to ensure maximum reliability of our ResNet-50-based CNN classifier.
We need more questionnaire answers
linked to immunity test results to improve our prediction algorithm. We are in discussions with UC Berkeley (The University of California, Berkeley) and UCSF (The University of California, San Francisco) to utilize our platform in their large scale studies to further improve our prediction algorithm. We have also been collaborating with
COV-CLEAR
for the integration of our platform into their clinical trials in the UK.
We need introductions to hospital administrators and government officials
. Our market research demonstrates considerable widespread interest in a platform like ours that streamlines and improves the testing process; we just need the opportunity to prove that our system works and iron out any minor problems. We are currently working with Lumiata to introduce our platform to one of their healthcare partners.
Built With
amazon-web-services
blockchain
expo.io
flask
infura
ipfs
javascript
keras
opencv
python
react
react-native
resnet
tensorflow
Try it out
immunolynk.com
github.com
github.com
github.com
github.com
immunolynk.herokuapp.com
|
ImmunoLynk
|
Immunity testing meets AI + Blockchain.
|
['Anurag Koyyada', 'Veeresh Shringari', 'Niamh Higgins | ImmunoLynk', 'Kaue Cano', 'Ruan Comelli', 'Matheus Tosta', 'Dmytro Ustianenko, PhD', 'Khush Tawar', 'Shivam Vedak', 'Excellence Ilesanmi', 'Tai Wu', 'Claudio Vilas Boas', 'Claire Louise Donnat', 'Frederick De St Pierre Bunbury']
|
['2nd place', 'First Prize, $10,000 and idea productionalized', 'Global SOS Finalist - Everything Remote Track']
|
['amazon-web-services', 'blockchain', 'expo.io', 'flask', 'infura', 'ipfs', 'javascript', 'keras', 'opencv', 'python', 'react', 'react-native', 'resnet', 'tensorflow']
| 12
|
10,023
|
https://devpost.com/software/breath-7pljc6
|
BREATH algorithm
Inspiration
The team that we created was inspirational. Everyone on the team felt motivated to work toward a common solution to improve the care of newborns throughout the world during COVID-19, and beyond.
What it does
Our app guides users with simple yes/no questions through the first minutes of the baby's life.
It covers many different complications that may occur and tells the user exactly how to cope with the situation by using audio and video instructions (PPV, compressions, alternative airways...)
The delivery of a baby can be nerve-racking, and the interventions are time dependent, so to avoid the risk that the provider loses track of time, a stopwatch is always visible, tracking the age of the baby in minutes and seconds.
Precisely-timed data about newborns is currently not available but carries a huge potential. With our app, each use of the app represents the delivery of one baby. This data is tracked and timestamped. Information about each delivery and the complications that the baby suffered are sent to our back-end.
How we built it
Identified relevant complications we wanted to cover and how one should act in that case (followed universal NRP guidelines for care after delivery)
Built a flow-chart diagram with the expertise of Dr. Holly Martin (practicing UCSF Pediatric Hospitalist who provides neonatal resuscitation and care and has worked to develop this concept internationally as well)
Recorded audio files, modified existing video files
Implemented the front-end of the app in flutter
Code implementation of the entire algorithm is complete for the English language and we have provisions in place for expanding it to other languages (12 languages are available for implementation through the collective linguistic capacity of our team.)
Added a backend using flask REST API
Challenges We Faced
Quickly getting team up to speed on clinical scenario and the goal of the intervention, luckily we had the team leader, Holly Martin, who had conceived of this idea previously as well as team members from obstetricians to EMTs who also brought their experiences to bear
Creating team atmosphere while never meeting face to face - We met regularly via zoom, took full advantage of slack, and used our time together to talk about all aspects of the project, as well as also allowing for time to reflect on current events, the state of the world, and coming together after this as an even stronger team to work on the task at hand.
Next Steps
Use the MVP with UCSF trainees to improve their neonatal resuscitation skills. Gain feedback on user interface with their participation.
Develop multiple language versions to test in alternate sites around the world, including Peru, India, Pakistan, Nigeria.
Continue to communicate with possible collaborators, including San Francisco EMT and Fire Dept, Gates Foundation and USAID and UNICEF
Plan for Clinical trials as next stage testing of BREATH app
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Every year, one million babies die on the day they are born. With proper care, 80% would live. Proper care includes neonatal resuscitation in the first minutes after birth. 10% of all babies need this time-sensitive intervention or they would not survive. The U.S. is not exempt. Black infants die at twice the rate of white infants. The coronavirus outbreak is affecting new mothers. Nearly a million women in the U.S. are expected to give birth in the next three months. Pediatricians must carefully don PPE before entering the room to save a newborn's life. "Pregnant and Scared of 'COVID hospitals' They're giving birth at Home." Countless women changed their birth plans because of COVID-19 concerns.
Lives are being lost but we are here to offer a glimmer of hope. BREATH, a smartphone application that provides real time guidance to health care providers as they resuscitate newborns.
Whether you are
...a labor and delivery nurse alone when the baby is born
...a midwife delivering a home birth
...a provider in Pakistan trying to save a baby
... or an EMT called emergently to a delivery.
The BREATH app was presented as a project for the UCSF COVID-19 Hackathon. Six incredible people volunteered for our team, bringing a wealth of skills and experience: software development, user interface, backend development, 911 dispatcher, obstetrics, marketing, and project management. We held virtual scrum meetings every other day to discuss accomplishments, roadblocks and plans. We effectively collaborated with team members in multiple states and countries. We tracked our progress and iterated on features while collaborating between meetings via Slack. Using lessons learned from prior feedback for our prototype, we started from scratch, developing and implementing a cross-platform application that runs on Android, iOS, and the web using the Flutter framework. With clinical guidance, we implemented the entire resuscitation algorithm. We included key features identified during the prototyping phase, the code timer, voice prompts, and media demonstrating the correct intervention. We also developed and deployed a REST API to collect de-identified resuscitation metadata to better understand pain points and usage patterns. See the video or Github for a demo of our MVP, with the potential for 12 language options. This MVP was built from the ground up during the UCSF COVID-19 Hackathon.
Using our integrated app and database, clinician-researchers will have access to never-before-seen information. This data will open a door, enhancing our understanding of the first minutes of life, and saving babies. Additionally, the app can be deployed immediately to train UCSF students and residents during the COVID pandemic. As we continue to focus attention on the institutional racism and bias that has led to disproportionately poor outcomes for black and brown families, BREATH provides an opportunity for improved, equitable care.
The UCSF Hackathon has the opportunity to truly "advance health worldwide" through this technology. We hope that the BREATH app will decrease neonatal mortality and the inequities that it represents throughout the world.
Built With
flask
flutter
mongodb
Try it out
github.com
|
BREATH
|
BREATH, is a smartphone application that provides real-time guidance to health care providers as they resuscitate newborns, given the limitations COVID-19 has created on staffing around the world.
|
['Holly Martin', 'Zille Huma', 'Philip Alabi', 'Amartya Dave', 'SveaMeyer13 Meyer']
|
[]
|
['flask', 'flutter', 'mongodb']
| 13
|
10,023
|
https://devpost.com/software/targeting-orf-3a-mediated-inflammation-in-covid-19-patients
|
Inspiration
The major cause of COVID-19 patient death is inflammation-related complication, and ORF3a is the major inflammation inducer. Targeting ORF3a can be a effective way to reduce COVID-19-related patient death.
What it does
We are screening drugs that reduce ORF3a protein levels, leading to decreased inflammation.
How I built it
Construct ORF3a-GFP-reporter lines and perform drug screening.
Challenges I ran into
Scheduling and coordinate with different people during the lockdown.
Accomplishments that I'm proud of
We have the platform established for the large-scale drug screening.
What I learned
Together, we will win the battle with COVID-19!
What's next for Targeting ORF-3a mediated inflammation in COVID-19 patients
Screen and optimize the lead compound.
|
Targeting ORF-3a mediated inflammation in COVID-19 patients
|
As ORF3a is a major inflammation inducer of the COVID-19 virus, target ORF3a can be a specific and effective way to reduce patient death.
|
['Zhenyi An']
|
[]
|
[]
| 14
|
10,023
|
https://devpost.com/software/ucsf-doctors-academy-student-tracker
|
Inspiration
The students who have completed the doctors academy program and go onto health professions are our inspiration. They are our future doctors. As we navigate through this pandemic, the need for healthcare professionals is even greater - especially for our underserved populations. The Doctors Academy has three sites in the Central Valley of California and focuses on supporting students with academic enrichment, clinical experience and college readiness so that they are prepared to enter college as a pre-health major.
This short video will give you a glimpse into the impact our program has on students:
https://youtu.be/zxXtlBih144
What it does
As a result of COVID-19, schools and instructional delivery will look very different. There are many unknowns, like how prepared are teachers to teach in this virtual environment? How will students be supported academically to pursue college and careers? This is why we decided to develop the Doctors Academy Student Tracker. The DAStudentTracker will allow our academic program coordinators to track students' academic progress, setup intervention plans, and provides a way to directly communicate with students and parents on what is needed to stay on track towards college readiness.
How I built it
Gordon and Sarah built the front end of this tool using the Flask framework and SQLAlchemy library in Python, HTML, CSS, and Javascript. For the back end database they used MySQL.
Challenges I ran into
The biggest challenge we are running into is how to host what was developed. The current UCSF IT infrastructure has many security requirements and we have had multiple meetings during the duration of the Hackathon, trying to figure out how we'll actually be able to access the student tracker.
Accomplishments that I'm proud of
I am most proud of how quickly Gordon and Sarah worked at the development of this student tracker. At the start of the hackathon, they both shared that they are novices, but willing to learn and help with our project. Through their research, they quickly agreed upon the use of Python and SQL - and got right to work. Within a few days, the DAStudentTracker came to life and we were able to continue through a storyboarding process to build out the functionality.
What I learned
I learned that the only way an idea could come to life is if you put it out there for others to support. Having never done any web development before, Gordon and Sarah were able to learn a lot about the process of developing and deploying web applications. While our project wasn't the most technical, we believe it could have a tremendous impact on the future of our healthcare workforce.
What's next for UCSF Doctors Academy Student Tracker
We need to determine how to continue funding the next phase of development, which would include creating a messaging tool within the tracker, as well as building out the larger database. Similar to Electronic Medical Records, we believe there is a market for this type of tool, especially as more health pathway programs move toward a virtual mode of instruction.
Built With
css
flask
html
javascript
mysql
python
sqlalchemy
|
UCSF Doctors Academy Student Tracker
|
Keeping the next generation of health professionals on track!
|
['Emy Lopez Phillips, Ed.D.', 'Gordon Luu', 'Mckenna Hauss', 'Sarah Woldemariam']
|
[]
|
['css', 'flask', 'html', 'javascript', 'mysql', 'python', 'sqlalchemy']
| 15
|
10,023
|
https://devpost.com/software/uplasma
|
uPlasma hand and small object disinfection device
Coronavirus disease 2019 (COVID-19) has an incredible high transmission rate and induced a once-in-a-century global pandemic. The main way to control the spread of the deadly virus is by maintaining hygiene.lack of sanitizing gel and side effects of chemical disinfectant inspired us to us our innovation to help people like ourselves to maintain hygiene easier and more efficient.
uPlasma is developing new disinfection and sanitizing agent in the format of spray and solution. one of the most important advantages of our plasma spray and solution generating devices is that it isn't susceptible to the supply chain vulnerabilities of traditional methods. The device uses a novel method to produce cold plasma, which turns air into a flow of reactive and excited species (electrons, charged particles, reactive oxygen species (ROS), reactive nitrogen species (RNS)) that can safely and quickly treat hands, surfaces, and medical devices. These meritorious behaviors are due to the presence of air molecules and atoms in an excited state.
uPlasma sanitizing device is environmentally friendly with an efficient decontamination technique and a possible solution for hand sanitization, environmental decontamination, specifically to prevent the transmission of the SARS-CoV-2 virus particularly in the clinical environment.
Our commercial prototype is in the process to be built by focusing on low-cost, effective, and deployable designs for commercialization with the aim to be widely used in this COVID-19 health crisis and can become a tool for maintaining hygiene in people's daily lives after crisis.
This prototype will be built at uPlasma Inc. Novel and multifunctional uPlasma devices will be developed and tested for COVID-19 by the following methods: (1) producing gaseous and vapor phase plasma species to kill coronavirus, (2) treating liquid to get plasma solution for hand and surface sanitation to prevent spread of COVID-19 infection.
The basic engineering research on technology is mostly completed. A functional version of the novel component (for the air-fed plasma device) has been tested, and general design for the prototype has been created. The next challenge is to build a prototype that can be feasible for daily use and early deployment. The prototype will include low-cost, dependable, off-the-shelf electronics. Multiple basic configurations have been planned, and testing will identify which is both the most effective.
Our plan is to test these functions and treatment modalities. We will work with medical researchers at UCLA, area hospitals, hospitals on other UC campuses, and areas where COVID-19 may be currently straining medical resources. The uPlasma devices are easily operable for COVID-19 experiments and treatments, even during the environment of sheltering-in-place. uPlasma devices will be portable and low cost so that they are easy, convenient, and comfortable to use. Also, experiments will be conducted for viruses on a range of medical surfaces including masks, gloves, instruments, etc.
the uPlasma disinfecting devices can be used in two formats 1- disinfecting hands and small objects such as mobile phone and keys and 2- s device hat will be used for decontamination surfaces and packages.
we are proud to announce our prototype send to the virology lab to be tested for proof of concept and UCLA health announced interest in using this device after proof of concept.
we hope to raise fund to build our commercial prototype and be ready for commercializing in 3 months to contribute in prevention of virus transmission
Team
• Dr. Richard E. Wirz, Prof. at UCLA, Founder and Director of uPlasma, Inc.
World-renowned plasma science and technology expert; Former NASA Engineer, Affiliate for NASA's Jet Propulsion Laboratory at Caltech, and Advisor for NASA's Glenn Research Center; Air Force, Principal Advisor on Advanced Plasma Propulsion Technologies; Chair of Fluid Mechanics for UCLA MAE; Associate Fellow, American Institute of Aeronautics and Astronautics.
• Dr. Zhitong Chen, Postdoc at UCLA, Founder and CTO of uPlasma, Inc.
Expert in cold atmospheric plasma physics, plasma-activated solutions plasma medicine, and plasma devices. Innovator of several plasma devices.
• Dr. Leyla Mirmomen, CEO of uPlasma Inc.High Tech Entrepreneurship, Businesses; Development, Leadership, Management. Started her own business with $ 25 million In revenue. Awarded among 100 best entrepreneurs.
Built With
hardware
|
uPlasma
|
We developed a disinfection device independent from supply chain. This novel method produce cold plasma, by turning air into a flow of reactive and excited species that safely and quickly disinfect.
|
['Leyla Mirmomen']
|
[]
|
['hardware']
| 16
|
10,023
|
https://devpost.com/software/computational-drug-screening-to-covid-19
|
Docking Mode between SARS-CoV-2 Virus and Remdesivir
Inspiration
For several months, the coronavirus COVID-19 pandemic has threatened human lives affecting almost all countries around the world. Unfortunately, there have been no vaccines and drugs to prevent COVID-19 yet. By Dr. Fauci from the White House Coronavirus Task Force, the first vaccine could be developed as early as 2021 Spring despite the warning of the second wave of coronavirus coming fall in 2020. Thus, urgent drug development for COVID-19 is necessary. One possible approach to expedite the development should utilize molecular dynamics (MD) simulations and drug binding simulations to screen and propose potential compounds, including the current trial drugs such as Remdesivir, as quickly as possible.
How I built it
Computer simulations, of course, can provide easy, fast, and safe approaches to test the dummy receptors, which play like ACE2 receptors, fishing the virus and even mutants.
Challenges I ran into
To search and list small molecules to inhibit SARS-CoV-2 virus protein.
Accomplishments that I'm proud of
Screening the binding mode between the target SARS-CoV-2 3CL protease and existing drugs. Predictions of binding free energies using homology modeling and docking simulations.
What's next for Computational drug screening to COVID-19
In vitro assays to validate predictions
Built With
chimera
molecular-dynamics-simulations
swissdock
swissmodel
|
Computational drug screening to COVID-19
|
Accelerating the development of drug design and dummy receptors for COVID-19 using Molecular Dynamics Simulations and Drug Binding Calculations
|
['Chang Woon Jang', 'Philip Alabi']
|
[]
|
['chimera', 'molecular-dynamics-simulations', 'swissdock', 'swissmodel']
| 17
|
10,023
|
https://devpost.com/software/covid-19-early-detection
|
Covid-19 Early Detection Surveillance
The Problem
Covid-19 is a virus that has the ability to spread silently and quickly through asymptomatic and pre-symptomatic spread. As we look to open up the economy and move away from a shelter in place model of life, how can we use limited and expensive testing in a meaningful way to protect us?
The Solution
The solution is to combine
pooled screening
(a variant on pooled testing) with
frequent repeated testing
to convert our testing approach from a reactive model where we test those who are symptomatic to an early detection model where initial cases are identified quickly in the pre-symptomatic and asymptomatic stages.
With pooled screening, the group size is larger than that for pooled testing, slightly less sensitive, and negative screen results are not reported as negative test results to the individuals in the group screen. This trade-off allows for more broad and more frequent screening which ultimately improves the sensitivity of the overall early detection system. Additionally, the use of a screen rather than a test presents less regulatory hurdles for CLIA labs.
For a facility, using a pooled screen of size 16 and testing twice a week, we can achieve the following level of broad, inexpensive, and early detection:
3 days
: Average time to detect new contagious case in facility
56:1
: Uses one test per day for every 56 people under surveillance
Under $5
: Cost per person per day for early detection
Here is a more detailed write-up of the early detection system:
Covid-19 early detection surveillance on a 280 person facility using 5 tests a day
To get from here to there requires no new science but it does require a shift in thinking, coordination among the pieces, and a recognition that early detection surveillance is a tool that we should implement to protect our vulnerable populations. The submitted video covers these issues in more detail.
Potential Impact
Implementing an early detection system broadly would provide a cornerstone for controlling the spread of COVID-19 throughout the country. It is an inexpensive model (under $5 a day per person) that could be replicated all over the country. Local labs capable of running 1000 tests a day could be providing ongoing surveillance for 56,000 local community members. It could provide early detection in places like nursing homes, jails, factories, and schools.
Currently, the US is running a little over 400,000 tests a day. If 1/4 of those were devoted to the early detection model, it would provide ongoing early detection surveillance for 5.6 million people.
Goals/Progress
Our goal is to get early detection surveillance going at a large scale in the United States. The road we envision is as follows:
Phase 1. Pursue two parallel tracks centered on providing information and education to the appropriate stakeholders and helping to shepherd through a modest size implementation (nursing home, campus building, campus, K-12 school, kids summer camp).
Track 1.
Local implementation at a lab and a site.
Track 2.
Getting general awareness and policy awareness of what is possible with a goal of driving movement from the top.
Phase 2.
Amplify the implementation accomplished in phase 1 along with providing thorough information for duplicating the result to allow large scale implementation.
Things are currently in phase 1. Here is an incomplete and unordered list of progress:
Partnered with Stanford who are in the process of submitting an EUA for pooled testing.
Established relationship with the California testing task force who has been supportive.
Created a website that will be a hub for information:
https://sites.google.com/view/covid-19-early-detection
Performed the mathematical analysis to quantify the effectiveness of an early detection surveillance system as a function of screen sensitivity and frequency of testing.
Building a network of interested stakeholders (public health, research, education).
Starting an information campaign to universities with their own testing facilities.
Next Steps
We plan to continue beyond the hackathon to achieve the goal of widespread implementation of early detection surveillance. If you’d like to get involved or are interested in finding out more, visit our website (above) or contact us as
[email protected]
|
Covid-19 Early Detection
|
An inexpensive system that combines pooled screening and frequent testing to provide early detection of asymptomatic and presymptomatic spread of Covid-19
|
['Shalmali Gadgil', 'Zeph Landau', 'brandon-zhang-98']
|
[]
|
[]
| 18
|
10,023
|
https://devpost.com/software/wing-health
|
Wing Health is enabling hyper localized telehealth access for communities while addressing the national shortage of available health care providers. At the heart of Wing Health is a conversational agent that guides patients to local and trusted telehealth providers who reflect the unique needs of the community they serve. The Wing Health agent helps individuals overcome barriers to entry by guiding them to necessary resources.
Try it out
www.wing.health
|
Wing Health
|
Virtual care with a nearby health professional. Wing Health is your personal guide to accessing local telehealth offerings.
|
['Stephen Grinich']
|
[]
|
[]
| 19
|
10,023
|
https://devpost.com/software/covid-pass-vog37y
|
Team
Use Case Examples
Best Practices
Entry Screening
Feasibility & Impact
Go-To-Market Plan
Scaling Plan
Inspiration
Popl
is a four-month-old startup that's trailblazing a new market that we call Instant Contact Sharing (ICS). Reading about COVID-19 exposure notification and entry screening led us to an epiphany. We could leverage our ICS app and cloud infrastructure to provide a robust tool for COVID-19 entry screening and event/location-based exposure notification - thereby helping to get R0 below 1 while facilitating the opening-up of society.
What it does
Covid Pass
is an app and cloud software that enable users to create an online profile that includes their recent favorable COVID-19 test result and questionnaire, then wirelessly present their test result and questionnaire to gain entry into events, venues and facilities that are implementing robust entry screening for COVID-19.
To assess the credibility of COVID-19 test results that users upload, Covid Pass scans those documents and performs a keyword analysis using machine learning tools such as Tesseract OCR.
Using their phones, users can wirelessly present their favorable COVID-19 test result via a QR code, or they can use near field communication (NFC) transmission - if they want to present the test result instantly (no camera required). Both approaches work across plexiglass shields and glass barriers (and the average length of
two outstretched arms is a socially-distanced 6 feet
).
Additionally, Covid Pass enables events, venues and facilities to send exposure notifications to selected users if it’s subsequently determined that those users were exposed to the novel coronavirus.
How we built it
We built the app using pure React Native. The machine learning functionality that assesses the credibility of the test documents uses Tesseract and eng.traineddata from Google.
Challenges we ran into
The biggest challenge that we encountered was how to assess the credibility of COVID-19 test result documents so that the documents can’t be easily falsified by rogue users.
Accomplishments that we're proud of
We are most proud of improving tools to get the pandemic R0 below 1, and at the same time, facilitating the opening-up of society.
What we learned
We learned about HIPAA compliance, and how to use machine learning to assess the integrity of COVID-19 test result documents.
What's next for Covid Pass
We plan to leverage Popl’s expanding Instant Contact Sharing business to launch Covid Pass and grow its adoption.
Our current 40,000+ popl users will automatically get a free update to their app that has the Covid Pass functionality.
We plan to promote Covid Pass through Popl’s social media channels that have over 1.6 million followers.
We would also like to leverage our connections to UC, to get warm introductions to UC people who are responsible for: (1) COVID-19 test centers (e.g. Ashe Center at UCLA and Tang Center at UCB), and (2) covid-related management of UC events, programs and facilities. Those people could help us pilot and expand the use of Covid Pass for robust entry screening and event/location-based exposure notification.
Currently, Covid Pass is optimized for COVID-19 virus testing. However, we plan to expand its functionality to cover COVID-19 antibody testing and eventually, vaccination verification.
Presuming Covid Pass gains traction, we might spin-out a non-profit company to run the operation separately from Popl.
Covid Pass FAQ
Q: Could people who use Covid Pass falsify a negative COVID-19 virus test to gain entry into a facility or event?
A: Yes. As with questionnaire screening and temperature screening, Covid Pass is not a fool-proof guarantee that a person won't spread the coronavirus. That's why we recommend using Covid Pass in conjunction with temperature screening, mask wearing, and social distancing. Furthermore, Covid Pass uses machine learning tools to perform a keyword analysis on test result documents to make it harder to falsify test docs. Finally, Covid Pass requires users to check a terms of service checkbox that warns of legal consequences if a user knowingly falsifies their Covid Pass.
Q: It’s easy to see how Covid Pass is used for entry screening, but how can Covid Pass be used for event/location-based exposure notification?
A: When Covid Pass users pop an entrance guard who also has the Covid Pass app, the guard's app maintains a record of each Covid Pass profile. In addition to the profile, the searchable records include timestamp of entry, and GPS location of facility / event. If it's subsequently determined that an event or facility was contaminated with the novel coronavirus when Covid Pass users were there, then the guard's Covid Pass app can send push notifications to (only) those Covid Pass users notifying them that they were potentially exposed to the novel coronavirus, and linking to CDC instructions on what to do.
Q: How does Covid Pass handle HIPAA compliance and privacy? Also, is Popl tracking where and when I pop my Covid Pass?
A: We presume that users will only upload and pop their favorable COVID-19 test results, and therefore users might be only nominally concerned about test document privacy. Nonetheless, Popl will upgrade its cloud server storage from PCI to HIPAA standards. Also, we won't share Covid Pass information with any third parties on our own, and we only track Covid Pass usage for anonymized, aggregated data analysis. Finally, if Covid Pass gains traction, then it might make sense to spin-out a non-profit that is separate from Popl, so that Covid Pass has no commercial, for-profit incentives.
Q: Covid Pass seems to be optimized for virus testing. Can it also be used for antibody testing and eventually vaccination verification?
A: Yes, we plan to expand Covid Pass functionality beyond virus testing to antibody testing and vaccination verification. In anticipation of this expansion, we use the generic phrase "favorable test result" instead of "negative virus test result" (because for the antibody test, you want a positive test result).
Q: Does Covid Pass include a standard COVID-19 questionnaire in addition to the test result document?
A: Yes, users' Covid Pass profiles include a standard COVID-19 questionnaire.
Q: Can Covid Pass work through a plexiglass shield or glass barrier?
A: Yes, QR codes and NFC (near field communications) work through plexiglass and glass (but maybe not super thick, bullet-proof clear barriers).
Q: How do I learn more about the emerging Instant Contact Sharing market?
A: Go to
popl
.
Built With
eng.traineddata
mysql
php
python
react-native
tesseract
Try it out
Popl.co
tinyurl.com
|
Covid Pass
|
For events & facilities that want robust covid entry screening & exposure notification, the Covid Pass app enables users to wirelessly show their covid test & survey plus get exposure notifications.
|
['Jason Alvarez-Cohen', 'Stephanie Jue', 'William Chang', 'Siranush Manukyan', 'http://popl.co', 'Nick Eischens', 'Mike Cohen']
|
[]
|
['eng.traineddata', 'mysql', 'php', 'python', 'react-native', 'tesseract']
| 20
|
10,023
|
https://devpost.com/software/epiwe
|
Team EpiWe was formed though this hackathon. Nobody had worked together before. The results were amazing!
Inspiration
At QuantaSTAT we were inspired to create a self-reporting application, and buckled down the first weekend of March to deliver a website to the world that would enable people to share their health status, as a means of getting ahead of the spread of COVID-19. Within a week we had participants from dozens of countries and even a representative of a European government ask if we could provide the application in their native language.
We received an outpouring of thanks from people who finally felt like there was something proactive they could do at a time when everything felt pretty hopeless. This inspired us to continue our pursuit and to provide a self-reporting system that can have immediate impact.
What it does
Our solution enables organizations to get back in action with confidence by mitigating the spread of COVID-19 through their populations by self-reporting, and exposure reduction. We decided to target NCAA athletic programs as our first adopters because 1) They have so much at stake, 2) They are role models for their entire student-body, 3) They are naturally in-tune with their bodies, and disciplined, 4) They are at high risk of infection.
They will complete a 30 second daily symptom report and either be cleared to attend practice and events, or benched until they receive a doctor’s health clearance. If they are benched the app will tell them to stay put at home and trigger a telemedicine appointment. The telemedicine provider may prescribe a self-administered test, which will enable the participant to continue to stay home and find out if they are positive for COVID-19. The test results will immediately update in the application, and either clear the participant for activities or provide continued medical support if the participant is positive.
This application invites participants to opt-in to share their health reports with public health and researchers, and can aid in contact tracing.
Our application encourages participation through gamification and socialization. Participants receive points for every check-in. The points can be redeemed for things valuable to their ecosystem, such as beverages, car washes, discounts at merchants, etc. Additionally, teams can form and compete against each other, inspiring friendly competition. Finally, there is a social feature built into the app where participants can encourage each other to participate and support any participants who are benched.
How we built it
We leveraged the QuantaSTAT HIPAA compliant platform, and configured it to capture the initial intake data, and daily self-reporting data. We included the QS barcode scanning feature as a means of automatically updating test results, and the QS wearables integration as a means of updating vitals and location in real time, in our overall project plan. We developed a triage algorithm that provides a digital triage based on the inputs and directs to next steps. This is a machine learning algorithm so that overtime the triage will be refined. We created wireframes to guide future development of the application, including the gamification and socialization features.
Challenges we ran into
As a newly formed team we had challenges finding a meeting time that would work for all participants. All teammates had limited bandwidth due to work and other obligations, as one would expect from such a high-functioning group.
Accomplishments that we're proud of
We are proud that we were able to deliver a functioning self-reporting application, in English and Spanish, that incorporates a machine learning algorithm providing digital triage, within the time limits of this hackathon.
What we learned
We performed research in four key areas: 1) Clinical research – we were lucky to have an RN from UCSF who works with the COVID-19 hotline team help us determine what were the critical health-data inputs for our survey and algorithm. 2) Epidemiological research – our team investigated what information would be most valuable to epidemiologists so that we were sure to address this information in our initial intake form and daily check-in form. 3) Users – our team developed an interview to gather information from different contacts in athletic departments including coaches, athletes, and directors. We met with representatives from swimming, track and field, football, and cycling to learn about the challenges they are current experiencing in regards to COVID-19 and how our app could provide them the most value and greatest adherence. 4) Engagement – we researched different gamification strategies, including avatar customization and incentives, social integration, and identified what features were likely to provide the most engagement. Beta testing will enable us to learn more and refine in all of these areas.
What's next for EpiWe
During the course of this hackathon we submitted our solution to the National Science Foundation to their Seed Fund program, and we also responded to a request for information form the National Institute of Health. We hope one or both of these agencies will financially support this project going forward.
QuantaSTAT is currently raising a Series-A round, which could facilitate the expeditious launch of this solution in time for the fall athletic season.
Built With
amazon-redshift
amazon-web-services
bootstrap
google-places
java
jquery
kotlin
linux
ng
objective-c
phpapis
postgresql
python
redis
ruby
ruby-on-rails
swift
Try it out
epiwe.quantastat.com
|
EpiWe
|
Self-reporting combined with telemedicine and self-testing will reduce exposure to COVID-19 and enable society to reopen. The challenge is low adoption of contact tracing apps. We make it fun!
|
['Heather Sittig', 'Ari Mostov', 'Thomas Egan', 'Siranush Manukyan', 'Rutanshu Shah']
|
[]
|
['amazon-redshift', 'amazon-web-services', 'bootstrap', 'google-places', 'java', 'jquery', 'kotlin', 'linux', 'ng', 'objective-c', 'phpapis', 'postgresql', 'python', 'redis', 'ruby', 'ruby-on-rails', 'swift']
| 21
|
10,023
|
https://devpost.com/software/dinosars
|
Add new Entities to narrow down your search
Look at the top Entities in the results
Choose your Data Collection
Look at the top Concepts in the results
Enter your question and some of the concepts you are interested
Inspiration
Since January 2020, there has been more than 23,000 COVID-19 papers published, and the number of publications is doubling every 20 days. Innovative solutions are needed to address this information overload.
What it does
DinoSARS is an AI-driven search engine that presents semantic metadata such as high-level concepts, named entities, and keywords with search snippets. Keywords can be used for search refinement to make another search from the results. Keyword queries can be combined with concepts and entities to zoom in on more targeted content.
How I built it
We developed the orchestration layer and web application that interfaced with IBM Watson AI services. The web interface is a single page application developed with Bootstrap and jQuery.
Challenges I ran into
Researchers and medical professionals don't want to use search engines and tools they are not familiar with.
Accomplishments that I'm proud of
Building the AI-driven knowledge engine.
What I learned
IBM Watson is a good foundation for an AI solution. But customization and development is required to meet requirements of end users.
What's next for DinoSARS
We want to put more efforts on the information extraction, especially on numeric values. We are also considering other user interfaces such as chatbots and graph visualizations.
Built With
figma
ibm-watson
Try it out
dinosars.marketengine.parts
|
DinoSARS
|
Understand the latest research data to stomp out SARS-CoV-2
|
['bohanchen Chen', 'Veso VGG-Consulting', 'Srujana Munamala', 'Don T', 'Peter Carrasco', 'denisovajsh']
|
[]
|
['figma', 'ibm-watson']
| 22
|
10,023
|
https://devpost.com/software/happy-n-healthy
|
Happy N’ Healthy: An App for Exercising During Quarantine
Reconnect and realign with nature, others, and yourself. A workout app to tackle home isolation and help transition exercise habit changes due to COVID-19.
Made with 🏃by Ilmaa, Kasey, Kendall, Kenny, and Natalie
March 2020 - June 2020
📱
Try it out
🎥
Project summary video
📺
Demo video
🔬
Research summary video
📝
View our 22 page write-up
COVID-19 impacted people in many ways, from mental health to their daily way of life. We specifically wanted to tackle home isolation and the way people’s exercise routines were affected. Design is creating experiences for people so we wanted to empower users of our product with the experience of health and as much physical normality possible during COVID-19.
Mission-Guided Research
Before creating a product to help people, we needed to understand the issue through user research. We scoured the internet, performed surveys, and interviewed people to understand the problem of altered exercise habits because of being at home more due to COVID-19.
Video of our research summary:
youtu.be/r19nHAY-AvA
Secondary Research: Observing online communities
We observed several communities of Reddit and Facebook from online collaboration groups including:
Reddit groups
: r/homefitness, r/naturalbodybuilding, r/advice, r/bodyweightfitness
Facebook Groups
: Home Workout, Healthy Meals and Workout Tips, Diet and Fitness, Body Building, Workouts for Lazy People
Reddit users
mainly expressed they wanted to exercise but felt too sluggish and tired to actually start.
Facebook users
usually asked for workout programs and shows they could watch at home, they talked about their daily exercise, and how they were often times unmotivated.
Some of the main themes we observed overall were:
Adapting to home workouts/using equipment found at home
Problems with motivation due to various situations resulting from COVID-19 (financial, social, etc.)
No personal trainer/gym staff = confusion around form
Memes and complaints about gyms being closed
Asking/giving various workout recommendations
Soreness and neck/posture problems
> “Yesterday I bought one of those pull up bars you hang from your door frame…AND I CAN’T EVEN DO ONE PULL UP ALONE!!”! — u/batdud3
From our secondary research, we found that because of quarantine, there have been a lot of obstacles for people to stay motivated. For example: gyms, fitness centers, parks, and beaches have closed, inhibiting people’s desire to be active and exercise.
According to the world health organization, the shelter in place order has posed that staying at home for prolonged periods of time can pose a significant challenge for remaining physically active and even “low levels of physical activity can have negative effects on the health, well-being and quality of life of individuals.” This shows that it is extremely important for people to get as much exercise as possible during the COVID-19 crisis.
User research: Online questionnaires
Conducting secondary research led us to decide on the most appropriate questions for a survey we sent out to these workout Reddit and Facebook groups. We wanted to send out a survey to get quantitative data to understand people’s reasons and ability to exercise during the stay-at-home order.
We divided our questions into four categories of people:
Exercise only before quarantine
Exercise only after quarantine
Exercise before and after quarantine
Do not exercise at all
Within each of these categories we asked the five w’s:
who, what, when, where why
.
We ended up getting
42 responses
total. Most of the respondants attempted to exercise both before and during quarantine.
Survey analytics + statistics
Working status
91% working or students part-time or full-time
9% unemployed
Housing status (before → after stay-at-home order)
83% → 24% lived in an apartment or dorm
17% → 76% lived in a house before then after quarantine
Exercise habits
23.8% only exercised before
7.1% only started exercise now
59.5% exercised before and exercises now
9.5% did not exercise before and do not exercise now
Participants that exercised only before the quarantine (10)
80% exercised for at least 30 min at least twice a week
70% did activities that involved being outside or to be done with other (hiking, gym, dance teams, etc)
96% exercised for either health reasons or fun
60% go to a gym
40% go outdoors
40% used equipment from a gym or for outdoor activities like hiking
90% did not follow a workout regimen or program
For those who did, they were all online programs
40% exercised alone
Participants that exercised only during the quarantine (3)
66.7% exercised 4 times a week
66% followed online exercise videos
33.3% went for runs in the neighborhood, others stayed home
Resistance bands were the only reported equipment
66.7% do not follow any routines/programs
If they did, it was an online regimen
Participants that exercised before and during quarantine (25)
40% (majority) exercised 4 times a week for at least 30 min
96% did a combination of gym machines, cardio workouts, and running/dance
40% exercise for health and fitness reasons
50% exercise for body image/confidence reasons
12% used equipment from the gym
40% followed workout regimens/programs
68.8% of these respondents said they were in person
43.8% followed online programs
64% exercised alone
Participants that do not exercise (10 people)
30% cite being too “lazy” to exercise
50% cite having no equipment or access to gyms to start
But 80% responded that they only needed themselves to start exercising
Primary research: Virtual interviews
We asked survey participants if they would be willing to participate in an interview. Each of us conducted two interviews totaling in 10 interviewees. Here are some the key quotes from interviews:
“I enjoy exercising in a group bc enjoys being around and with people especially people are optimistic and happy but… I am committed to walking because of essential health problems so must stay with exercise plan to thrive so have not struggled because it it not optional.” — 65-year-old female
“I would run from ERC/Pangea parking structure to either the medical center on the left or right and just go”. Now: “I’m not eating enough to feel energized enough to run. I could eat more but haven’t had motivation. It’s a me problem.” — 19-year-old female
“To be frank, I am a lot of times lazy to exercise and usually I occupy my time with other things. Classes and other responsibilities like orgs and work can get a lot sometimes. Because of this I don’t even think about exercise since my mind focuses on something else.” — 19-year-old male
We then made analysis for each category of exerciser:
Exercised only before quarantine
It is possible that the change in lifestyle from ‘normality’ into quarantine has been the largest contributor to why they decided to stop. For example, 60% of them used to exercise in a gym — which are now closed — so the lack of equipment may put them off from changing routines to home friendly ones. Additionally, the 70% did outdoor activities which are also all blocked off. A major theme among all survey takers was loss of motivation so this may have been a large factor as well.
Exercise only after quarantine
The most noticeable fact from here is that the quality of exercise is very low. This may be because they are restricted in the types of exercise they can do, and since they are only starting now, it may impact their views on exercise.
Exercised before and after quarantine
These people seemed a lot more motivated to continue exercising through quarantine possibly because they have a solid routine down, and because 64% already exercise alone they do not have any major changes in motivation (partner wise). For them, the biggest obstacle was figuring out how to change their routines to work during quarantine.
Do not exercise at all
Among the participants saying they are too ‘lazy’, we found again that by laziness, they mean loss of motivation or not having the motivation at all to start. Additionally, self image and body confidence were also a obstacle, especially when other people are watching them. However they also responded that the only thing they needed was themselves so they have the right mindset, but just need a push to get started.
User personas
Since we were unable to meet with our group in person, we had a virtual sticky note board to make notes from our user research about different categories of people. From the general interview results, we split the types of answers into 6 archetypes:
Based off of these archetypes from our user research we created the main user personas from common theme of people. These are the key users we would target when creating and marketing our product:
Storyboard journey maps
To create a product that would solve our personas, we created storyboards with potential solutions for user problems.
Scenario 1
Scenario 1 showcases how we saw people having trouble adapting to their new home environment, especially when it comes to exercising. Our research saw that people don’t know how to exercise at home and with their limited equipment were at a complete loss. Additionally, people felt alone and awkward when exercising by themselves, unlike how they used to exercise in bigger groups (not everyone but targeted towards people who stated they worked out in groups before). Our solution calls for a mobile app that will not only teach people new home workout routines and show them what they can use at home as equipment, but also allows people to video call others to exercise as a group.
Scenario 2
Scenario 2 is mostly targeted towards students who have switched to online learning. Here, students may feel swamped with work and oftentimes will forget when to exercise. Our mobile app helps remind the student when they should exercise, stand up, and hydrate. Additionally, this app will also suggest basic exercises and stretches a student can do while at home.
Scenario 3
Scenario 3 is for those who used to workout with trainers or others who taught them how exactly to workout with the correct form. The solution app helps people connect with others to learn more about what they can do and how exactly to do it in terms of working out.
Scenario 4
Scenario 4 is for those who lack workout equipment at home. The solution is a mobile app that helps people find common objects throughout households and showing them how they can incorporate them into their workouts. Additionally, this app allows users to connect with others to complete workouts and/or post workout routines. This ultimately will also help increase one’s motivation to actually exercise.
Scenario 5
Scenario 5 is for people who, despite the quarantine, still wish to exercise outside. Our app allows users to search up places to exercise while at the same time upholding social distancing and maintaining safety. This app will help ease people in their fears of being in a crowded area during this outbreak.
Prototyping
Now that we had a solid understanding of the main product users, their background, and the issues as well as the general solutions they would be receptive toward we could begin to prototype product designs. Our personas would be most receptive to incorporating their phone into their routine so we decided to create a mobile app.
Sketches and wireframes
We created general wireframes of user flows and sketches of what the potential mobile app would look like.
Low-fidelity prototyping
We created digital outlines of our sketches and user flows.
Mood board
Moving into more visual interface prototyping, we wanted our app to have a certain feel when using it. Here were some of our inspirations:
High-fidelity prototyping
We then created high-fidelity prototypes based off of the sketches, mood-board, low-fidelity user testing, and low-fidelity prototypes.
User testing
We performed five user tests on both our low-fidelity and high-fidelity prototypes.
We created tasks and asked questions for the participants to complete to see if our app was designed in an understandable interface to someone who would be new to it.
Tasks (from low-fidelity)
Create account
Pick indoor/outdoor for their prefered workout location
If outdoor: scan current location (shows how much foot traffic is there) → search for a specific park, trail based on proximity in search bar
If outdoor: can view and select different workouts based on location → select workout based on area of focus: ex. Cardio, arms, abs
If indoor: can view and select different workout → select workout based on area of focus: ex. Cardio, arms, abs (with or without equipment option as well)
Schedule a workout meet with friends → create a meet for 5pm, (every MWF) and add two friends already connected to the app and invite one friend from contact list/facebook
Questions (from high-fidelity)
Is there anything confusing about the app?
Do you feel comfortable using this app?
What (if anything) would you want to change about this app?
Are there any features you feel are necessary?
Are there any features you feel are unnecessary?
Feedback (from low and high-fidelity)
The main general voice we received from the interview questions we asked included:
Fitness and exercise based, gave a sense of normalcy
Lacks intuitively, would need a description
Would use the app although it seems like it has a lot of features
Would use the app to exercise outside or indoors with friends
Challenges we faced
We learned a lot from this project. Here were some of our challenges and insights gained:
Communication is difficult when a group is unable to meet up in person: this makes communication even more important → at first a few of us did meet up on Zoom at first but then started doing it more as things ramped up.
Sometimes working together makes work slower: while working together is great sometimes it made things awkward and indecisive → we worked best when we met up during shorter periods of time to discuss then finish individual parts on our own time
Designs are not perfect their first time: or their second or third or…tenth → we had to iterate many times from our initial designs until we ended up with something cohesive, user-friendly, visually pleasing, and marketable
Work overlaps leads to confusion: when people are assigned similar tasks they might create two different solutions → team work makes the dream work so combining designs usually was best
Below are some of our initial designs and the results of iteration, collaboration, and working out design kinks together.
Market Evaluation
This product would be distributed on the app store and work through a free-to-use service. Users would be able to use all features of the app but would be restricted in how many workouts they could schedule with friends at a time, how many goals they had per week, and how many indoor or outdoor workouts would be displayed. They would pay a single, one-time fee of $20 to unlock all unlimited features for life.
Competitive Analysis
We evaluated similar apps on the market but found they fall short in their use during COVID, exercise accountability, and are subscription-based services. The one-time fee of $20 would seem more worth it over time and ultimately provide more benefit to users.
Final product
We performed high-fidelity user tests, created a style guide and component library, and polished it up to come up with a final product.
Welcome and home screen
When first entering the app users are greeted by the login screen. Users will see rings around their profile picture. These rings indicate the progress the user has made on their daily and weekly exercise goals. Users are asked whether or not they want to start their workout indoors or outdoors. Choosing an option here will take the user to another screen where they can finalize where, when, and who they want to workout with.
Indoor and outdoor workouts
If the user is used to exercising outside they are presented a heatmap with locations near them signaling whether it is safe and socially distant enough or not to work out. They can also filter locations based on outdoor features they are interested like having a trail.
If a user usually works out indoor like at a gym, they are shown a list of workouts than can be done at home. This is to provide inspiration and guided workouts to assist in the transition. They can sort by what type of work out it is, like strength or cardio then find related workouts.
Saved workouts and locations
Users can save their indoor and outdoor workouts then find them by searching, sorting, or filtering!
Add + connect with friends
To help motivate users, we added a friends function that can connect to their contacts or other social media to find friends. This allows them to view their progress in meeting their goals. Together, they can start on the same routines and workouts.
Chat
With our built in chat system, users are able to communicate with their friends, families, workout groups, or whoever. Here, users can do a range of things from sharing their favorite workouts to scheduling a workout with others.
Schedule
Our connect screen allows the user to see an overview of their month, along with any exercise meets they have scheduled. Scheduling a new meet is simple, just click the plus and it will bring you to a scheduling page. The user can choose a title, any potential attendees from their list of friends, a time and date, location, and type of workout all in one screen. With one click they can schedule a workout with a friend.
Profile and goals
The profile page is laid out so that the user can see all of her scheduled workouts, and the progress that she has made throughout the day and week. From there once the user clicks on add and edit goals, there will be an edit page where they can edit their bios, name, and most importantly add a new goal!
Style guide and component library
We wanted to maintain consistency and make it easy to edit components so we made a modular component library with colors, text, buttons, headers, and various commonly used cards and boxes.
Video of our project summary:
youtu.be/Lh6t1fi0DFc
Conclusion
Creating a product to solve home isolation and working out during the stay-at-home order was an exercise in user-understanding, iteration, and creating something that has the potential to truly help people at a low cost. We had a lot of interesting times designing, learning about issues that have arisen from the quarantine, and trying out own hand at being able to collaborate during COVID-19. We hope you enjoyed our process and write-up to creating a product that solves an issue that arose from this pandemic.
Changes for the future
In the future we hope to conduct more testing to refine, combine, or add features that are useful. While the shelter-in-place orders are currently being lifted, the danger of COVID-19 is still just as high, gyms are still not open, and it is likely they will be put in place again and we will want to find a team to work with to release this app to help people exercise at home. In the coming years when it is not longer necessary to be socially distant we will pivot the app to be still sociable and useful long after, with personality and accountability being our defining feature in being an app.
Our roles
Made with 🏃by Ilmaa, Kasey, Kendall, Kenny, and Natalie. Each of us worked toward the challenge and mission statement, performed research, user-testing, made write-ups, and designed interfaces. We highlight some of our key contributions:
Ilmaa: Designed Scheduling, orchestrated the mood board, refined and polished the design
Kasey: Designed Add and Connect with Friends
Kendall: Designed the Saved, Indoor, and Outdoor interfaces with filters, performed user research on diverse groups
Kenny: Designed the Home, Login, Chat, and Guided Workout for indoor and outdoors
Natalie: Designed Profile Add and Edit Goals, performed in-depth user research; spearheaded the Competitive Analysis
Try it out
www.figma.com
|
Happy N' Healthy
|
A workout app to tackle home isolation and help transition exercise habit changes due to COVID-19
|
['Kendall Nakai', 'Ilmaa Haque', 'Kasey Chen', 'Kenny Tran', 'Natalie Hun']
|
['3rd Place ($300)', 'The Wolfram Award']
|
[]
| 23
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10,023
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https://devpost.com/software/noninvasive-parkinson-s-detector
|
Landing Screen
1st Test: Static Spiral Test start
Instructions after clicking the info icon on the app bar
2nd Test: Dynamic Spiral Test start and Static Spiral Test graph result on bottom
3rd Test: Stability Test start and Dynamic Spiral Spiral Test graph result on bottom
1st Test restarts showing the Spiral Test results, which is the UPDRS percent score
Inspiration
I was inspired by a personal event to create an easy, non-invasive technique to help patients get an "early" and "accurate" diagnosis of Parkinson's, wherever they are around the world. Traditional methods of extrapolating the severity of Parkinson's Disease from patients are often time-consuming, take numerous rounds of tests and still stay undiagnosed; the cost is exorbitant, and invasive and inconvenient to the body. I was reading a
study
of a simple, new test that patients could take to find their UPDRS (Universal Parkinson's Disease Rating Scale) rating, and I decided to build upon it. There was a problem in that study; only those who had special equipment and access to a medical facility could leverage what was being done. I wanted to bring this capability to the patient with a minimalistic process, easy access, and with accuracy for as many people as possible.
What it does
The Noninvasive Parkinson's Detector is a mobile app that can be used to detect the mobility section of the UPDRS scale in less than five minutes using a series of tests. The first test is the Static Spiral Test, in which the patient traces a spiral shown on the screen. The app takes into account how much the user's hand-drawn path meanders from the actual spiral. The second test, the Dynamic Spiral Test, is similar to the Static Spiral test, except the spiral on the screen flashes on and off, forcing the patient to keep track of the spiral's shape, intending to be more difficult. The app stores the instant accelerations of each of the two tests in its respective histogram, and the Mobility UPDRS score is derived from the L2 Norm of the histograms. The closer the score is to 0%, the healthier the patient is. The third test, the Stability Test, measures how stable the patient can keep their finger on a certain point for ten seconds. Again, the lower the score, the better shape the patient is in.
After every test, the app generates a line-graph which plots the user's acceleration when drawing (pixels/unit time^2). This is also a visual indicator to show how well the patient is performing; if the graph is more-or-less closer to the x-axis, the lower the UPDRS rating. There is also an info button on the top right which gives clear instructions as to how the patient may take the test.
How I built it
I built this app using Dart/Flutter, enabling cross-platform iOS and Android capability. I used several libraries such as GestureDetector and Charts to create the app. I also used the graphic design tool, Adobe Illustrator to design the logo.
Challenges I ran into
Some challenges include finding the correct way to mathematically calculate the rating, accommodating for display metrics as some devices have a notch or wider screens, and how to create a pop-up modal. Although these took me some time to figure out, I am proud to have finished it.
Accomplishments that I'm proud of
I am proud to have created a simple but effective tester that can be accessible by anyone all around the world, without needing special equipment or medical facilities - which at this time, are really booked and busy with Coronavirus patients.
What I learned
I learned how to use GestureDetector to draw on the screen, how to create line graphs, and how to use statistics to calculate the UPDRS score.
What's next for MobiTest
I am currently looking to collaborate with a neurologist who can review or refer this app to his or her patients, so I can possibly get more data and use machine learning for alternate tests. I am also working on a feature where patients can save their previous tests, keep track of trends, and send them to their doctor.
Built With
charts
dart
flutter
gesturedetector
Try it out
github.com
drive.google.com
|
MobiTest - Parkinson's Detector
|
MobiTest non-invasively uses a series of verified tests, like the Static & Dynamic Spiral test & the Stability test, to accurately detect the motor section of the UPDRS Scale - all at home due to SIP.
|
['Shreyas Rana']
|
[]
|
['charts', 'dart', 'flutter', 'gesturedetector']
| 24
|
10,023
|
https://devpost.com/software/norona
|
Inspiration
After I lost my phone a few weeks ago, I realized I could track it down in real time using my google maps location history. Our team discovered that, by default, "Google Timeline" logs all of our location data unless the smartphone user opts out. We also discovered that this is all downloadable via "Google Takeout". If this data could be aggregated and secured in a user friendly way, the public health implications are astronomical.
Problem
While we may think that we are safe in the confines of our homes, we can't self quarantine forever. The national attitude towards social distancing is shifting, and some states are already opening back up. To prevent outbreaks in the future,
there is a dire need for contact tracing programs that identify potential carriers of COVID-19 based on their interactions with positively diagnosed patients.
However, health departments are severely understaffed. It is projected that America needs 300,000 additional staff to conduct contact tracing interviews, but only 1000 have been hired so far.
Value proposition
ContainCovid is a web application that helps contact tracers identify and isolate individuals at-risk of carrying COVID-19. These individuals may choose to anonymously share their location history data, which can help prevent community-wide transmission. These individuals may also share the contact information of people they have recently interacted with. As such, ContainCovid streamlines communications between contact tracers and their clients, effectively reducing the transmission of COVID-19.
Although many “risk exposure notification” mobile applications exist, they either continuously collect sensitive GPS data (ie SafePaths) or log interactions via bluetooth (ie apps using the Google/Apple API), the latter of which being quite useless for public health authorities to communicate with and serve their communities. These mobile apps also require ~40% of the population to download a single solution to be effective. However, only ~17% of the Singapore population downloaded an app endorsed by the national government, and the US has dozens of such competing apps that don’t cross-talk.
Fortunately, ContainCovid does not need a large critical mass of users to be effective. Our solution does not log data in real time, but rather looks back in time at existing data stored on any Google Maps user’s smartphone. As such, we don’t require anyone to download an app preemptively. Furthermore, we don’t require a huge critical mass of users. Instead, we can simply partner with healthcare organizations to share the platform with COVID-19 patients. These patients’ location trails are readily available, and this data is useful even if accessed post-diagnosis. We can then encourage these patients to invite their contacts to the platform, perhaps anonymously with a public health official as an intermediary. This will ensure that the “primary” contacts of COVID-19 patients are informed of their risk of exposure.
Core competencies
Out team's competitive advantage lies in our incredible development speed, made possible by our development stack— Webflow + meteor.js (and MongoDB) + React.
We are also partnered with professionals who are leading contact tracing efforts in Massachusetts, as well as the UC Berkeley Department of Public Health to pilot our launch within the UCPD and the Berkeley campus Tang Health Center. Through these channels, we will pilot our technology and acquire location history data from confirmed COVID-19 patients. This can all be done without accessing sensitive patient health records; in this initial phase of our launch, we will only distribute this technology through healthcare providers' communication channels, circumventing the need to verify self-reported positive tests with EHRs.
How I built it
The web application was designed in Webflow and implemented with Meteor, React, and MongoDB. We chose this stack because it would enable the designer, front end developer, and back-end developer to work in parallel for most of the process. We are currently hosting our site on Galaxy and MongoDB Atlas; both can be scaled at the click of a button.
What we built this hackathon
We restructured our entire codebase to make it more modular, allowing this project to be truly open source and ready for anyone to use. To do this, we ran a security audit of the code as well as our hosting providers. This security audit included:
defining rate limits for requests
checking for exposed client side database method calls
enabling strict browser policies on what content type from which origins can be loaded in the app
whitelisting which IPs can talk to the database
enabling two-factor authentication
requiring difficult passwords when creating an account
checking for potential NoSQL injection points.
We also fixed numerous bugs and incomplete features, such as
the inability to log out
links for web pages were inconsistent with the user flow
disjointed front and back end elements, such as the "check risk" button and the risk assessment algorithm
lack of clarity in the user flow for undiagnosed individuals
Notably, our team also doubled in size from 6 to 12 members. We worked meticulously on the UX of our
contact tracing dashboard
in Webflow based on feedback from various mentors and newly recruited designers.
However, perhaps our most significant accomplishment this hackathon was our sprint to create a web application tailored for use in a UC Berkeley public health study
. Finally, we began our customer discovery process with a Facebook ad campaign to survey whether people would be comfortable sharing their location data through a web portal, which to our surprise was over 50% of randomly sampled respondees.
Timeline
As of June 10th, 2020, ContainCovid is working towards a soft-launch as a data collection platform for a UC Berkeley study. If all goes well, ContainCovid will be used to assess the feasibility of opening up campus. ContainCovid is also working to make a centralized contact tracing portal with the aforementioned chatbot functionality. Our self-imposed deadline for this contact tracing portal is June 15th. By early July, we hope to show proof-of-concept to public health organizations, either through the UC Berkeley study or a public soft-launch. We strongly believe that rapid contact tracing is crucial to adapting to a new normal, and by mid July, we expect to integrate our platform with existing state-imposed contact tracing platforms.
To reach this endpoint, we hope to form crucial partnerships beyond our UC Berkeley network. We are currently in touch with MIT Safepaths, and we hope to partner to inform public health officials with actionable data on population-level transmission. We are also partnering with CoVis, a data intelligence platform that provides deep insights into population-level immunity. In terms of organizational structure, we are fiscally sponsored by The Youth Project USA, a 501(c)(3) nonprofit. Finally, we hope to form meaningful relationships with contact tracing organizations like Partners in Health, provider networks like Kaiser Permanente, and CROs like Curebase to distribute our platform COVID-19 patients. These patients have the most powerful and actionable data, as well as the strongest incentive to anonymously share this data to protect their communities.
Built With
atlas
meteor.js
mongodb
react
safetrace
webflow
Try it out
www.containcovid.org
docs.google.com
www.containcovid.org
github.com
|
ContainCovid
|
A web application that enables users to securely upload their location history data via Google Takeout to assist public health officials with contact tracing and community-level risk assessments
|
['Jacob Luo', 'Evan Anderson', 'Sushanth Varma']
|
['3rd Place Overall Winners', 'Track Runner Up: 3rd Place', 'Top 10']
|
['atlas', 'meteor.js', 'mongodb', 'react', 'safetrace', 'webflow']
| 25
|
10,023
|
https://devpost.com/software/flexitestport
|
FlexiTestPort
login
Doctor appointment
Doctor Dashbord
At Home Collection . Test Execution at Home
At Home Collection. Test Execution at the Lab
Standards - FHIR
ZKP
IPFS
HL7-FHIR
Inspiration
The multi-dimensional complexity the COVID situation has brought impacting the world globally. It has hit states and nations in many fronts - health, financial and mental health and general confidence.
In the initial stages, the challenge was equipments and supplies which is slowly improving. But from day 1, the accessibility and safety of testing has been and continues to be a challenge.
There has been mistrust and inadequacies in centralized systems reported in many countries in many aspects including transparency and integrity in reporting of covid related impact numbers, adequacy of testing, tracking provenance of testing including quality of test kits, and time lags in reporting of results due to technology and process limitations of existing enterprise systems.
The emerging technologies in blockchains and AI for patient-initiated platform for testing that enhances safety for everyone ( patients , doctors, other patients ), provenance for non-repudiation, re-imbursement and a privacy-protected pass for businesses and communities for overall safety.
What it does
FlexiTestPort is a platform that
For consumers, provides options for At-Home testing
At Home collection and Execution at the Lab
At Home collection and Execution at home
based on providers providing instructions over tele-test to perform the sample collection and infer
the results.
increases the safety for the patients by considerably reducing infection risk.
standards - FHIR based interface for patient mediated data transfer
For the Providers ( Doctors / Labs )
A tele-test platform for covid related testing
increases doctor/ healthworker safety
helps reduce the chances of infection and helps them with patient base confidence and
overall patient appointments
provenance for their tele - test activities in the recently changing regulatory environment with new rules
around tele health including audit, re-imbursements
a more robust system of security for off-chain test data storage in IPFSvand location pointer
transactions on-chain
a way to quickly share results with patients and potential immunity certificate passes
provenance for test-kits and for recalls should there be a need if there are inconsistencies in test kit
accuracies, as the testing still matures
For a verifier - checker role < An abstract role - could be employers , event planners, community groups, law
enforcement, small business owners>
a patient / consumer initiated privacy-protected mechanism that is secured by minimalistic data
sharing
data minimalized data aggregation minimizing chances for re-identification
How I built it
by leveraging decentralization based technologies including IPFS, the ethereum blockchain to prove
existence of key data and metadata across organizations for an audit-ready yet privacy protected designs
based on the ethereum stack
by leveraging DID specifications and components, secure distributed file storage systems
by incorporating Zero knowledge proofs for minimalistic proof sharing
by designing to FHIR API standards ( Encounter, Observation, Patient, Provider resources etc ) for
patient mediated interoperable data transfer
by researching in to the FDA EURs, CLIA standards and recent government policy decisions
Challenges I ran into
tracking the labs that are working on At-Home testing
understanding the healthcare regulatory framework (and yet there are the new opportunities after covid 19)
Accomplishments that I'm proud of
quick clinical validations working with many doctors, and academic institutions that are working
on covid testing
quick implementation of privacy-protection features
learning and mentoring
What I learned
the realization that re-opening of the economy and wide- spread high scale SAFE testing are 2 sides of the
same coin
there is considerable interest from employers and small businesses and could increase as testing maturity and
understanding of the disease and the antibodies keep increasing
people are realizing the use of tele-health and the demand is likely to increase, but the security , auditability
infrastructure needs to improve.
What's next for FlexiTestPort
continue to partner with labs
continue business development - employers, small businesses, local governments
further functional enhancements and standards FHIR based integrations
Built With
did
ethereum
fhir
solidity
zokrates
Try it out
github.com
|
FlexiTestPort
|
Safe tele-testing for COVID-19 at home, provenance for non-repudiation, quality, and patient initiated privacy-preserving pass for safe re-opening
|
['Chainaim SK', 'Dhruv Upadhyay', 'Devansh Swarup', 'Aryan Pandey', 'Shobit Puri', 'k ray']
|
[]
|
['did', 'ethereum', 'fhir', 'solidity', 'zokrates']
| 26
|
10,023
|
https://devpost.com/software/gmedchain-2e1a7k
|
GMEDCHAIN LOGO
Inspiration
The COVID-19 2020 crisis has put an added unprecedented strain on the global medical supply chain. Millions of people around the world have died because they didn’t have timely access to safe and affordable medicines, vaccines, and other health services. There have been widespread reports of fraudulent production and fraudulent claims of PPE and other critical supplies across the chain.
The medical supply chain is long, costly & multilayered, resulting in a lack of communication, transparency and trust between supply and demand. There are risks that products may not arrive in the right condition at the right locations at the right time.
The current situation is really a broader story about weak supply risk management and lack of governance and integration across the supply chain.
According to the WHO, it is estimated that up to $200 billion worth of counterfeit pharmaceutical products are sold globally every year and 50% of these drugs are purchased online. There are counterfeit medicine cases in every part of the world. Interpol officer Aline Plançon says “there is a flow of products coming from everywhere and going to everywhere, there are so many hubs."
The FDA’s Drug Supply Chain Security Act, part of the Drug Quality and Security Act, was first implemented in 2015 and sets drug and pharmaceutical product tracing, verification and identification requirements and implementation benchmarks that will roll out through 2023.
Similarly, the E.U. is putting the Falsified Medicines Directive (FMD) into action by 2019, requiring drug companies to adopt mass serialization, which encodes drug packages with a unique identifier, like a scannable barcode, serial number or radio frequency identification (RFID) coding, allowing for easy tracking by companies and regulators.
What it does
GMedChain is here to solve the medical supplies and medication shortages and fraud problems with our blockchain supply chain solutions to improve trust, efficiency, and transparency of the healthcare supply chain system.
By integrating Blockchain technology & IoT into the healthcare supply chain ecosystem, we are developing a fully transparent and decentralized platform offering safer, more efficient ways to connect with all medical supply chain stakeholders as well as to track and trace medical supplies.
Our solution provides product authentication to prevent counterfeit vaccines, cold chain monitoring management with real-time data, and advanced analytics to respond quickly and intelligently to market disruptions.
It can track and manage resources at the ecosystem level, which provides greater accuracy and better forecasts, reducing waste and preventing stock-outs, supply-side shortages, delivery time variability, and supply-chain disruptions. Especially in developing countries where no efficient system is in place, and the combination of infrastructure issues could create supply chain dysfunction.
How I built it
React UI & Baseline/Corda Blockchain platform
Challenges I ran into
Accomplishments that I'm proud of
We already have two venders that are willing to use our platform.
What I learned
According to the WHO, it is estimated that up to $200 billion worth of counterfeit pharmaceutical products are sold globally every year and 50% of these drugs are purchased online.
According to OECD estimates, up to US$2 trillion of procurement costs could be lost to corruption.
What's next for GMEDCHAIN
Within the health care field specifically, the goal of supply-chain management is to guarantee the availability of the products needed to treat patients.
The ability to track and manage resources at the ecosystem level can provide greater accuracy and better forecasts on medications and medical supplies, reducing waste from expired and damaged goods, preventing stock-outs, supply-side shortages, delivery time variability, supply-chain disruptions due to natural disasters, political unrest, and many other causes. Its Traceability enhances product safety & inventory allocation.
Blockchain is an enabling technology, which is most effective when coupled with other next-generation technologies such as the IoT, robotic cognitive automation, or smart devices. The whole ecosystem can be useful in understanding which products led to successful patient outcomes, projecting future programmatic and budgetary needs, protecting against corruption, and ultimately allowing for an increase in the number of patients served.
It is especially needed in developing countries where no efficient supply chain system is in place and the combination of infrastructure issues could create supply chain dysfunction.
Built With
ai
analytics-seo
blockchain
data
iot
machine-learning
ui
Try it out
www.gmedchain.com
|
GMEDCHAIN
|
Our solution offers an Industry-focused relationship-first platform, integrating data, smart-logistics, and blockchain to exchange and deliver life-saving medical supplies safely.
|
['Husein Attarwala, CPHIMS, PMP', 'Alejandro Ballesta', 'Miriam Dong', 'Derek Bryson', 'Yan He']
|
[]
|
['ai', 'analytics-seo', 'blockchain', 'data', 'iot', 'machine-learning', 'ui']
| 27
|
10,023
|
https://devpost.com/software/physio-box
|
Physio-box, use
Applications
Applications
Applications
Materials
Instruction 1
Instruction 2
Instrution 3
The problem
Currently, in many countries, most health professionals that must necessarily have physical contact with the client, such as physiotherapists, osteopaths, chiropractors and masseurs, are in search for devices that guarantee additional protection to ordinary PPE and allow a more hygienic and safer treatment.
Even where opening clinics is not explicitly restricted by the governments, most of the professionals cannot efficiently prevent the spread of aerosol particles.
In addition, as the coronavirus pandemic rapidly sweeps across the world, it is inducing a considerable degree of fear, worry and concern in the population, and many customers will be hesitant to actively take charge of their health and their physical well-being, to avoid infections.
The implications of social distancing and reduced customer trust on this major sector - and on People’s health status - will be drastic.
Our solution will allow an additional division between the operator and his client, limiting the direct exposure of bacteria and pathogens and their spread in the room.
The solution
Physio-box is composed of 7 surfaces in transparent polystyrene synthetic glass which, joined together by heat merge, constitute a compartment of dimensions (width x depth x height) = 64 x 50 x 64. These surfaces can be obtained by a 200x100cm glass sheet, 5 mm thick.
A further limitation of the exposure of bacteria and pathogens in the room, is guaranteed by the application of disposable transparent curtains, which can be applied by the healthcare professional before the treatment on the adhesive strips.
Based on our business model, keeping the prices affordable, and being self-sustainable, the entire cost would be around 50 € per box.
The construction is very, simple, does not require permanent installations or modifications to the pre-existing structures, and does not limit (if moderately) the movements of the professionals.
During the hackathon, we developed the idea and prepared technical drawings and specifications ready for prototyping. We have also prepared an initial business plan, pricing model and roadmap with next steps for when the project is launched. We were happy to consult several experienced mentors and incorporate their feedback in our solution.
Since the hackathon & next steps:
Since the hackathon:
Prototype
We built our first prototype and submitted to a team of physiotherapist, that guaranteed that, with the current dimensions, it is possible to implement approximately 90% of the techniques.
Next:
Approval from health authorities and certification
We are searching for Notified Body to issue the CE mark.
We are reaching out to the health authorities to get their view on our solution, and requirements to make sure our solution is accepted
Implementation of a large-scale production
We are searching for a construction partner that can help us build a prototype on a large-scale production.
Funding
We are also looking for investors and institutional funding to launch the product.
Solution’s impact on the crisis
COVID-19 can be especially hazardous on people's health, not only due to the respiratory problems related to the virus, but also because the pandemic can create a hazardous cycle. With less money in the budget, people who are already under financial stress tend to cut corners in areas they shouldn't, like healthcare. Though this tactic may seem like a good way to keep costs down, delaying medical care can actually lead to worse health outcomes and higher costs, both of which can lead to more stress.
It is therefore important that communities are proactive in assessing their risk and vulnerability and facilitate people to actively take charge of their health and their physical and emotional well-being. increase people's autonomy and control of their own lives and help them to support and face difficult situations reacting proactively in a transformative perspective- from learn helplessness to learn hopefulness.
Furthermore, Physio-box allows thousands of professionals to continue operating in a profitable manner, creating a positive impact on the region’s employment and communities’ wellbeing and economy.
Other use cases
There are also many sectors which will need this kind of solution to operate safely while establishing client trust.
Hairdressers;
beauticians;
Doctors’ and dentists’;
Built With
polycarbonate
|
Physio-box
|
Physio-Box is an affective device that guarantees additional protection to ordinary PPE to physiotherapists, osteopaths, chiropractors and masseurs, and allows a more hygienic and safer treatment.
|
['Stefano Re']
|
[]
|
['polycarbonate']
| 28
|
10,023
|
https://devpost.com/software/papure-2tpv60
|
paPURE Setup - Angeled View - Utilizing Snorkeling Mask
paPURE Setup - Front View - Utilizing Snorkeling Mask
paPURE Setup - Side View - Utilizing Snorkeling Mask
paPURE Setup - Back View - Utilizing Snorkeling Mask
Original Prototype of paPURE Design View
paPURE Base - Top View - Inserted Compressor Fan and Fan Shroud
paPURE Base - Top View - Empty
Abstract:
The Filtrexa paPURE is an affordable, 3D printed powered air-purifying respirator (PAPR) that provides our healthcare providers with better protection than even N95s, especially in high-risk and confined environments (E.g. ICUs, ERs). It incorporates readily available components and can be easily manufactured locally. We can thus increase accessibility of PAPR technology by enabling hospitals to produce and purchase it as per their need, optimizing the 3D-print to produce it at a cost that is over ten times cheaper than PAPRs currently offered on the market, and using exchanging highly specific components for readily available and affordable components. The Filtrexa paPURE also has made design changes to improve comfort, ease of use, and longevity of PAPR technology.
Introduction
One of the most immediate and impactful effects of the COVID-19 pandemic are global shortages of proper personal protective equipment (PPE), forcing healthcare providers (HCPs) to consistently work in high-risk environments and unnecessarily place their own lives at risk. Our product is a powered air-purifying respirator (PAPR) that creates a positive pressure field with filtered air to protect frontline healthcare workers from airborne threats such as SARS, TB, measles, influenza, meningitis, and most immediately COVID-19. This technology improves upon current PAPR devices in terms of cost-efficacy, ease of access, and ease of implementability. Our solution not only serves to combat general PAPR shortages across the country, but also eases PPE shortages that arise from COVID-19 and future patient surges through an on-demand 3D printing process.
Value Proposition
Powered, air-purifying respirators (PAPRs) are currently the gold standard in medicine when treating patients diagnosed with COVID-19 and other highly infectious respiratory diseases[1] due to their positive pressure system. This system filters air extremely effectively before it reaches the airway. However, this technology package is costly, often totaling over $1800[2] and requires highly specific components which are currently in short supply. Both well-established hospitals such as the Mayo Clinic (with a ratio of 4500 physicians to 200 PAPRs)[2] and smaller county hospitals such as the Hunterdon Medical Center (where not a single PAPR is available to physicians) are facing critical shortages of personal protective equipment (PPE). Evidently, the aforementioned barriers render PAPR technology inaccessible to most frontline HCPs, leaving them far more vulnerable to infection.
Alternatives to PAPR technology include N95s, surgical masks, and currently, homemade masks due to a worldwide shortage of PPE. Although they provide a barrier against aerosols, standard and surgical N95s are easily compromised with an improper fit and have an assigned protection factor (APF) of ten[4], while PAPRs have an APF of 25 to 1000, rendering PAPRs far more effective at protecting HCPs. Additionally, physicians tend to prefer PAPRs over N95s because PAPRs are reusable, easier to breathe through, do not require fit testing, and make them feel safer[1][5].
Our Solution
In order to provide purified air to those in the most high-risk environments, we have developed a novel, inexpensive, and accessible PAPR device that is both lightweight and 3D-printable within 24 hours. Printed using readily-available filaments (e.g. PLA, ABS), paPURE is mounted to the user’s hip and assembled via on-hand motors and batteries. (See Appendix 2.5).
Through PAPR technology, HCPs are given access to filtered positive pressure air systems (in which airflow serves to seal any gaps in masks, as well as reduce respiratory fatigue in HCPs), drastically decreasing infection risk in areas such as ICUs and ERs.
Our device’s customizability allows for interoperability with existing masks, filters, and hosing (See Appendix 3.1), enabling hospitals, or possibly surrounding hobbyists/machinists (regulatory dependent), to produce PAPRs for their physicians and nurses. For images and procedures: See Appendix 1 and 2.
The system features a dual battery set-up that allows HCPs to utilize one or both batteries independently, as well as swap out batteries while the device is in use (such as during an extended patient procedure that a physician cannot leave from). Additionally the belt system, with the fan/chassis on you lumbar and 2 battery on ports on both hips gives a better weight distribution for improved comfort in extended usages (such as a surgeon leaning in an awkward position during the operation). The use of an inline filter means that air is pushed into a filter at the end of the device, as opposed to regular PAPRs that pull air through filters. This setup means that the risk of an imperfect seal compromising air quality is virtually nullified as no negative pressure system exists after air filtration in our device. Additionally, the aforementioned inline filters are better at filtering biological particles without disturbing airflow than standard P100s and are already used extensively in anesthesiology and respiratory care departments of hospitals across the country.
After printing the device’s chassis and shroud, integration with an inline bacterial/viral filter, housing, and masks will be followed by on-site fit and efficacy testing to ensure proper device assembly.[6] Then, an HCP would don their mask, clipping the paPURE chassis and two smart power tool batteries to a provided utility belt, and connecting to the mask via a hose. At most, we expect equipping paPURE to add 1-3 minutes to a medical professional’s routine and greatly improve safety and comfort.
An Improvement from Traditional PAPRs
Our technology eliminates the need for a middle-man manufacturer. Because the only required components are readily available to hospitals and clinics, hospitals can produce the device as per their need. We anticipate working with local 3D-printing facilities to produce and assemble the product, then to distribute the Filtrexa PAPR to hospitals. Physicians and NIOSH officials (most notably Richard Metzler, the first Director of the National Personal Protective Technology Laboratory at NIOSH), have already given us promising feedback regarding the need for this technology, and we are looking into potential partnerships with PPE developers and/or motor manufacturers. Some hospital purchasing experts have additionally communicated a need for affordable PAPRs. Our solution is over 10 times cheaper than current PAPR technologies ($155; see Appendix 2, Figure 2), increasing likelihood of adoption. To allow smaller hospitals to easily obtain our technology, we plan to raise awareness of our business through phone calls and emails to hospitals throughout the country.
Implementation Plan
paPURE’s solution is implementable almost immediately. The main barrier between our tested prototype and implementation is FDA/NIOSH approval (FDA EUA Sec II/IV Approve NIOSH Certified Respirators). We have also identified conditions that will allow us to expedite the regulation and roll-out of the production (such as the IDE and 501(k) pathways suggested to us by regulatory experts).[15] Because our device is based on existing PAPR technology, this predicate nature in combination with existing precedents for 3D-printed medical technology, can help expedite its deployment.[16]
Our technology minimizes the need for a middle-men. We are partnering with regional additive manufacturers to allow for quick, standardized, yet still decentralized production of the device. The only required components are readily available to hospitals and clinics, allowing HCPs to produce the device as per their need. Additionally, if regulatory approval permits, we may utilize local schools/universities/hospitals with on-site 3D printers in order to allow for fully decentralized manufacturing. After NIOSH Approval, our device (and depending on regulatory guidelines, possibly our CAD file) will be sent to those with 3D printers available, who could print and assemble the device (See Appendix 3.1).
Players involved in the production of this technology would be hospital assembly workers, but the design is easily assembled by anyone (the only limitation being that assembly be done under a fume hood to prevent contamination). Physicians we’ve already talked to have given us promising feedback regarding the need for this technology. We are currently looking into potential partnerships with PPE developers (See Appendix 3.2) and/or motor manufacturers. Our solution is over ten times cheaper than current PAPR technologies (See Appendix 3.3), increasing the likelihood of adoption.
Due especially to the length of this health crisis, hospitals are facing dire shortages of PPE. This has accelerated our timeline, but we are confident that it is feasible given the current state of emergency (See Appendix 3.4).
Since this product has yet to be implemented in hospitals, we are writing to you today to gauge your interest in paPURE. Additionally, any feedback you have relating to our product or interest in helping us with laboratory testing of paPURE would be greatly appreciated.
We anticipate our project to reach full fruition within 6-12 months. Our timeline is as follows. Our second iteration of prototyping for clinician testing will conclude in 2-3 weeks, followed by initial clinical testing, which will finish in around 1.5 months. As soon as clinical testing is finished and the product is validated, we will submit our product officially to NIOSH for regulatory approval. We anticipate receipt of regulatory approval within 1.5 months from submission. After approval is obtained, we will also apply for either a provisional patent or copyright, depending on legal advice. Within 1-2 months after regulatory approval, we plan to roll out our product to hospitals via centralized 3D-printing. During the next 1-2 months, we will continue to iterate and optimize the product. Official hospital rollout, with multiple 3D-printing partners and company partnerships, will occur around a month later. This will be around 6-7 months from now. As seen, our timeline is aggressive as we wish to equip healthcare providers with PPE as soon as possible. The prior goals mentioned in our timeline are our key goals and objectives for the project at this time.
Current Testing and Partnerships
Technical Testing is being carried out at Filrexa's primary residence and at Johns Hopkins University and includes analysis of airflow data, battery life, and filtration efficacy. For clinical testing, we already have established connections for clinical testing with both Johns Hopkins Medical Institute and Stanford University. In regards to business-focused assistance, we have also partnered with FastForwardU for advising regarding intellectual property protection, strategic marketing, and clinical networking.
Planned Partnerships
We plan to designate one 3D-printing company (current candidates include Xometry, Protolabs, Cowtown, and Health3D) as our manufacturer during our initial launch into the market, but will continue to partner with additional 3D-printing companies as our business grows. Due to our unique manufacturing approach, all hospitals, regardless of their size, will be able to order and quickly receive PAPRs, lowering the impact of the current shortage. In order to supply the auxiliary materials such as motors, batteries, and more, we plan to initiate company partnerships with large corporations such as 3M, Dyson, Black and Decker, GE, Cuisinart, Hitachi, Makita, Shop Vac, Hoover, Bissell, Shark, iRobot, and Bosch.
Additional Video
https://youtu.be/iFMtzt52BEQ
Appendix and Citations
Click here!
Website
paPURE Website
Built With
3dprinting
cad
cpap
p100
|
paPURE
|
paPURE is a hospital accessible PAPR Technology utilizing 3D printing and readily available hardware to give healthcare's frontline the gold standard of personal protective equipment right now.
|
['Sanjana Pesari', 'Hannah Yamagata', 'Sneha Batheja', 'Joshua Devier']
|
['2nd Place Overall Winners', '1st Place', 'The Wolfram Award', 'The Best Business Idea', '3rd Place Hack', 'Best COVID-19 Hack']
|
['3dprinting', 'cad', 'cpap', 'p100']
| 29
|
10,023
|
https://devpost.com/software/faco-fight-against-corona-jfcza9
|
GIF
Confusion matrix for our final model
INSPIRATION
A diagnosis of respiratory disease is one of the most common outcomes of visiting a doctor. Respiratory diseases can be caused by inflammation, bacterial infection or viral infection of the respiratory tract. Diseases caused by inflammation include chronic conditions such as asthma, cystic fibrosis, COVID-19, and chronic obstructive pulmonary disease (COPD). Acute conditions, caused by either bacterial or viral infection, can affect either the upper or lower respiratory tract. Upper respiratory tract infections include common colds while lower respiratory tract infections include diseases such as pneumonia. Other infections include influenza, acute bronchitis, and bronchiolitis. Typically, doctors use stethoscopes to listen to the lungs as the first indication of a respiratory problem. The information available from these sounds is compromised as the sound has to first pass through the chest musculature which muffles high-pitched components of respiratory sounds. In contrast, the lungs are directly connected to the atmosphere during respiratory events such as coughs, heart rate.
PROBLEM STATEMENT
In this difficult time, a lot of people panic if they have signs of any of the symptoms, and they want to visit the doctor.
It isn’t necessary for the patients to always visit the doctor, as they might have a normal fever, cold or other condition that does not require immediate medical care.
The patient who might not have COVID-19 might contract the disease during his visit to the Corona testing booth, or expose others if they are infected.
Most of the diseases related to the respiratory systems can be assessed by the use of a stethoscope, which requires the patient to be physically present with the doctor.
Healthcare access is limited—doctors can only see so many people, and people living in rural areas may have to travel to seek care, potentially exposing others and themselves.
SOLUTION
We provide a point of care diagnostic solutions for tele-health that are easily integrated into existing platforms. We are working on an app to provide instant clinical quality diagnostic tests and management tools directly to consumers and healthcare providers. Our app is based on the premise that cough and breathing sounds carry vital information on the state of the respiratory tract. It is created to diagnose and measure the severity of a wide range of chronic and acute diseases such as corona, pneumonia, asthma, bronchiolitis and chronic obstructive pulmonary disease (COPD) using this insight. These audible sounds, used by our app, contain significantly more information than the sounds picked up by a stethoscope. app approach is automated and removes the need for human interpretation of respiratory sounds, plus user disease can also be detected by measuring heart beat from camera of smartphone.
The application works in the following manner:
User downloads the application from the app store and registers himself/herself.
After creating his/her account, they have to go through a questionnaire describing their symptoms like headache, fever, cough, cold etc.
After the questionnaire, the app records the users’ coughing, speaking, breathing and heart rate in form of video from smartphone.
After recording, the integrated AI system will analyze the sound recording, heart rate comparing it with a large database of respiratory sounds. If it detects any specific pattern inherent to a particular disease in the recording, it will enable the patient to contact a nearby specialist doctor.
The doctor then receives a notification on a counterpart of this app, for doctors. The doctor can view the form, watch the audio recording, and also read the report given by the AI of the application.
The doctor, depending upon the report of the AI, will develop a diagnosis, suggest medicines, or recommend a hospital visit if the person shows symptoms of corona or other serious condition.
In cases where the AI detects a very seriously ill patient, it will also enable the physician to call an ambulance to the users’ location and continuously track the user.
HOW WE ARE GOING TO BUILD IT
We will take a machine learning approach to develop highly-accurate algorithms that diagnose disease from cough and respiratory sounds. Machine learning is an artificial intelligence technique that constructs algorithms with the ability to learn from data. In our approach, signatures that characterize the respiratory tract are extracted from cough and breathing sounds. We start by matching signatures in a large database of sound recordings with known clinical diagnoses. Our machine learning tools then find the optimum combination of these signatures to create an accurate diagnostic test or severity measure (this is called classification). Importantly, we believe these signatures are consistent across the population and not specific to an individual so there is no need for a personalized database Following are the steps the app will take:
Receive an audio signal from the user's phone microphone
Filter the signal so as to improve its quality and remove background noise
Run the signal through an artificial neural network which will decide whether it is an usable breathing or cough signal
Convert the signal into a frequency-based representation (spectrogram)
Run the signal through a conveniently trained artificial neural network that would predict the user's condition and possible illness
Store features of the audio signal when the classification indicates a symptom
IMPACT
FACO will help patients get themselves tested at home, supporting in areas where tests and access to tests are limited. This will help democratize care in hard-to-reach or resource-strapped areas, and provide peace of mind so that patients will not overwhelm already stressed healthcare systems. Doctors will be able to prioritize patients with an urgent need related to their speciality, providing care from the palm of their hand, limiting their exposure and travel time.
CHALLENGES WE RAN INTO
No financial support
Working under quarantine measures
Working in different time-zones
Scarcity of high-quality data sets to train our models with
One Feature Related Problem- Legal shortcomings we might face when adding the tracking patient feature
ACCOMPLISHMENTS
We went from initial concept to a full working prototype. We got a jumpstart on organizational strategy, revenue and business plans—laying the groundwork for building partnerships with healthcare providers and pharmacies. On the creative side, we built our foundational brand and design system, and created over 40 screens to develop a fully working prototype of our digital experience. Our prototype models nearly the entire app experience—from recording respiratory sounds to reporting to managing contact, care, and prescriptions with physicians. Technologically, we successfully developed an algorithm for disease and have begun the application development process—well on our way to making this a fully functional product within the next 20 days.
You can explore the
full prototype here
or
watch the demo
(and
check out our promo gif
)!
WHAT WE'VE DONE SO FAR
We wanted to show that the project is feasible. Scientific literature has shown that audio data can help diagnose respiratory diseases. We provide some references below. However, it is unclear how reliable such a model would be in real situations.
For that reason, we used a publicly available annotated
dataset
of cough samples:
It is a collection of audio files in wav format classified into four different categories.
We wrote code in Python that converts those samples into MEL spectrograms. For the time being we are not using the MEL scale, just the spectrograms. We did several kinds of pre-processing of the signals, including data augmentation, then convert all pre-processed signals, along with their categories into a
databunch
object that can be used for training artificial neural networks created in the fastai library. The signals within the databunch were divided into training and validation sets.
Because the dataset size was reduced, we used
transfer learning
. That is, we used previously trained networks as a starting point, rather than training from scratch. We treated the spectrograms as if it were images and used powerful models pre-trained to classify images from large datasets. In particular, we tried both two variants of
resnet
and two variants of
VGG
differing on their depth (number of hidden layers). This approach implied turning the sprectograms into image-like representations and normalizing them according to the statistics of the original dataset our models were trained on (imagenet). We first changed the head of the networks to one that would classify according to our categories and trained only that part of the net,
freezing
the rest. Later on we
unfroze
the rest of the net and further trained it. We finally compared the different models by the confusion matrices that we obtained from the validation test. We finally settled on a model based on
VGG19
. We exported the model for later use in classifying audio samples through the pre-existing interface of our mobile app.
The results are promising, especially considering the small amount of data that we have available at this moment. We have included an image of the final confusion matrix that shows how our current network can correctly classify all four categories of signal about 50% of the time, far better than the random level of 25%. We conclude that wav files obtained trough a phone mic provide information that can be useful for diagnosing respiratory condition. We are confident that we can vastly improve both the sensitivity and the specificity of our model if we can gain access to larger, more representative datasets.
We provide an image of the final confusion matrix for our model in the gallery.
This is a
repository
that contains the most important pieces of our work, including some code, the confusion matrix image and the exported final model.
SUMMARY
We are developing digital healthcare solutions to assist doctors and empower patients to diagnose and manage diseases. We are creating easy to use, affordable, clinically validated and regulatory cleared diagnostic tools that only require a smartphone. Our solutions are designed to be easily integrated into existing tele-health solutions and we are also working on apps to provide respiratory disease diagnosis and management directly to consumers and healthcare providers.
Feel free to click on our
website
for more information. We developed this website using Javascript, HTML, CSS, Figma, and integrated it with Firebase to manage hosting and our database. Thank you for reading, and don't hesitate to reach out if you have any questions!
REFERENCES
Porter P, Claxton S, Wood J, Peltonen V, Brisbane J, Purdie F, Smith C, Bear N, Abeyratne U,
Diagnosis of Chronic Obstructive Pulmonary Disease (COPD) Exacerbations Using a Smartphone-Based, Cough Centred Algorithm, ERS 2019, October 1, 2019.
Porter P, Abeyratne U, Swarnkar V, Tan J, Ng T, Brisbane JM, Speldewinde D, Choveaux J, Sharan R, Kosasih K and Della, P,
A prospective multicentre study testing the diagnostic accuracy of an automated cough sound centered analytic system for the identification of common respiratory disorders in children,
Respiratory Research 20(81), 2019
Moschovis PP, Sampayo EM, Porter P, Abeyratne U, Doros G, Swarnkar V, Sharan R, Carl JC,
A Cough Analysis Smartphone Application for Diagnosis of Acute Respiratory Illnesses in Children, ATS 2019, May 19, 2019.
Sharan RV, Abeyratne UR, Swarnkar VR, Porter P,
Automatic croup diagnosis using cough sound recognition, IEEE Transactions on Biomedical Engineering 66(2), 2019.
Kosasih K, Abeyratne UR,
Exhaustive mathematical analysis of simple clinical measurements for childhood pneumonia diagnosis, World Journal of Pediatrics 13(5), 2017.
Kosasih K, Abeyratne UR, Swarnkar V, Triasih R,
Wavelet augmented cough analysis for rapid childhood pneumonia diagnosis, IEEE Transactions on Biomedical Engineering 62(4), 2015.
Amrulloh YA, Abeyratne UR, Swarnkar V, Triasih R, Setyati A,
Automatic cough segmentation from non-contact sound recordings in pediatric wards, Biomedical Signal Processing and Control 21, 2015.
Swarnkar V, Abeyratne UR, Chang AB, Amrulloh YA, Setyati A, Triasih R,
Automatic identification of wet and dry cough in pediatric patients with respiratory diseases, Annals Biomedical Engineering 41(5), 2013.
Abeyratne UR, Swarnkar V, Setyati A, Triasih R,
Cough sound analysis can rapidly diagnose childhood pneumonia, Annals Biomedical Engineering 41(11), 2013.
FACO APP VIDEO DEMO
LINK
FACO PRESENTATION
LINK
FACO 1st Pilot Web App
LINK
Built With
android-studio
doubango
fastai
firebase
google-cloud
google-maps
java
machine-learning
mysql
numpy
pandas
python
pytorch
sklearn
sound-monitoring-and-matching-api
spyder
webrtc
Try it out
github.com
|
FACO: Fight Against Corona
|
A contactless digital healthcare solution to assist doctors and empower patients to diagnose and manage diseases
|
['Archit Suryawanshi', 'Oghenetejiri Agbodoroba', 'Ntongha Ibiang', 'Sahil Singhavi', 'Ruthy Levi', 'Navneet Gupta', 'Mohamed Hany', 'Prachi Sonje', 'GAVAKSHIT VERMA', 'Shraddha Nemane', 'snikita312', 'Gauri Thukral', 'udit agarwal', 'Francisco Tornay', 'Rubén Aguilera García']
|
['1st place', 'The Best Women-Led Team']
|
['android-studio', 'doubango', 'fastai', 'firebase', 'google-cloud', 'google-maps', 'java', 'machine-learning', 'mysql', 'numpy', 'pandas', 'python', 'pytorch', 'sklearn', 'sound-monitoring-and-matching-api', 'spyder', 'webrtc']
| 30
|
10,024
|
https://devpost.com/software/sudoku-solver-walm2c
|
Inspiration
Enjoyment for Math
What it does
Solves the Sudoku puzzle. Submit a picture, and it will solve the sudoku puzzle for you
How I built it
Java, Backtracing
Challenges I ran into
N/A
Accomplishments that I'm proud of
N/A
What I learned
N/A
What's next for Sudoku Solver
N/A
Built With
java
Try it out
github.com
|
Sudoku Solver
|
Solves Sudoku Puzzle for you
|
['Gavin Wong', 'Allen Ye']
|
['Free WebifyBayArea Design']
|
['java']
| 0
|
10,024
|
https://devpost.com/software/dovid_x
|
Inspiration
The World Economic Forum has published a blog post highlighting the contributions that drones have made in the fight against COVID-19 , Using Drones to Fight Coronavirus” brings out the contributions made by drone in an environment where limiting human-to-human contact is of paramount importance
What it does
Our system (Dovid-x) is perform a medical analysis by using drones without human-to-human contact – which minimizes the risk for both the doctor and the normal persons.
SYSTEM CONSISTS:
Ambulance or movable lab , Scanning quad , Sterilized quad(option) , Mobile application
How I built it
STEPS OF ANALYSIS SYSTEM:
When ambulance come to a specific street, the operator inside ambulance get out the scanning quad copter towards houses by camera in SC- quad copter to start scanning people in this street.
Scanning quad copter carrying 10 small boxes inside it, each box contain scanning tools for medical analysis (PCR analysis material or analysis material for American test which the latest technology in world that can show you results in 5 minutes) for one person, each box have number for this box (1,2,3,…) , by motors in quad copter operator will move the required number box to the person , and person will open our mobile application and input his data as name ,address, age ,… and the application transfer it to QR code this QR code will scanning by QR scan device in this quad copter to save all data in memory of quad copter and save it with box number which arrive to this person ( SC-quad copter can carry payload 1 KG , if one box weight 100 gram , then SC-quad copter can carry 10 boxes every time.
SC-quad copter start to take photo for this person, make thermal temperature check for this person by IR camera and save temperature with saved data, then this person will make blood analysis still operator watching by camera on quad copter then quad copter fly to other person to do this process again , now required from person how take box which content medical analysis scanning material (PCR test material) using this material to perform analysis for himself ,will using our mobile application to know this test analysis method by Explanatory videos or can connect face to face to doctors to explain all steps and notes, then after finish this analysis test take back again all material to same box ,, he will left his box on the window and the SC-quad copter will collect it again by magnets. After SC-quad copter arrive all box to 10 person and all person finish mention steps quad copter will return again to collect all boxes.
After that SC-quad copter will go to the ambulance enter sterilization device
Operator will replace old 10 boxes with new 10 boxes to start a new scanning.
When finish scanning for all street sent this boxes sent to lab by ambulance to check results positive or negative for covid-19 infection and sent results for person by our mobile application.
For positive infection will record in database of our application of covid-19 patients to can Restrict places of covid-19
When find positive infection for Covid-19, will send sterilized quad copter to the house of infected person and starting to sterilizes the house by using UV waves generated by sterilized quad.
Then doctor can decide what action can take forward infected person
Our drone can delivery any medical tools and living requirements to infected person and our application helps doctor connect with infected person.
Challenges I ran into
Generate a new idea that helps fight covid_19 effectively, especially with the shortage of hospitals and laboratory laboratories for injuries.
Accomplishments that I'm proud of
The action of a whole system that does not depend on humans to reduce the interaction between patients and the doctor, which helps to reduce the spread of virus covid _19
What I learned
Relying on technology, programming and drone to solve global crises
What's next for DOVID_X
We now have the idea and strive to implement the idea and implement the final product
Built With
application
ardiuno
drones
mobile
|
DOVID_X
|
System Perform a medical analysis without human-to-human contact by using drone , special PCR analysis or American test To detect infection with Covid-19 Virus
|
['Mina Habib', 'abanoub boshra botros']
|
[]
|
['application', 'ardiuno', 'drones', 'mobile']
| 1
|
10,024
|
https://devpost.com/software/narad
|
A 3D render of a Narad Unit.
The Daran Labs website.
The COVID-19 Cluster tracker.
How many users the Cluster is getting, according to Google Analytics.
We are a team of 4 dedicated high school students who each have something unique to offer but have the same passion for changing the narrative in society. Our team consists of Somesh Kar (16), Angad Singh (16) Ashvin Verma (16) and Priyanshi Ahuja (17).
Team Narad came together after a member of our team saw something that they couldn’t forget. He was on a family trip and crossed multiple villages along the way, but as he was approaching the end of the trip, his car broke down near a poor farmer’s house. The farmer gave them some water. Our team member tried thanking him in Hindi but the farmer couldn’t comprehend. Just then, another man, who could understand both hindi and the local language told us about how their language isn’t that well known and that they don’t hear it anywhere. Intrigued, our team member talked to the man about how they get their information, and to his surprise, our team member learnt that the village was on the other side of a very wide information and resource gap that existed due to the very seemingly mundane reason of speaking a language that was not sufficiently recognised in our massive country.
We learned that life isn't as simple and superficial as it seems in the urban context. We learned that there are people out there who don't have access to resources, information and language. But at the same time we learnt that anyone, even high school students like us, can take a step towards making their life a bit more easier. Considering many services that have come up which support the urban population especially during the corona virus pandemic, we need one that supports the rural as well as the urban areas, and one which can be used after we pass this time as well.
We've always been fascinated by microcomputers like the Raspberry Pi, and we were amazed to discover we could underclock the CPU clock speeds of one core to effectively be able to transmit at FM frequencies. With the first version of what we built, we decided to take it a step further and add a simple piece of solid gauge wire, which acted as the antenna. With this setup, we could transmit at distances over 500m with no distinguishable loss in quality.
However, we soon realised a single Raspberry Pi couldn't cover the area of an entire Indian village. As such, we decided to use a mesh (a type of network topology, akin to star networks) system consisting of multiple raspberry pis, with only one requiring an internet connection. We settled on a WiFi based mesh network, since Raspberry Pi Zero Ws are inexpensive and come with WiFi antennas built in. We also tried using Zigbee and LoRa (Long Range radio) for this, but soon realised the extra cost didn't carry sufficient benefits for us, since each unit being relatively cheap is a major selling point for Narad.
Initally we had the plan to have a few Narad Units built with extra features such as live broadcasting in the local area, for village Sarpanchs (the head of Indian villages) to be able to transmit mission critical information whenever required. However, this plan didn't work out as the units would've need a screen(preferably touch based) and a microphone, which significantly add to the cost. This led us to build the Narad app, which gives people in local governments and intuitive way to live broadcast locally relevant information.
The Narad app is built using react native, and communicates with the Raspberry Pis in the mesh network by joining the WiFi network they create.
Built With
golang
nextjs
node.js
react
react-native
Try it out
daranlabs.now.sh
github.com
cluster.covid19india.org
github.com
|
Narad
|
Narad solves the linguist,socio-economic information barriers.We designed a localised broadcasting device, an optional app,a covid cluster-graph tracker (already has a 9.8m user base-google analytics)
|
['Somesh Kar', 'Ashvin Verma', 'Priyanshi Ahuja', 'Angad Singh']
|
['Top 10']
|
['golang', 'nextjs', 'node.js', 'react', 'react-native']
| 2
|
10,024
|
https://devpost.com/software/covid-19_tracker_analysis
|
structure
COVID-19_tracker_analysis
Functions:
Obtain data from widely-used third-party
APIs
and update the virus data automatically from server API every hour
Add a safety advice module to tell people what to do during this pandemic infection.
Write a footer and about us page to let people who browse this website have a better understanding about our project.
Build a news page using Microsoft Bing News API to let the website show the top news about coronavirus in the US
Write an Email subscriber to let people who want to keep updated with our website can subscribe to our newsletter, using
nodemailer
.
Host this project on Heroku
Tools in use:
Frontend framework: Angular and Bootstrap4
Backend framework: Node.js
Frontend icon: Google Map API and Chart.js
Host Platform: Heroku
Structure:
Getting started
To get the Node server running locally:
Clone this repo
to install all required dependencies
$ npm install
go to backend and run
$ node index.js
To get the frontend running locally:
go to /frontend, then run
Clone the repository.
Run npm install in folder.
Make sure you have npm and angular-cli installed.
After installation is done, run ng serve and open website at
https://localhost:4200
Group members:
Haoyu Guo
[email protected]
Tianyi Xu
[email protected]
License:
MIT
YouTube link：
https://www.youtube.com/watch?v=HiZ4z87VUSY&t=64s
Built With
css
html
javascript
typescript
Try it out
github.com
|
COVID-19_tracker_analysis
|
full-stack project about COVID-19
|
['Haoyu Guo', 'njuxty']
|
[]
|
['css', 'html', 'javascript', 'typescript']
| 3
|
10,024
|
https://devpost.com/software/servamap
|
We, the servaMap team, anticipate that the COVID-19 pandemic will impact
our economy throughout the rest of the year as eased measures of social
distancing will remain in place for the foreseeable future. Although
businesses will start to slowly open back up, decreased sales will have
lasting damage on those who are especially vulnerable.
Our solution mainly aims to mitigate the damage from the nationwide
economic lockdown.
ServaMap, a web-based map interface can display and provide useful
information for both the business owners and regular customers. With
consent, we first collect users' location anonymously. Along with Google
Maps API, we use the location data to see which businesses have how many
numbers of customers in a given moment. Then, the customers can make
their own independent decision as to which business is so not crowded
and therefore is relatively safer to visit. However, because of certain
privacy issues, we make sure that the location data remain anonymous to
the other users, and each user can only view the location data within
certain parameters.
For an interactive UI/UX, we display pinpoints on the map interface
showing how many people are in certain malls and businesses.
Furthermore, we have an additional functionality where people who need
urgent help can display out a pinpoint of emojis based on their
circumstances. For instance, a pinpoint of food emoji means that they
need food in their location, and a baby emoji means that they need help
with babysitting. Once a regular user comes in to find local businesses
around them, we also show that there are people in their neighborhood in
need of help. This community-based approach will be the most efficient
and economical way of protecting small businesses and neighbors. This
implementation can be scaled to provide disaster relief, when the
southern part of the country gets hit by a tornado, for example.
We are in the process of making a hotline phone number for those who
don't have ready access to the internet. With the help of IBM Watson, we
can parse out calls and extract information about what people are
requesting, whether that is food, face masks, or other things, and also
put them in the list of people who had requested for care.
Built With
crispy-forms
django
geojson
google-geocoding
google-maps
google-places
ibm-watson
postgresql
python
twilio
Try it out
github.com
|
servaMap
|
servaMap is an interactive map that connects communities during the covid-19 crisis. Think of it as Yelp, Facebook, and Google maps all in one.
|
['suraps3 Surapaneni', 'jaepark1104 Park']
|
[]
|
['crispy-forms', 'django', 'geojson', 'google-geocoding', 'google-maps', 'google-places', 'ibm-watson', 'postgresql', 'python', 'twilio']
| 4
|
10,024
|
https://devpost.com/software/beeats-d1zc5g
|
beeatsCover
beeatsBlueprint
beeatsView
Inspiration
Beeats came when I was trying to make an automaton when I was 13. Then, eight years later, I started teaching industrial automation at a technical school and realized that I could use the same concept to improve electro-pneumatic systems to improve production processes on manufacturing lines. In 2103, I won a challenge offered by industries in the manufacturing sector. next year, I improved the concept of transforming into a smart device to manage electric and pneumatic energy and won another innovation challenge. Two years later I was approved to be sponsored by Samsung and made an IoT platform to help managers and operators intake decisions on production lines in the industry. I am now seeing the opportunity to open my project to help people in difficult living conditions. And now I'm adapting the project to transform it into low-cost ventilators. In addition, the ventilators are attached to an IoT platform to allow medical staff to track infected patients and operate machines without being in direct contact with the machines and patients. This could save lives and improve the safety of medical staff.
What it does
Beeats It is a life support platform implemented by a set of sensors and actuators that works as low-cost respirators and the machine interface. To provide a low cost compressed air generation and electricity management service. As well as providing real-time data to assist medical teams in decision-making, especially in pressurized environments, and to provide remote operation and control of machines to avoid the risks that infect them.
service
We offer a service to automate, control and supervise critical medical enviroments through a modular system capable of coupling to ventilators and other equipment for real-time operation and remote control. it also has its own compressed air generation and valve control system. In addition to sensors for the acquisition of critical data that can be processed by an artificial intelligence algorithm to help support decision making as well as machine control remotely.
product
Initiality the product was designed for control of pneumatic and electrical energy. As well as the measurement of variables such as: temperature, vibration and electric current to make machines worthy in real time. Now the product can have a high potential to be applied in environments that need remote access due to the high degree of infection.
How I built it
Esp32, C++, javascript, firebase, tensorflow, 3D printing, machining, p5js, processing.
Challenges I ran into
Design a 3D model capable of organizing all electronic components in the best possible way, design the electronic system, design the board layout, machine the printed circuit board, solder the components, board the sensors and actuators, Program the microcontrolled system, program the data processing platform, develop the mobile application, test and validate, do research with customers, work on the design of the presentation, Miniaturization and cost reduction
Accomplishments that I'm proud of
I am proud to have simplified a compressed air flow generation and control system in a simple system, reduced by an integrated controller. Basically, we transformed a complex electropnemic system with large dimensions and several connections with hoses in just one integrated controller that has sensors and sends data to a cloud platform.
What I learned
Control the variables of compressed air and electricity with a simplified system
What's next for Beeats
Develop a complete platform, containing: modular hardware, mobile software and web platform
We offer a mechanical system for the generation and control of pneumatic and electrical energy, based on the combination of permanent magnets that replace complex control systems (relays, controllers and power systems). In addition to sensors for the acquisition of critical data that can be processed by an artificial intelligence algorithm to help support decision making as well as machine control remotely.
Our mission is to provide intelligent sensors and actuators as a service to help people avoid the high costs of medical treatments, using simple and modular solutions.
Mainly to help: medical units with few financial resources, therapy centers, remote communities
Built With
3dprinting
altium
c++
esp32
javascript
machining
Try it out
www.aion.systems
|
Beeats
|
Life support service implemented by a decision support platform for the creation of a cyber-physical system through smart sensors/actuators. Even as an alternative for emergency low-cost respirators
|
['Vinicius De Moraes Nascimento']
|
[]
|
['3dprinting', 'altium', 'c++', 'esp32', 'javascript', 'machining']
| 5
|
10,024
|
https://devpost.com/software/fighting-covid-today
|
Inspiration
Working on a
resource mapping toolkit
one of our mentors showed us a relevant video of
Destin @SmarterEveryDay
, then at the end of the video, I ordered the
fightingcovid.today
domain then published the call to action on it.
What it does
Supporting communities to have their easy-to-use webpage under a
fightingcovid.today
subdomain.
Providing tools and strategies for collaboration and the emergence of communities.
Listing
#FightingCOVID
solutions, resources, and other databases.
How I built it
The current page was created with Godaddy's free webpage creator but needed to rebuild in a more adaptive way with simple
HTML5
,
CSS
,
JavaScript
,
.json
technology with some kind of
NoSQL
database.
Challenges I ran into
I am too slow with coding today and hard to find good programmers who are available for agile development.
Accomplishments that I'm proud of
It was a great feeling to find the perfect available domain for it and setting up a quick MVP under. Many people were giving positive feedback about the idea.
What I learned
Today it's hard to find agile developers and more effort is needed to spread the word.
What's next for Fighting COVID Today
The next step is to
replicate the current site on a
node.js
compatible hosting to be able to start the development and
bring more people to the team,
make connections with similar projects and
find incentives for the community to build spread the world and build a bigger database.
Built With
css
html5
javascript
Try it out
fightingcovid.today
|
Fighting COVID Today
|
Supporting communities to Fight against Covid-19. Working together to find real solutions to support Life. Think Globally, Start Locally, Expand Regionally! Common platform for resources and requests.
|
['eapo sztrof']
|
[]
|
['css', 'html5', 'javascript']
| 6
|
10,024
|
https://devpost.com/software/coughdetect-anonymous-covid-19-test-using-just-coughs
|
Coughdetect Logo
How this project started.
This project started as a research study conducted by University of Essex (United Kingdom) and the Costa del Sol University Hospital (Spain), soon later other hospitals in United Kingdom and Spain joined during the peak of the pandemic to help in this very relevant data collection. The interest in this project has become inter-continental with collaborators based in Mexico, USA & Canada.
Now with this very helpful database with the help of some clever algorithms we have developed, we are looking forward to roll out a free testing application to allow anonymous testing COVID-19 worldwide using an app.
Legal Disclaimer
This mobile app is intended for informational, educational and research purposes only. It is not and is not intended for use in the diagnosis of disease or other conditions, or in cure, mitigation, treatment or prevention of disease for individuals. Health and Care providers and professionals should exercise their own independent clinical judgment when using the mobile app in conjunction with patient care.
FAQ.
Are you a company or commercial business?
No, we are a bunch of friends, students and academics trying to help the world in our free time.
Why do you think coughs is the solely marker needed for this ?
Dry coughs represent a key and atypical symptomatic feature of COVID-19 as a different to voice and breaths. Furthermore, coughs sounds are anonymous so they are GDPR complaint data. Therefore our intention is to maximize coughs as much as we can to make an anonymous, quick and cheap test. Otherwise there are already rapid swab, saliva test.
Do you have data for this?
Yes, we started collected a clinical validated dataset for research purposes with hospitalized patients. We also receive data through our minimal anonymous (totally non-tracking) webs
http://coughdetect
and
http://coronatos
. We use the data collected from this website for calibration purposes.
What is the accuracy you claim?
We have obtained 81-87% accuracy in our in sample test, which is really encouraging for such a simple and anonymous test. However, confirming an accuracy requires an exhaustive scientific peer-review process or a clinically validated trial, is not possible to determine the real accuracy of any tests Our research follows strict scientific standards. Our objective is to use this app to support our ongoing study in hospitals and seek approval and certification from the health authorities.
Do your project has any commercial perspectives
We would not like to frame this as a commercial product during the course of this pandemic. It is likely that in the future the utility of the app an algorithms developed for Coughdetect may be transfered for some other purpose than testing Covid-19. We believe that this technology could be of great help for developing countries to cope with the pandemic. Specially for those with feeble healthcare systems.
Built With
c++
java
php
python
|
Coughdetect. Fully anonymous COVID-19 test using just coughs
|
An Anonymous COVID-19 test using just coughs from our already collected Covid-19 Cough clinically validated database that can be implemented in smartphones, on-node or implantable sensors
|
['Jade Twining', 'Humberto Pérez', 'Orion Reyes', 'Nick Gatzoulis', 'Felipe Orihuela-Espina', 'Delaram Jarchi', 'Alejandro Torres-Garcia', 'Abigail Rendle', 'Javier Andreu-Perez']
|
[]
|
['c++', 'java', 'php', 'python']
| 7
|
10,024
|
https://devpost.com/software/covid-heal
|
Home page
Information on how to stay healthy
Face Touch Reminder with Artificial Intelligence
Live news
Remedies, memes, quotes, and videos
Inspiration
As a result of the current situation, the COVID-19 pandemic, our fellow family and friends are in a state of urgency. As of right now, the problem is that we have not been able to find a cure for COVID-19. With lockdowns happening everywhere, people are staying at home, quarantined, we realized that the world is in a dire situation and requires a lot of help. So, we (three high school students) decided to help out by creating a web app.
What It Does
COVID-HEAL encompasses many different and helpful functionalities:
•Built-In Artificial Intelligence that will detect when you are touching your face when on your computer and then notify you. This functionality works even in the background and is accurate at catching when you are picking your nose.
•The latest news about the COVID-19 virus based on your location.
•A live meter of the number of cases, deaths, and recoveries.
•A relax page that takes away the "corona-anxiety" that everyone is getting. This page includes light-hearted jokes and memes, special music that is proven to heal disease, and motivational quotes. On top of that, this page provides helpful tips and strategies credited by nurses and doctors to help you stay healthy at home.
How We Built It
This app is built using many languages and frameworks. The frontend is built with HTML, CSS, JQuery, and Bootstrap and the backend is built using Node.js. The frameworks and APIs we used were Tensorflow, Smartable, Bootstrap, Express.js and Forismatic. These languages & frameworks allowed us to make the web app responsive, use accurate data on COVID-19, and effectively use Artificial Intelligence.
Since we couldn’t meet each other, we planned through voice chats & voice calls. For the version control system, we used Git, and we deployed the web app using Heroku.
We decided to divide and conquer when building the app in order to use our time efficiently. Labdhi worked on a majority of the frontend, which involved designing the web app. Ashay worked on the backend and Artificial Intelligence feature, which involved implementing the Machine Learning models. Sohum worked with the APIs that gave us data on news, statistics, and memes.
Challenges We Ran Into
We found that it was a bit difficult planning & coding when we weren’t able to see each other in person, but we became flexible and learned to use Google Docs, voice calls, and Git.
Often, we came into issues when we weren’t able to merge/combine our code without conflicts. We solved this problem by using Git and using an iterative process when combining our code.
Accomplishments That We Are Proud Of
Within 4 days, we accomplished many coding feats and incorporated unique features. We were able to use machine learning models that could effectively detect when someone touches their face. We were able to pull data from numerous sources using APIs and display this information. Most importantly, we were able to deploy the web app, obtain a custom domain name for the app, covidheal.org, and spread awareness.
What We Learned
Throughout the 4 days we worked on COVID-HEAL, we had the opportunity to learn a lot. Coding-wise, we sharpened our knowledge on the application development process, from brain-storming to deploying. We learned how to effectively use Git to develop a web app. We also learned how to deploy the app on Heroku and add a custom domain name to the app.
Along with that, we learned how to use our coding skills to help out and spread awareness. We got to ask people what they are really in need of to fight against COVID-19. With the current situation, no one holds an antidote to save COVID-19 patients and that has caused a lot of unnecessary fear. We made it our goal to take this fear away, and provide a sense of positiveness and security to people around the world. This is a new and unique idea different from the existing media already in place because it gives a positive vibe rather than a cynical one.
What's Next For COVID-HEAL
Even though our web app is already up and running, there are several future steps we have in mind. We hope to make this web app more specific & interactive in future versions. The website can be made more specific to the user by giving more local news such as events occurring in the users hometown that are related to the coronavirus. More information about clean practices that limit the spread of the virus for people in quarantine would be beneficial as well as more references to numbers that can be called for further questions about the virus. Also, we believe that adding more at-home precautions and treatments would spark a lot of interest.
Built With
bootstrap
css3
html5
javascript
node.js
opencv
smartable
tensorflow
Try it out
covidheal.org
|
COVID-HEAL
|
An all-in-one tool, COVID-HEAL reminds you every time you touch your face, will keep you updated to the latest news based on your location, and provide helpful tips and strategies to avoid COVID-19.
|
['Labdhi Jain', 'Sohum Bhole', 'Ashay Parikh']
|
['Challenge Winner']
|
['bootstrap', 'css3', 'html5', 'javascript', 'node.js', 'opencv', 'smartable', 'tensorflow']
| 8
|
10,024
|
https://devpost.com/software/covid-19-test-centers-map-data
|
This page in our website contains a map with which users can enter their location and find the nearest Covid testing sites.
This page contains all the test centers per state through geodata and a quantity table.
This page contains a form with which users can submit their own testing locations.
The Team
We are a group of students that wanted to do our part in combatting the COVID-19 pandemic.
Inspiration
We saw that there were many drive-thru test centers for COVID 19, but there wasn’t a database that listed them all. So we wanted to become part of the solution to the COVID 19 crisis by making a website that includes all the testing sites.
What it does
The website we’ve created will compile the list of all the testing centers in the US so that the user can identify the nearest location. It also includes an interactive map, a data dashboard, a form to add more testing locations, and a contact page.
How we built it
First, we used Spreadsheets to collect the data. Then we used Wordpress.com to build the site. We used Storepoint to create the interactive map and also used Google Data Studio for the data dashboard.
Challenges we ran into
We had trouble collecting all the data because there was no single resource with all the testing locations. We had to go through various web pages and news articles to find the test center locations.
Accomplishments that we’re proud of
We’re proud of our cooperation in combining the map with the website. We are also proud of the many hours we put into data collection.
What we learned
We learned that cooperation is essential to success in a group project; if one person lacks, everyone suffers, and the whole project gets delayed.
What’s next for COVID 19 Test Centers Map & Data
The next step is to continue research and find all the test center locations in the US. However, to do this, it is necessary that we gain the public's help through crowd-sourcing; we can also work with other partners to collect more data.
Built With
css
google-data-studio
google-spreadsheets
html
iframe
storepoint
wordpress
Try it out
covidtestingnear.me
|
Covidtestingnear.me
|
A website to see the map of all the COVID-19 testing sites in the US, and find the nearest location to the user.
|
['Saad Nawaz', 'Ahmed Nawaz', 'Amjad Nawaz', 'Tamjeed Nawaz', 'Zahid Nawaz', 'Tauheed Nawaz']
|
['Highlighted Project', 'Honorable Mention']
|
['css', 'google-data-studio', 'google-spreadsheets', 'html', 'iframe', 'storepoint', 'wordpress']
| 9
|
10,024
|
https://devpost.com/software/alien-fighter-boy
|
Alien Fighter Boy
Alien Fighter Boy
Alien Fighter Boy
Alien Fighter Boy
Alien Fighter Boy
Alien Fighter Boy: Final World Hero is an action based 3D adventure game. The world has been conquered by hostile alien monsters originating from a nearby solar system. Humanity has been reduced to only 25% of it's population who serves as workers for the Aliens who are after our world resources.
Tiger Junior is a brilliant young boy who decides to fight for the freedom of humanity. He reversed engineered alien technology and is now able to fire endless amount of plasma fireballs against the enemies. His goal is to liberate villages from the tyranny of those Aliens.
Built With
unity
Try it out
www.indiedb.com
|
Alien Fighter Boy
|
Alien Fighter Boy: Final World Hero is an action based 3D adventure game.
|
['Stephane Jolicoeur']
|
[]
|
['unity']
| 10
|
10,024
|
https://devpost.com/software/covnatic-covid-19-ai-diagnosis-platform
|
Landing Page
Login Page
Segmentation of Infected Areas in a CT Scan
Check Suspects using Unique Identification Number (New Suspect)
Check Suspects using Unique Identification Number (Old Suspect)
Suspect Data Entry
COVID-19 Suspect Detector
Upload Chest X-ray
Result: COVID-19 Negative
Upload CT Scan
Result: Suspected COVID-19
Realtime Dashboard
Realtime Dashboard
Realtime Dashboard
View all the Suspects (Keep and track the progress of suspects)
Suspect Details View
Automated Segmentation of the infected areas inside CT Scans caused by Novel Coronavirus
Process flow of locating the affected areas
U-net (VGG weights) architecture for locating the affected areas
Segmentation Results
Detected COVID-19 Positive
Detected Normal
Detected COVID-19 Positive
Detected COVID-19 Positive
GIF
Located infected areas inside lungs caused by the Novel Coronavirus
Endorsement from Govt. Of Telengana, Hyderabad, India
Endorsement from Govt. Of Telengana, Hyderabad, India
Generate Report: COVID-19 Possibility
Generate Report: Normal Case
Generated PDF Report
Inspiration
The total number of Coronavirus cases is
2,661,506 worldwide
(Source: World o Meters). The cases are increasing day by day and the curve is not ready to flatten, that’s really sad!! Right now the virus is in the community-transmission stage and rapid testing is the only option to battle with the virus. McMarvin took this opportunity as a challenge and built AI Solution to provide a tool to our doctors. McMarvin is a DeepTech startup in medical artificial intelligence using AI technologies to develop tools for better patient care, quality control, health management, and scientific research.
There is a current epidemic in the world due to the Novel Coronavirus and here
there are limited testing kits for RT-PCR and Lab testing
. There have been reports that kits are showing variations in their results and false positives are heavily increasing.
Early detection using Chest CT can be an alternative to detect the COVID-19 suspects.
For this reason, our team worked day and night to develop an application which can help radiologist and doctors by automatically detect and locate the infected areas inside the lungs using medical scan i.e. chest CT scans.
The inspirations are as below:
1. Limited kit-based testings due to limited resources
2. RT-PCR is not as much as accurate in many countries (recently in India)
3. RT-PCR test can’t exactly locate the infections inside the lungs
AI-based medical imaging screening assessment is seen as one of the promising techniques that might lift some of the heavyweights of the doctors’ shoulders.
What it does
Our COVID-19 AI diagnosis platform is a fully secured cloud based application to detect COVID-19 patients using chest X-ray and CT Scans. Our solution has a centralized Database (like a mini-EHR) for Corona suspects and patients. Each and every record will be saved in the database (hospital wise).
Following are the features of our product:
Artificial Intelligence to screen suspects using CT Scans and Chest X-Rays.
AI-based detection and
segmentation & localization of infected areas inside the lungs
in chest CT.
Smart Analytics Dashboard
(Hospital Wise) to view all the updated screening details.
Centralized database (only for COVID-19 suspects) to
keep the record of suspects and track their progress
after every time they get screened.
PDF Reports,
DICOM Supports
, Guidelines, Documentation, Customer Support, etc.
Fully secured platform
(Both On-Premise and Cloud)
with the privacy policy under healthcare data guidelines.
Get Report within Seconds
Our main objective is to provide a research-oriented tool to alleviate the pressure from doctors and assist them using AI-enabled smart analytics platform so they can
“SAVE TIME”
and
“SAVE LIVES”
in the critical stages (Stage-3 or 4).
Followings are the benefits:
1. Real-world data on risks and benefits:
The use of routinely collected data from suspect/patient allows assessment of the benefits and risks of different medical treatments, as well as the relative effectiveness of medicines in the real world.
2. Studies can be carried out quickly:
Studies based on real-world data (RWD) are faster to conduct than randomized controlled trials (RCTs). The Novel Coronavirus infected patients’ data will help in the research and upcoming such outbreak in the future.
3. Speed and Time:
One of the major advantages of the AI-system is speed. More conventional methods can take longer to process due to the increase in demand. However, with the AI application, radiologists can identify and prioritize the suspects.
How we built it
Our solution is built using the following major technologies:
1. Deep Learning and Computer Vision
2. Cloud Services (Azure in this case)
3. Microservices (Flask in this case)
4. DESKTOP GUIs like Tkinter
5. Docker and Kubernetes
6. JavaScript for the frontend features
7. DICOM APIs
I will be breaking the complete solution into the following steps:
1. Data Preparation:
We collected more than 2000 medical scans i.e. chest CT and X-rays of 500+ COVID-19 suspects around the European countries and from open source radiology data platform. We then performed validation and labeling of CT findings with the help of advisors and domain experts who are doctors with 20+ experience. You can get more information in team section on our site. After carefully data-preprocessing and labeling, we moved to model preparation.
2. Model Development:
We built several algorithms for testing our model. We started with CNN for classifier and checked the score in different metrics because creating a COVID-19 classifier is not an easy task because of variations that can cause bias while giving the results. We then used U-net for segmentation and got a very impressive accuracy and got a good IoU metrics score. For the detection of COVID-19 suspects, we have used a CNN architecture and for segmentation we have used U-net architecture. We have achieved 94% accuracy on training dataset and 89.4% on test data. For false positive and other metrics, please go through our files.
3. Deployment:
After training the model and validating with our doctors, we prepared our solutions in two different formats i.e. cloud-based solution and on-premise solution. We are using EC-2 instance on AWS for our cloud-based solution.
Our platform will only help and not replace the healthcare professionals so they can make quick decisions in critical situations.
Challenges we ran into
There are always a few challenges when you innovate something new. The biggest challenge is “The Novel Coronavirus” itself.
One of the challenge is “Validated data” from different demographics and CT machines.
Due to the lockdown in the country, we are not able to meet and discuss it with several other radiologists. We are working virtually to build innovative solutions but as of now, we are having very limited resources.
Accomplishments that we're proud of
We are in regular touch with the State Government (Telangana, Hyderabad Government). Our team presented the project to the Health Minister Office and helping them in stage-3 and 4.
Following accomplishments we are proud of:
1. 1 Patent (IP) filled
2. 2 research paper
3. Partnership with several startups
4. In touch with several doctors who are working with COVID-19 patients. Also discussing with Research Institutes for R&D
What we learned
Learning is a continuous process. Our team learnt
"the art of working in lockdown"
. We worked virtually to develop this application to help our government and people. The other learning part was to take our proof of concept to the local administration for trails. All these “Government Procedures” like writing Research Proposal, Meeting with the Officials, etc was for the first time and we learned several protocols to work with the government.
What's next for M-VIC19: McMarvin Vision Imaging for COVID19
Our research is still going on and our solution is now endorsed by
the Health Ministry of Telangana
. We have presented our project to
the government of Telangana for a clinical trail
. So the next thing is that we are looking for trail with hospitals and research Institutes. On the solution side, we are adding more labeled data under the supervision of Doctors who are working with COVID-19 patients in India. Features like
Bio-metric verification, Trigger mechanism to send notification to patients and command room
, etc are under consideration. There is always scope of improvement and AI is the technology which learns on top of data. Overall, we are dedicated to take this solution into real world production for our doctors or CT and X-rays manufacturers so they can use it to fight with the deadly virus.
Built With
amazon-web-services
flask
google-cloud
javascript
keras
nvidia
opencv
python
sqlite
tensorflow
Try it out
m-vic19.com
|
M-VIC19: McMarvin Vision Imaging for COVID19
|
M-VIC19 is an AI Diagnosis platform is to help hospitals screen suspects and automatically locate the infected areas inside the lungs caused by the Novel Coronavirus using chest radiographs.
|
[]
|
['1st Place Overall Winners', 'Third Place - Donation to cause or non-profit organization involved in fighting the COVID crisis']
|
['amazon-web-services', 'flask', 'google-cloud', 'javascript', 'keras', 'nvidia', 'opencv', 'python', 'sqlite', 'tensorflow']
| 11
|
10,024
|
https://devpost.com/software/masked-ai-masks-detection-and-recognition
|
Platform Snapshot
Input Video
Model Processing
Model Processing
Output Video Saved
Output Video Snapshot
Output Video Snapshot
Output Video Snapshot
Output Video Snapshot
Output Video Snapshot
Output Video Snapshot
Inspiration
The total number of Coronavirus cases is 5,104,902 worldwide (Source: World o Meters). The cases are increasing day by day and the curve is not ready to flatten, that’s really sad!! Right now the virus is in the community-transmission stage and taking preventive measures is the only option to flatten the curve. Face Masks Are Crucial Now in the Battle Against COVID-19 to stop community-based transmission. But we are humans and lazy by nature. We are not used to wear masks when we go out in public places. One of the biggest challenges is “People not wearing masks at public places and violating the order issued by the government or local administration.” That is the main reason, we built this solution to monitor people in public places by Drones, CCTVs, IP cameras, etc, and detect people with or without face masks. Police and officials are working day and night but manual surveillance is not enough to identify people who are violating rules & regulations. Our objective was to create a solution that provides less human-based surveillance to detect people who are not using masks in public places. An automated AI system can reduce the manual investigations.
What it does
Masked AI is a real-time video analytics solution for human surveillance and face mask identification. Our main feature is to identify people with masks that are advised by the government. Our solution is easy to deploy in Drones and CCTVs to “see that really matters” in this pandemic situation of the Novel Coronavirus. It has the following features:
1. Human Detection
2. Face Masks Identification (N95, Surgical, and Cloth-based Masks)
3. Identify human with or without mask in real-time
4. Count people each second of the frame
5. Generate alarm to the local authority if not using a mask (Soon in video demo)
It runs entirely on the cloud and does detection in real-time with analysis using graphs.
How we built it
Our solution is built using the following major technologies:
1. Deep Learning and Computer Vision
2. Cloud Services (Azure in this case)
3. Microservices (Flask in this case)
4. JavaScript for the frontend features
5. Embedded technologies
I will be breaking the complete solution into the following steps:
1. Data Preparation:
We collected more than 1000 good quality images of multiple classes of face masks (N95, Surgical, Clothe-based masks). We then performed data-preprocessing and labeled all the images using labeling tools and generated PASCAL VOC and JSON after the labeling.
2. Model Preparation:
We used one of the famous deep learning-based object detection algorithm “YOLO V-3” for our task. Using darknet and Yolo v-3, we trained the model from scratch on 16GB RAM and Tesla K80 powered GPU machine. It took 10 hours to train the model. We saved the model for deploying our solution to the various platforms.
3. Deployment:
After training the model, we built the frontend which is totally client-based using JavaScript and microservice “Flask”. Rather than saving the input videos to our server, we are sending our AI to the client’s place and using Microsoft Azure for the deployment. We are having on-premise and cloud solutions prepared. At the moment, we are on a trail so we can’t provide the link URL.
After building the AI part and frontend, We integrated our solution to the IP and CCTV cameras available in our house and checked the performance of our solution. Our solution works in real-time on video footage with very good accuracy and performance.
Challenges we ran into
There are always a few challenges when you innovate something new. The biggest challenge is “The Novel Coronavirus” itself. For that reason, we can’t go outside the home for the hardware and embedded parts. We are working virtually to build innovative solutions but as of now, we are having very limited resources. We can’t go outside to buy hardware components or IP & CCTV cameras. One more challenge we faced was that we were not able to validate our solution with drones in the early days due to the lockdown but after taking permission from the officials that problem was not a deal anymore.
Accomplishments that we're proud of
Good work brings the appreciation and recognition. We have submitted our research paper in several conferences and international journals (Waiting for the publication). After developing the basic proof-of-concept, We went on to the local government officials and submitted our proposal for a trial to check our solution for better surveillance because the lockdown is near to be lifted. Our team is also participating in several hackathons and tech event virtually to showcase our work.
What we learned
Learning is a continuous process. We mainly work with the AI domain and not with the Drones. The most important thing about this project was “Learning new things”. We learned how to integrate “Masked AI” into Drones and deploy our solution to the cloud. We added embedded skills in our profile and now exploring more features on that part. The other learning part was to take our proof of concept to the local administration for trails. All these “Government Procedures” like writing Research Proposal, Meeting with the Officials, etc was for the first time and we learned several protocols to work with the government.
What's next for Masked AI: Masks Detection and Recognition
We are looking forward to collaborating with local administrative and the government to integrate our solution for drone-based surveillance (that’s currently in trend to monitor internal areas of the cities). Parallel, The improvement of model is the main priority and we are adding “Action Recognition” and “Object Detection” features in our existing solution for even robust and better solution so decision-makers can make ethical decisions as because surveillance using Deep Learning algorithms are always risky (bias and error in judgments).
Built With
azure
darknet
flask
google-cloud
javascript
nvidia
opencv
python
tensorflow
twilio
yolo
|
Masked AI: AI Solution for Face Mask Identification
|
Masked AI is a cloud-based AI solution for real-time surveillance that keeps an eye on the human who violates the rule by not using face masks in public places.
|
[]
|
[]
|
['azure', 'darknet', 'flask', 'google-cloud', 'javascript', 'nvidia', 'opencv', 'python', 'tensorflow', 'twilio', 'yolo']
| 12
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10,024
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https://devpost.com/software/divoc-e0fywm
|
Flow chart depicting the working of the whole system.
Homepage of the application
Teacher Login
Student Login
Teacher Dashboard
Student Dashboard
Canvas as a blackboard
Asking question in middle of a lecture
Tab Change alert to gain students attention to the lecture
Inspiration
There is an old saying,
The Show Must Go On
, which kept me thinking and finding out a way to connect teachers and students virtually and allow teachers to take lectures from home and to develop a completely open source and free platform different from the other major paid platforms.
What it does
This website is completely an open source and free tool to use
This website whose link is provided below, allows a teacher to share his / her live screen and audio to all the students connected to meeting by the Meeting ID and Password shared by the teacher.
Also this website has a feature of Canvas, which can be used as a blackboard by the teachers.
Including that, this website also contains a doubtbox where students can type in their doubts or answer to teachers questions while the lecture is going on.
Again this website also has a feature of tab counting, in which, tab change count of every student is shown to the teacher. This will ensure that every student is paying attention to the lecture.
Also, teacher can ask questions in between the lecture, similar to how teacher asks questions in a classroom.
How I built it
1) The main component in building this is the open source tool called WebRTC i.e. Web Real Time Communication. This technology allows screen, webcam and audio sharing between browsers.
2) Secondly Vuetify a very new and modern framework was used for the front end design.
3) Last but not the least NodeJS was used at the backend to write the API's which connect and interact with the MongoDB database.
Challenges I ran into
The hardest part of building this website was to find a
open source
tool to achieve screen and audio sharing. This is because Covid crisis has affected most of the countries economy due to lockdown. Hence, it is of utmost important that schools and colleges do not need to pay for conducting lectures.
Accomplishments that I'm proud of
I am basically proud of developing the complete project from scratch and the thing that anyone who has the will to connect to students and teach them can use it freely.
What I learned
I learned a new technology called WebRTC which I believe that is going to help me more than I expect in future.
What's next for Divoc
Integrating an exam module and allowing teachers to take exams from home.
Built With
mongodb
node.js
vue
webrtc
Try it out
divoc.herokuapp.com
|
Divoc
|
DIVOC - An Antidote For - COVID
|
['Sanket Kankarej']
|
[]
|
['mongodb', 'node.js', 'vue', 'webrtc']
| 13
|
10,024
|
https://devpost.com/software/covid19-kn-guide
|
Self Check page
Front Page View
News Page
Inspiration
enables one to do a self-check of symptoms of COVID 19 before approaching the test center, and notify health management of the state about the status of your sel-check whether it comes HIGH or Low risk
What it does
The aim of the WebApp is to help people understand their risk of getting infected, get access to verified updates Nationwide & information on the virus, learn about preventive measures, report a suspected case, and contact the Response Team immediately.
How we built it
We developed an API for getting statistics from our database, and allow save check, and case reports.
Challenges we ran into
Self-Check report, we run into issue of calculating those questions where ask in the self-check page.
Accomplishments that we're proud of
We did it, and having a chance to provide our own API for other to use it.
https://documenter.getpostman.com/view/2591492/SzmY9gi3
What we learned
We learned how to work in a team also how to build a solution that is rampant. We were also encouraged to solve local problems.
What's next for Covid19 KN Guide
working on the “Pin Location” feature so that people can be alerted if they have been in contact with a confirmed case through their pinned location history. This will not only curb the spread and help in tracing contacts but would also let people know that they should either isolate themselves or get tested.
Built With
html
json
mysql
php
Try it out
covid19.startupkano.com
github.com
|
Covid19 KN Guide
|
The Webapp is to help people understand their risk of getting infected, get access to verified updates Nationwide & information on the virus, and contact the Response Team immediately.
|
['Samiu Salihu', 'Shahid Sani Abdullahi']
|
[]
|
['html', 'json', 'mysql', 'php']
| 14
|
10,024
|
https://devpost.com/software/covid-fyi
|
Why we built this-
Critical information
on where to go, who to call, and what to do is scattered across gazillion tweets, websites, Whatsapp forwards. There is
no one place
for all official information for Citizens. The information is
not standardized
- they exist in non-standardized circulars. These are often
too technical
for the common man. With cases rising,
Chaos
and
misinformation
might only aggravate.
What does it do-
COVID FYI brings updated information from all
official sources
at all levels of granularity (National to Hyperlocal Data) on resources a common man could access to alleviate their problems, report emergencies or provide help. It brings the list of labs, hospitals, helpline, telemedicine, fever clinics, grocery store numbers etc. at one place such that a common man is aware.
How we built this-
We created a
nationwide database
with a team of Data folks, collating data manually across government websites releasing circulars and announcements on a daily basis. This database is used to power a frontend UI that is
user friendly
to provide only relevant information to each stakeholder and use case. If you are a
citizen
you would be more interested in testing facilities and helpline. If you are a
supplier
you could access control room numbers etc.
Challeneges we face-
Data is of such
varying forms
, at so many places, far away from the reach of common man - finding them all was a tedious task. Every day these circulars were updated, thus we needed to keep a check. There were so many classifications and sub-classifications, making it all the more difficult to decide on the right user flow (Eg. Labs include - Private, Government, Only Testing facility, Only Sample collection facility etc.)
Achievements we are proud of-
Several
startups
showed positive interest to partner with us.
Mapmyindia
wanted to provide us APIs to link location for all data.
Livehealth
an international e-diagnostic startup wanted to provide sample collection facility through them, handling the Lab-side of the market with their team, putbnb a
crowdsourcing platform
offered to crowdsource our database. Our idea was demo-ed at
Coronathon.in
(Indian Corona-Hackathon) and was also selected for
HackTheCrisis India top 300
. We have received appreciation from
several VC firms
and startups in Indian space.
Indian Institute of Management Kozhikode
is our strong pillar of support to provide outreach and media help to promote our project.
Our team
grew from mere 5 member team to 25+ (including volunteers). Without investing a single penny we have created this product in
record 5 days
.
What we learned-
We learned that though important, it is difficult to access
high-level government officials
directly. We initially thought to
partner with big brands
to put our best foot forward and scale faster. However we realised we need
proof of concept
to show the validity of our idea. Hence now our focus is to not ask for any help, and first build the initial 1lac or 0.1million users, have some numbers to speak for idea. We learnt a lot from each other's skills and got to know a lot about other areas.
Our plans for the future-
In the near future we are implementing our website for
India-wide launch
using organic mediums. We will work on extra features- Location tagging, API integration to help other projects. Since the Database is a never ending
Work-in-progress
in times like these, we will constantly update and work to add more official data. For this we are looking to work for, and work with several
state governments
.
Built With
djangorestframework
flask
google-cloud
heroku
mongodb
netlify
postgresql
react
vuejs
Try it out
covidfyi.in
github.com
github.com
|
COVID FYI
|
One-stop platform for the citizens to access covid-related services and help from official government sources.
|
['Prateek Katiyar', 'Mohammed Zeeshan Fatmi', 'Sujit Joshi', 'G Rohit', 'Yogesh Bhatt', 'Vishesh Agrawal', 'Tanmay Mundra', 'saathwik chandan', 'Aliasgar Kundawala', 'Utkarsh Gupta', 'Manan Gouhari', 'SIMRAN SONI']
|
[]
|
['djangorestframework', 'flask', 'google-cloud', 'heroku', 'mongodb', 'netlify', 'postgresql', 'react', 'vuejs']
| 15
|
10,024
|
https://devpost.com/software/coronavirus-probability-checker
|
Coronavirus Probability Checker
Short Brief
It is a Probability Checker for COVID-19 , people can input values and symptoms and accr. to the data , patient will get the probability of +ve COVID-19.
Currently , I trained this on Random data.
If we execute this idea with official Data , it can be very helpful for Peoples.
Problem Statement
India has a high population country and hence it is not possible to check everyone for the SARS-CoV-2 virus which is the cause of the corona virus outbreak.
Everyone who is living in this country is affected due to the high population as there not enough facilities for each person
The spread of false news is causing the people to worry. By the use of self evaluation for themselves and their family members they can take the decision to inform the authorities and ask for help.
If someone enters false details then that would result in a small error in the assessment of the outbreak.
The Technology/Product Concept
The developed hack is End user input based Web application. It takes the input from the user for their details and shows a list of symptoms which they may be experiencing and can confuse with Covid-19. On the basis of their entered choices the recieved input will be processed and their probability will be shown to them. If the probably is severe or high than the person is prompted to consult a doctor. His input will be stored in the system for Machine Learning. On the basis of this the priority of the tests can be done so as to reduce the transmission of the disease and also to make the most of limited testing capacity in such highly populated country such as India.
A Machine Learning Model is then trained on the data to find out the probability of a person having the infection.
The Model is then used to find out whom to test for the infection first under a limited testing capacity.
The Same Model can be used to find potential candidates for conducting random tests.
Benefits From this
There is a situation of fear among the people and spread of fake news is further making the situation worse. This Hack can be used to control this fear by self assessment of the user and their family. Also, if the prediction comes severe the authorities will be informed and this will be used to control the COVID-19 Pandemic upto a very large extent.
Built With
html
jupyter-notebook
python
Try it out
github.com
drive.google.com
|
Coronavirus-Probability-Checker
|
It is a Probability Checker for COVID-19 , people can input values and symptoms and accr. to the data , patient will get the probability of +ve COVID-19.
|
['Siddhant Khare']
|
['Honorable Mention']
|
['html', 'jupyter-notebook', 'python']
| 16
|
10,024
|
https://devpost.com/software/confrontation-of-pandemic
|
Greetings from Greece.
INTRODUCTION
A national center “National Health Bank” must be set up for the collection of the biological material of all the patients of the world who suffering from diseases with the right hierarchy of secret e-data (after an agreement), with new safe & secret telecommunication system (quantum telecommunications or something more sophisticated & secret), with new safe & secret internet (quantum internet or something more sophisticated & secret which will use blockchain, ghost polarization communication, quantum cryptography, chaos cryptography or newer sophisticated secret cryptography technology or a combination of all together), with national cloud computing, with DNA digital data storage, with machine intelligence, with artificial intelligence, with computational intelligence, with evolutionary computation, with machine learning, with deep learning, with machine vision, with automation & with the best scientists on these fields.
Biological material to be diffuse into full body – organism connected microchip organs [human organs-on-chips or human organism-on-chips or human body-on-chips or human-on-chips (from cells, stem cells & etc of the patients – people)] or into full body – organism connected printed organs (from cells, stem cells & etc of the patients – people) by multidimensional biological printers for personalized testing & continuous monitoring of patient’s biological -genetic (substances) reactions in vitro, to record the disease before tests & the cure (on molecular – submolecular – atomic – subatomic – etc levels) after continuous monitoring of multiple tests on multiple full body – organism connected microchip organs & on multiple full body – organism connected printed organs for each person – organism individually in order to understand in maximum levels everything about the confrontation of diseases.
BODY OF PAPER
Geniuses – top scientists with hazmat suits at BSL-4 laboratories – > injection on mass scale with the same disease – diseases, virus – viruses, microbe – microbes, parasite – parasites & pathogen – pathogens (experiments with each one disease – virus – microbe – parasite – pathogen each time until the completion of all the diseases – viruses – microbes – parasites – pathogens) on multiple very ill humanized mices & on multiple very ill orangutans – monkeys – apes – chimpanzees – gorillas (generally to all the organisms that have common biological – genetic material with humans) that need treatment urgently in quarantine, on multiple full body – organism connected microchip – robotic organs [full body – organism connected human organs-on-chips or human organism-on-chips or human body-on-chips or human-on-chips (from the cells – stem cells – etc of humans)] in quarantine & on multiple multidimensional full body – organism connected biological printed organs (from the cells – stem cells – etc of humans) in quarantine -> continuously monitoring & diagnosis of the multiple organisms, of the multiple full body – organism connected microchip – robotic organs & of the multiple multidimensional full body – organism connected biological printed organs [with new generation machines of PET/MRI on all the surface at the same time, with new generation machines of PET/CT on all the surface at the same time, with new generation machines of high sensitivity perovskite detectors for X-rays & maybe for low-energy G-rays, with all type of new generation machine of Ultrasound Molecular Imaging on all the surface at the same time, with Biopsies, with all types of latest technology microscopes, with mass cytometry, with full tests (blood – excrement – urine – saliva tests & etc) & etc] -> experiments with FLASH Radiotherapy, experiments with electrodes & experiments with all type of biological – light – sound – ultrasound – wave – vibration – pulse – magnetic – electrical – etc stimulations at the same time on all the organisms as & on multiple full body – organism connected microchip – robotic organs-on-chips & on multiple multidimensional full body – organism connected biological printed organs, also experiments of individually – separately injections of all the existing medicines – vaccines, of individually – separately injections of all the chemistry periodic table substances, of individually – separately injections of all the simple & edited – evolved biological & genetic material & of individually – separately injections of all the simple & edited – evolved microorganisms & later injections of all the combinations of the medicines – vaccines, of all the combinations of chemistry periodic table substances, of all the combinations of all the simple & edited – evolved biological & genetic material, of all the combinations of all the simple & edited – evolved microorganisms & of all the combinations of all of them (medicines – vaccines, chemistry periodic table, biological & genetic material & microorganisms) together (mixtures) in quarantine on multiple animals, on multiple full body – organism connected microchip – robotic organs & on multiple multidimensional full body – organism connected biological printed organs – > results (from a full test that will show us all the data that can exist about the organisms, about the multiple full body – organism connected microchip – robotic organs & about the multiple multidimensional full body – organism connected biological printed organs) -> continuously monitoring & diagnosis of the multiple organisms, of the multiple full body – organism connected microchip – robotic organs & of the multiple multidimensional full body – organism connected biological printed organs in quarantine [with new generation machines of PET/MRI on all the surface at the same time, with new generation machines of PET/CT on all the surface at the same time, with new generation machines of high sensitivity perovskite detectors for X-rays & maybe for low-energy G-rays, with all type of new generation machine of Ultrasound Molecular Imaging on all the surface at the same time, with Biopsies, with all types of latest technology microscopes, with mass cytometry, with full tests (blood – excrement – urine – saliva tests & etc) & etc]. Finally, creation of 5 separately vaccines with the safest biochemical substances, with the antibodies (from survivors) & with dead or inactivated the pathogen (in order to awake the immune system), with evolved NK cells [injection into Thymus (with mimetic scaffolds from a safe biomaterial that will be dissolved later in order to give them a boost)], with evolved B-cells [injection into Bone Marrow (with mimetic scaffolds from a safe biomaterial that will be dissolved later in order to give them a boost)] & with evolved killer & helper T-cells [injection into Thymus & Bone Marrow (with mimetic scaffolds from a safe biomaterial that will be dissolved later in order to give them a boost) with receptors that recognize the molecule which called MR1, with receptors that recognize the molecules of cancerous EVs & generally all the specific molecules of cancerous & viral cells in order to destroy them] in moderation all of them after in vitro evolution (of them) on full body – organism connected organs-on-chips (from human cells – stem cells & etc) & on full body – organism multidimensional printed organs (from human cells – stem cells & etc) in order to find all the receptors & antibodies which recognize all the cancerous & viral antigens – molecules – submolecules in vivo in order to destroy – eliminate them. These 5 vaccines will help to the confrontation of the diseases – viruses – microbes – parasites – pathogens.
CONCLUSION
Scientists must procure & produce all the existing substances - medicines in logical quantities in order to be ready.
Each laboratory of each State will do different tests (with different substances) in order to have spherical results in a record time.
The technology that is needed for everything already exists. The matter is to share the scientific data to a specific template – program on the global cloud database through cooperation & coordination all the national hospitals, clinics, health centers, educational institutes, pharmaceutical companies & laboratories. There will be real time monitoring (through the cloud database) of the interactions of the biological material of the patients – volunteers with genetics, biochemistry, nanites & all type of stimulations in order to confront the pandemic.
Finally, everything of the previous plans should become only after an agreement about the ethics & after an agreement with the volunteers - patients - people.
Thank you for your time & for everything.
An article by Chaideftos Chaideftos.
SOURCES
https://www.google.com/
https://scholar.google.com/
https://www.technologyreview.com/
http://news.mit.edu/
https://news.stanford.edu/
https://hms.harvard.edu/news
https://www.broadinstitute.org/
https://wyss.harvard.edu/
https://www.utoronto.ca/news
https://www.anu.edu.au/news
https://www.cam.ac.uk/news
http://www.ox.ac.uk/news-listing
https://news.tsinghua.edu.cn/
http://www.iitd.ac.in/media
https://www.ncbi.nlm.nih.gov/
https://www.nature.com/
https://www.cell.com/
https://www.sciencemag.org/
https://www.nejm.org/
https://www.thelancet.com/
https://jamanetwork.com/
https://www.embopress.org/
https://phys.org/
https://medicalxpress.com/
https://rupress.org/
https://www.genengnews.com/
https://www.embl.org/
https://www.researchgate.net/
https://www.ted.com/
https://www.youtube.com/
https://www.ieee.org/
https://techxplore.com/
https://sciencex.com/news/
https://www.mdpi.com/
https://www.biorxiv.org/
https://arxiv.org/
https://www.wikipedia.org
https://www.utsouthwestern.edu/newsroom/
https://www.timesofisrael.com/
Built With
wordpress
Try it out
globalisation.gr
|
Confrontation of the Pandemic
|
Method for the confrontation of the pandemic.
|
[]
|
[]
|
['wordpress']
| 17
|
10,024
|
https://devpost.com/software/celafaremoitalia
|
Homepage with the CelafaremoItalia logo and a clear view of Covid-19 situation in Italy
Emergency National Numbers
The map of the Italian Regions
Charts that give people clear information
Useful answers to update citizens
Example of the Virus updated situation in one of the Italian Regions: Campania
Charts that give people clear information about a specific Italian Region: Campania
Example of News section where we fight against fake news by giving verified information coming form safe and reliable sources
Newsletter subscription and useful links such as the one for National Donations
The story of the CelafaremoItalia team
CelafaremoItalia on Google Analytics
Inspiration
The main inspiration came from the desire to help the citizens of our country, giving them clear data, charts and graphs of every single region, in order to inform them about the emergency situation and to inspire them with the idea that:
we can defeat the virus!
What it does
Made for people against fake news
. A monitored and clear vision about the Covid-19, day by day. This project consists of an online platform that provides the collections of data, information and news regarding the virus situation in Italy.
How we built it
First of all
we realized a software
subdivided into two main processes:
Collecting data from different sources.
Converting the collected data into one format.
Then we used
Wordpress.com
and
Elementor
for building up the site. The plugin called
RVM
Map has been very useful to create an interactive Italian map. We used
Google Analytics
for the data dashboard.
For the creation of the Logo we used
Adobe Illustrator
. Instead of the team pictures and for the general design of Celafaremoitalia we used
Photoshop
.
Challenges we ran into
We had trouble
collecting all the data
because at the beginning we didn’t have a suitable instrument for our purpose. We had to
write a program
which would have developed into two main functions: the first one had to gather information, the second one had to reunite, and compress them into one file read by Wordpress. Another challenge was being able to create a
“friendly” graphic style
and a
simple navigation
. For this reason we had to pursue on Colour and Brand study which would evoke the feelings of one clear message: we will make it, Italy! In the end, for the news, we have looked for the
most reliable sources
and
volunteer collaborators
who would have helped us to write and spread them out.
Accomplishments that we're proud of
A thing that we're really proud of is our interactive
Italian map representation
, because it makes our website more user friendly.
When we've seen data, through Google Analytics, we've been surprised. We think that thanks to his usability Celafaremoitalia has managed to
reach thousands of people
around the country and overseas.
In the end, we have a
logo
and everyone likes it.
What we learned
We have learned that cooperation is essential for a successful group project, particularly during a quarantine period, where
smart working is the only way to be together
. While making video conferences using the Zoom platform, we’ve fully understood that great values such as friendship, commitment, and respect between people is even possible through the screen lights.
What's next for CelafaremoItalia
Our next goal is to move on the
phase 2 of the project
:
Working on the development of
new strategies to monitor fake news
.
Hire a
volunteers permanent team
for the News.
Create the
multilingual version
of the website.
Optimize the platform and make it faster for
mobile devices
.
Find further resources to improve CelafaremoItalia
in order to open a Blog Community, a place where people can directly share experiences and information during and after virus life.
What we worked on during this weekend
We worked on building up the website as well as fixing some bugs. We are going to introduce the
verified news section
, where users can find exclusive updates provided by secure and reliable sources.
Built With
css3
elementor
express.js
html5
javascript
node.js
php
siteground
wordpress
zoom
Try it out
www.celafaremoitalia.it
|
CelafaremoItalia
|
Made for people against fake news. A clear vision of the Italian situation, day by day.
|
['Lorenzo Muccioli', 'Raffaele Trapanese', 'Valerio Di Pasquale', 'Ciro Vitale', 'Luigi Bacco']
|
[]
|
['css3', 'elementor', 'express.js', 'html5', 'javascript', 'node.js', 'php', 'siteground', 'wordpress', 'zoom']
| 18
|
10,024
|
https://devpost.com/software/intellihearts
|
COPD machine learning
COPD machine learning
Atrial fibrillation machine learning
COPD machine learning
Respiratory diseases test screenshot
IntelliHearts App screenshot with cardiac analysis response
Cardiac anaysis with heartbeats classification
Inspiration
Starting from the WHO document about needs, requirements and challenge we decide to develop our solution starting from this need:
"Optimize current delivery platforms and develop alternative delivery
platforms for essential health services
: develop remote work solutions,
boost home hospitalization programmes
and rapidly scale up existing e-Health strategies"
- WHO -
Problems:
we observed 3 main problems:
detect patients at risk
screening the whole population
monitor them
People with chronic pulmonary diseases have a higher risk of being infected by covid-19 and have a more negative trend. It is crucial to make a quick diagnosis and monitor these patients.
What it does
With our team competence we create:
Real-time analysis & AI elaboration
of
heart, respiratory diseases
and vital parameters (Single-derivative-ekg, Oxygen saturation, Heart rate, Respiratory rate, Blood pressure).
For the vital parameters we did a long search about the best device for us, we don’t provide hardware
WE USE EXISTING HARDWARE
, reducing the timing for ideation, prototype and create the chain for production. The real improvement is our knowledge on Software and machine learning
MAKING ABLE EVERY COMPATIBLE DEVICE TO WORK WITH OUR SOFTWARE
and scalable up to million of users right in a day.
Pre-triage online, home remote monitoring
, thanks to a test that evaluates symptoms, vital data, risks factor and processes them through a score to classify patients based on their stable, unstable or critical condition
Sharing and visualization
of scores, parameters and AI elaboration with physician and Hospitals.
The anonymous database is very flexible and make use of modern no-sql technologies for securely storing personal data anonymously and share it with physicians and integrate it with hospital and all third parties. Our technology is really flexible and easy to manage letting us able in few days to satisfy every request comes from this kind of organization.
How we built it
Scientifically proven Machine Learning and AI algorithms
:
Here we attach the 2017 position paper.
Essentially the technology used is that presented in that paper. From a technical point of view, the deep learning neural network used in that work was replaced with the
newer ResNet-152 giving a leap of accuracy of 93% in F1-Weighted score for arrhythmias classification and 95% F1-Weighted in atrial fibrillations
(not yet described in that paper).
With IntelliHearts we mainly use sensors related to ppg and ECG, and we are able to detect:
Bradycardia, tachycardia, fusion beats and ventricular and supraventricular ectopic beats.
In the experiment that was conducted we first acquired the signal and cleaned it with statistical techniques (e.g. wavelet) then inter-patient separation was made. Subsequently every single beat is plotted and shown in 2d centered in the peak R.
Consequently we have n beats that can fall into the categories: normal, ectopic, supraventricular and fusion. There are not always the same number of beats within the same class, for this reason we refer to the F1 microscore average.
This is the basis of the paper. From this moment on, the classifier has been changed, as each plotted beat is analyzed by a classifier, which in the paper was a Convnet, but which we have changed and used a ResNet 152 layers. Reaching from 90% of the ConvNet proposed in the paper to
93% of the current ResNet
.
Respiratory diseases detection was conducted in the following way:
We used the data from Paper: Α Respiratory Sound Database for the Development of Automated Classification
Filter noise and remove background sounds
We used several segmentation techniques based on spectrograms of respiratory audio
We used a custom deep learning neural network to predict binary healthy/unhealthy patients using only audio recorded by smartphone
Developed a web service and web page/app to record and show the result.
We use medical algorithms
already used in the wards of infectious diseases or pre-surgery to investigate the general state of the patient, taking into account oxygen saturation, temperature, heart rate, respiratory rate, neurological condition. Thanks to the test we investigate the symptoms, the risk pathologies and we can give indications to the patient about what to do
Shared databases
that could be used by physician for evaluation, motorize and have history of the patient.
Challenges we ran into
VAE/GAN needs lots of data:
the majority of reviewed papers use 70-30 split without using the official test set provided by the challenge. Thus they don't use a inter-patient separation scheme, revealing wrong results.
Find valid datasets, identify the right devices to work with, competition and hackathon dismissed for covid-19.
What we have done during the weekend
During the weekend we have developed the respiratory disease detection with deep learning algorithm, we develop a demo web app and created a temporary web service for breath audio analysis.
We hit the 95% of accuracy to dectect respiratory diseases.
You can test it at
https://159.203.68.29:5000/
do it from your smartphone and follow the instruction on website.
Accomplishments that we're proud
The segmentation works!! For the classification at moment I have 95% accuracy in binary classification (healthy vs pathology) with interpatient separation scheme testing on the official test set provided originally from the paper.
Accomplishments that we're proud in past
Be part and take the graduation for Y-Combinator startup school 2020 (SUS2020) in March with this team;
Exceed the state of the art, percentage of accuracy for COPD and heartbeat classification;
Find the right compatible devices and start to building partnership with hardware supplies and Hospitals
What we learned
Probably managing Audio deep learning as a 1D time series, instead of transforming it in images (as done in the majority of the research reviewed) is effective, but requires a specialized network architecture.
That our team has the skills to turn the emergency around and is fun do hackatlon!
The solution’s impact to the crisis
Doctors will already have all the triage data in their database, this will help giving a quick glance to all their patients’ overall being and evaluate in no time whether to suggest them an in-hospital consultation.
Thanks to our efforts into looking for subjects at risk, we can prevent and propound the doctor who to mostly keep monitored.
We would have a much better healthcare system organization, a newer and quicker doctor-patient online relationship, and more focus on prevention to avoid the aggravation of clinical conditions that could have been treated immediately, beforehand.
A further positive impact falls on patients not affected by covid-19, but with other pathologies. With this type of organization, even non-covid patients will have the right care and attention from doctors.
The Mews score allows to evaluate vital parameters, discriminating stable, unstable or critical clinical conditions and is applicable to a wide spectrum of pathologies.
In conclusion, thanks to IntelliHearts even those suffering from other diseases can constantly monitor their parameters from home and share them with their doctor, allowing a more efficient management of all patients.
The necessities in order to continue the project
Imaging to the have high numbers of visitors on the tests of respiratory disease and Online triage we could need more and stronger servers.
So we need to get funded to pay servers, services, device’s certification and software medical certification, hire additional engineers and physicians. Support medical trials.
The value of our solution after the crisis
According to WHO estimates, 251 million people in the world have chronic obstructive pulmonary disease (COPD) and cardiovascular diseases (CVDs) are the number 1 cause of death globally, taking an estimated 17.9 million lives each year.
Thanks to IntelliHearts we will help patients with chronic obstructive pulmonary, cardiovascular, infectious and Metabolic diseases.
The project was born before the crisis for automatic diagnosis with a smartwatch and AI enabled app. Thus, our solution is ready for the post-crisis era, where people will pay more attention to their health and people understood the value of medical doctors, nurses and health operators, thus will reduce the accesses to family doctors (primary care physician) and hospitals by continue using new technologies (which they were forced to use during the lockdown) and managing everything remotely with remote health platforms.
We estimate that our technology can reduce by 40% hospital costs, playing an important role in prevention more than treat them and following the vision of current medicine.
MEDICAL DISCUSSION
Cardiac injury is a common condition among patients with COVID-19, and it is associated with a higher risk of in-hospital mortality.The findings presented in the research on 416 patients in Wuhan, Shi and colleagues [2], highlighted the need to consider cardiac complication in COVID-19 management. The patients with Covid-19 have respiratory distress and low blood oxygen levels, consequently they have high risk of ischemia or heart attack that compromises myocardial contractility and this situation can cause severe arrhythmia.
Respiratory diseases detection was conducted in the following way: We used the data from paper [1] Filter noise and remove background sounds We used several segmentation techniques based on spectrograms of respiratory audio Segmentation is necessary to understand when the first respiratory cycle starts. We used a custom deep learning neural network to predict binary healthy/unhealthy patients using only audio recorded by smartphone Developed a web service and web page to record and show the result. The accuracy is tested on the official test set in [1]
Why is early detection of COPD important for covid19?
SARS-CoV-2 uses the angiotensin converting enzyme II (ACE-2) as the cellular entry receptor to infect the lower respiratory tract. ACE-2 expression in lower airways is increased in patients with COPD and with current smoking [3] . ACE-2 is expressed in a variety of different tissues including both the upper and lower respiratory tract and myocardium. Importantly, nearly all deaths have occurred in those with significant underlying chronic diseases including COPD, and cardiovascular diseases These findings highlight the importance of increased surveillance of these risk subgroups for prevention and rapid diagnosis of this potentially deadly disease.
References
1- Rocha, B. M., Filos, D., Mendes, L., Vogiatzis, I., Perantoni, E., Kaimakamis, E., ... & Paiva, R. P. (2018). Α respiratory sound database for the development of automated classification. In Precision Medicine Powered by pHealth and Connected Health (pp. 33-37). Springer, Singapore.
2- Shi S, Qin M, Shen B, et al. Association of Cardiac Injury With Mortality in Hospitalized Patients With COVID-19 in Wuhan, China. JAMA Cardiol. Published online March 25, 2020. doi:10.1001/jamacardio.2020.0950
3- ACE-2 Expression in the Small Airway Epithelia of Smokers and COPD Patients: Implications for COVID-19. Janice M Leung, Chen Xi Yang, Anthony Tam, Tawimas Shaipanich, Tillie L Hackett, Gurpreet K Singhera, Delbert R Dorscheid, Don D Sin. Published in European Respiratory Journal doi: 10.1183/13993003.00688-2020
4 - Halpin, D. M., Faner, R., Sibila, O., Badia, J. R., & Agusti, A. (2020). Do chronic respiratory diseases or their treatment affect the risk of SARS-CoV-2 infection?. The Lancet Respiratory Medicine.
What's next for IntelliHearts
05/2020: Relase the platform letting people be able to use Machine learning detection and medical scores.
06/2020: Get the necessary medical certification.
07/2020: Involve hospitals for trials.
08/2020: find partnerships and investors.
Our Links:
Presentation link:
IntelliHearts presentation for EUvsVirus 2020
Respiratory disease test:
https://159.203.68.29:5000/
Video of Cardiac Disease test
Our AI Application that work with one of the compatible Smartwatch, running a cardiac analysis and parameters visualization
Video explain respiratory Disease test
Our AI on respiratory audio
Covid Risk test
(based on Italian Guidelines)
Covid risk test online
GitHub
GitHub Repo
Check out Our website IntelliHearts.com to keep in touch and have more Info
Built With
api
css
firebase
github
html
html5
java
javascript
keras
python
tensorflow
Try it out
159.203.68.29
www.intellihearts.com
www.intellihearts.com
github.com
|
IntelliHearts
|
Use automatized algorithm to early detect Covid-19 complications at Home
|
['Lorenzo Diomeda', 'Selene Colapietra', 'Antonio diomeda']
|
[]
|
['api', 'css', 'firebase', 'github', 'html', 'html5', 'java', 'javascript', 'keras', 'python', 'tensorflow']
| 19
|
10,024
|
https://devpost.com/software/the-virus-limiter-3-0
|
Our certificate in HackTheCrisisIndia
Our Certificate on CivicTechhub
I wanted to find a solution for COVID-19 and I signed up for HackTheCrisisIndia and made it to top 300! Unfortunately, I couldn't make it to top 30.I also got a special mention in The Global Hack. I knew I wasn't gonna give up and I signed up for This Hackathon!
The People who lost their job due to COVID-19 can go to The Virus-Limiter app and apply for a job that is going to be listed by companies who need more people to help stop this pandemic. So a cashier at a restaurant might switch to a COVID-19 volunteer by using our app and contacting a hospital.
I don't have a working model yet.
A very big barrier I have in my way is The Age Barrier. I am only 10 years old and have no experience in coding and all. And I have like people who are 40 going against me!
A very big accomplishment that I think that I have, is that I made it to top 300 of HackTheCrisisIndia.
What I thought in HackTheCrisisIndia was that my idea was just really basic, so I decided to change that This Hackathon. And in my idea in The Global Hack was a little hard to widely spread.
So, right now I am recruiting a team that will help me on my journey to success!
Built With
unified-software-addressval
Try it out
sites.google.com
|
The Virus-Limiter 3.0.
|
The People who lost their job due to COVID-19 can go to The Virus-Limiter app and apply for a job that is going to be listed by companies who need more people to help stop this pandemic.Like hospitals
|
['Rehan Raj', 'Fahim Khan']
|
[]
|
['unified-software-addressval']
| 20
|
10,024
|
https://devpost.com/software/comercialmedapp
|
home page
Business model canvas
Inspiration
Nowadays, with the arrival of the new economic consequences of the coronavirus crisis, our main inspiration raised on how to increase the efficiency to reduce the transaction costs in every European business. Also, we wanted to promote internal business transactions between European companies.
What it does
CommerceMed helps companies to find products that they need to produce here own products, but making sure that those products are from the closest European Companies.
e.g. Imagine a company that manufactures sanitary napkins. Probably it's obtaining the raw material from some Chinese factory. Increasing Europe's internal economy has always been an important mission, but with the economic crisis caused by the coronavirus, it is even more essential. CommerceMed will help this company in the example to contact easily with other companies (in Europe) that produce the raw material that it needs.
How we built it
We build it with a lot of love and with expectations about a good (healthily and economically) future for all us.
Technically front-end and back-end are built with Django, knows as "the Web framework for perfectionists with deadlines". With this, the architecture of the application is an MVT architecture (Model-View-Template). Data is managed with Postgresql. We have used Docker for simplifying and to accelerate the workflow.
Challenges I ran into
Make an application usable enough, for connecting companies.
All the application has been made during the weekend.
What's next for ComercialMedApp
Making the search tool more powerfull in orther to make the search for the users as easy as possible.
Finish a couple of things that due to the lack of time we havent been able to implement, becouse ase we said the application was made entirely this weekend.
Implement directly communication (e.g. via chat).
Make a Mobile APP in orther to make easyer the communication.
Built With
admin-tle
crispy-forms
django
docker
i18n
postgresql
python
Try it out
github.com
commercemed.herokuapp.com
|
CommerceMed
|
A commerce mediator. The objective of the ComerceMed team is to help to increase the internal trade of the European Union, especially, during the Covid-19 crisis..
|
['Alba Lamas', 'horno Palacin', 'Sergi Simón Balcells', 'Joaquim Picó', 'Oriol Alàs']
|
[]
|
['admin-tle', 'crispy-forms', 'django', 'docker', 'i18n', 'postgresql', 'python']
| 21
|
10,025
|
https://devpost.com/software/data-dao
|
Inspiration
DataDAO mission is to allow the pooling of datatokens into a meaningful and valuable dataset who’s value is greater than the sum of it’s parts alone. Laying the foundations to a fairer and more inclusive value distribution in any product or platform, to the members who actually generate it.
It also serves as a demand generating tool — anyone in need for specific data set can source this data from the community, and the community can join forces together to create it.
DataDAO grows Ocean Protocol data marketplace by expanding it from being a peer-to-peer, or one-to-one data marketplace, into a many-to-one data exchange protocol. This is how we can foster demand on OceanMarket!
Compiling together elements from DeFi, decentralized governance and data marketplace, we believe all the pieces that are needed to bring such a product to market exist, and with the right design, technology stack, and crypto economics / incentive mechanism, we can showcase the value such a tool can unlock.
Watch the Demo Videos
What it does
DataDAO utility is similar to the narrative of an AMM (automated market making, like uniswap, bancor, balancer etc…) for liquidity pooling - the value of each isolated individual liquidity is not valuable in it’s own, but the ability of them to pool together unlock a tremendous amount of value in the form of large token exchange liquidity pools.
Same applies for DataDAO, instead of Capital → Data. - the pooling of fragmented data together can create a valuable dataset.
Phase 1 - Dataset request
Data Requestor ask for information (i.e - 500 twitter threads and discussion around ocean and the web3 data economy, json format ) and stake 50$ DAI.
Upon submission of this request, a DataDAO ‘OL72’ is opened - a DAO instance is created on DAOstack.
Phase 2 - DataDAO contribution phase
Contributors provide links for this request. Effectively they are minting and sending datatoken into the DataDao vault. In return the DAO sends DataPool tokens which represent the contributor financial stake in the Datapool, and REP which represent voting rights in the DAO that governs the combined Dataset
Phase 3 - the dataset is ready
Data requester with a metamask authentication buy access to the combined dataset, and receive all of the Datatokens held in the DAO.
(Note - In future stages there will be the possibility to appoint a reviewer / verifier / keeper that will serve as a validation step to ensure quality of data, propose which contributions are valid and which are not, and add to the confidence level of the data requestor. Further info on this will be published later on)
Phase 4 - Data providers redeem their holding from the DAO and can now get their proportional claim on the DAO treasury against sending their DataPool tokens and slashing their REP.
Note - in future stages DAO members can propose and vote on decisions that relate to the dataDAO, such as using the proceeds to further grow the dataset, bring more buyers or improve the quality of the data through proposals like data clean-up, image annotation etc...
How I built it
Frontend build using React + Redux
Smart contracts implemented using Truffle + OpenZeppelin + Drizzle
Interaction with the Ethereum blockchain using Web3.js, Notify.js and Web3Modal
Interaction with OceanProtocol using ocean.js and @oceanprotocol/react components
DAO management using DAOStack Arc framework
DAO metadata management using IPFS
Integration with Filecoin using Textile Hub, so people can use a permanent+cached decentralized storage solution for uploading Individual data contributions
Challenges I ran into
How to make sure a contributor is not revoking the data from the url where he originally stored it How to structure the DataPool token / MasterDataToken in the most efficient way, as each datatoken contributed by a user is a unique token
Accomplishments that I'm proud of
Building a structure that allows a fully permission - less structure in the process of creating a combined dataset from multiple individual contributions. The structure chosen ensure that at any given point in time, no member is able to hold the entire dataset, and the only point in time it does happen is when a buyer pay to buy the combined dataset
Lot’s of interest from the community. We kepts the project in stealth until submission, so our discord and telegram channels are just starting, but an example reference that indicates there is a clear path to ensure large-scale adoption:
https://twitter.com/dennisonbertram/status/1341493651143352321?s=28
What I learned
By doing the research as part of building this POC, we were amazed by the number of applications that can use DataDAO concept - AI, ML, curation lists, investment
Used ERC-998 for deploying MasterDataToken
What's next for Data DAO
Use ERC998+ERC1155 standard on our MasterDataToken for improving user experience
Enable a more fine-grained access control policy for individual data contributions
Selection of a validator as a function to increase confidence in a DataDAO dataset and reduce spamming
Include staking function – for a dataDAO initiator and for contributors
A DataDAO governance – iterate and deploy the dataDAO governance scheme
Built With
blocknative
daostack
filecoin
oceanprotocol
react
redux
solidity
textile
truffle
typescript
web3js
Try it out
datadao.io
app.datadao.io
github.com
medium.com
discord.gg
twitter.com
|
Data DAO: Enabling a Collective-owned dataset economy
|
Merging decentralised governance and incentive mechanism into a permissionless data marketplace!
|
['Santiago Gonzalez Toral', 'Gary Latta', 'Lior Goldenberg']
|
['First Place', 'Protocol Labs (Filecoin) Bonus Prize', 'DAOstack Bonus Prize']
|
['blocknative', 'daostack', 'filecoin', 'oceanprotocol', 'react', 'redux', 'solidity', 'textile', 'truffle', 'typescript', 'web3js']
| 0
|
10,025
|
https://devpost.com/software/videowiki
|
GIF
A collaborative platform for learners and educators to secure learning continuity in times of restrictions and beyond.
Table of Contents
About Project
Problem -
How do we ensure learning continuity?
Solution -
Online and immersive digitalization of Content, Curriculum, Coaching, and Classroom.
Prototype
& Technology -
Rapidly developed, working, and ready for use.
Vision -
Project goals and alignment with the SDGs
Strategy -
Steps to build a sustainable and scalable business
About Market
Market Size -
Size of impact and response
Market Shift -
Traditional to Transformed Education
About Impact
Next steps
About Us
Team & Contacts
Acknowledgements
About Project
Problem:
Innovation in the field of education seems to be lagging behind In a fast-changing digital environment with many disruptions and distractions. Technology has enabled us to break from a centuries-old model of blackboard training and the pandemic has moved us online. People have moved almost instantly online for imparting education, however, the transformation and creation of content for digital channels is the biggest challenge of the industry now.
Because online digital mediums are heavily centralized, education has various different meanings in different government rules and the world of knowledge is bifurcated with censorship, control, and fabricated (fake) information. This is the problem of a lack of trusted authorship.
Prototype
Ensuring learning continuity with VideoWiki
Description of the project:
-- VideoWiki is a platform for publishing video content that can be automatically generated using AI, from text-based content. We aim to be a fully decentralized and censorship-resistant video publishing platform. An Open, Collaboratory Content Editing Platform that enables rapid Creation, Modification, Protection, and Monetization of Immersive Content.
-- As a Technology (Utility Token) it can enable use-cases of education, corporate L&D, training, news and journalism, marketing, research, science, and more. It is purposefully built with the aim of decentralization for transparent assignment and share of ownership between collaborators.
Vision: The problem we are solving
In recent days we have seen the rise of various online platforms for online teaching and conferencing with online teaching sessions, however, there is little done towards creating/converting/publishing educational content for the online distribution and immersive learning. Even the existing content platforms have the following issues:
- They are centralized and heavily censored
- There is no scope of more than one author/creator/teacher contributing to the same content
- Once published, it’s difficult to modify the content or publish new versions of it as the subject/technology develops
- Often creator has to master skills like video editing to make the visual content engaging and immersive
Vision
To ensure learning continuity for all.
Standardized education and access to all with equal opportunities to educational processes.
Content
Curriculums
Coaching
Classroom
- [x] This project aims in solving all of these issues by creating a platform where engaging video content is created using AI and a versioning system is maintained using blockchain.
- [x] The videos are published on our website (our fork of OCEAN MarketPlace)
Work completed so far
- [x] The AI assisted content creation module is created.
- [x] A fully functional Text to Video converter with auto-script (scenes) creation and video footage suggestions
- [x] Blockchain Version control framework strategized
- [x] Video Classroom are live and operational in MVP
Prototype & Technology:
Rapidly developed, working, and ready for use.
Goals:
Project goals and alignment with the SDGs
The importance of education is highlighted in
SDG 4
(“
Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all
”). This goal encompasses various dimensions that are critical to supporting young workers’ transition into the labor market and career progression.
Target 4.3:
“
By 2030, ensure equal access for all women and men to affordable quality technical, vocational and tertiary education, including university.
”
Target 4.4:
“
By 2030, substantially increase the number of youth and adults who have relevant skills, including technical and vocational skills, for employment, decent jobs and entrepreneurship.
”
Future project roadmap
○ Video Editor
§ Single teacher use cases
§ Class records and single-user edits
§ Publishing and Curriculums
○ Collaboration and re-edits Jan 2021
○ Classroom records and edits Feb 2021
○ Blockchain Testnet - June 2021
§ Wallet Issuance and Teaching Credits
§ Version control released for test-net
Market Shift:
Traditional to Transformed Education
About Impact
Learner Experience
&
Teacher Experience
Mission and Guidelines
Next Steps
Even after the crisis, the solution will be a DIY help to teachers to automatically convert their lectures and notes to digital content with simple tools and minimum tech knowledge. An immersive approach for all regardless of their income level, technology level, or age.
Immersive and applied training is a new concept that will attract students to gain knowledge.
A plug-n-play integration with universities with easy adoption.
Digital content generation can also be expanded to other applications like memos, university announcements, news, etc.
Teachers are technically challenged and cannot generate their own digital content.
The tool is extremely simple and has been curated by keeping customer satisfaction, ease of use as major priorities.
We have hosted a mock prototype on
http://videowiki.pt/
, in this link the working is explained in the video.
Team
Natalia Rheskava
• Role: Co-Founder/CIO
• Relevant Credentials:
○ LinkedIn:
https://www.linkedin.com/in/nataliia-rzhevska-486a791a8/
• Background:
○ PhD, Grygoriy Scovoroda University in Pereiaslav
○ Media Relations & International Medical Community (IMC) Lead
○ HundrED Ambassador
○ World Economic Forum Digital Member
Shivam Dhawan
• Role: Co-Founder/CTO
• Relevant Credentials:
○ LinedIn:
https://www.linkedin.com/in/shivamdhawan/
• Background:
○ Founder @GetBoarded
○ Founder Chief Strategist @Arbunize
○ Web Analytics Manager @Metriplica
○ BI Team Lead @Annalect
○ Business Systems Analyst @MetLife
○ Web Analytics Application Developer & Consultant @CTS Argentina SRL
Puneet Gupta:
• Role: AI | Backend Developer
• Relevant Credentials:
○ LinkedIn:
https://www.linkedin.com/in/puneet-gupta-5415a4153/
○ Github:
https://github.com/SabiNinza
○ GetBoarded:
https://ci.getboarded.tech/en/e/puneet-gupta-1605745717-5127
• Background:
○ AI and Django Developer @Arbunize
○ Summer Intern and Campus Ambassador @Coding Blocks
Chinmay Jain
• Role: Frontend Developer
• Relevant Credentials:
○ LinkedIn:
https://www.linkedin.com/in/chinmay-jain-3b499a181/
○ Github:
https://github.com/chinmay81098
○ GetBoarded:
https://ci.getboarded.tech/en/cv/Chinmay-Jain-1605840708-5130
• Background:
○ Software Developer @Conic Skill
○ Frontend Developer @TDG Labs
○ Intern @NTPC Limited
Bhaskar Dutta
• Role: Blockchain Developer
• Relevant Credentials:
○ LinkedIn:
https://www.linkedin.com/in/bhaskar-dutta-6b23b616a/
○ Github:
https://github.com/BhaskarDutta2209
○ GetBoarded:
https://ci.getboarded.tech/en/e/Bhaskar-Dutta-1599901871-4539
• Background: Blockchain Developer @FixNIx
Jyoti Sing
• Role: User Experience Designer
• Relevant Credentials:
○ LinkedIn:
https://www.linkedin.com/in/jyotising/
○ Github:
https://github.com/jyotising
• Background:User Interface Designer
Built With
ai
ai-model-using-python-&-django-front-end-using-vue-stack-ethereum-as-public-blockchain-network-javascript-libraries-of-daostack-and-ocean-protocol-code-versioning-control-gitlab-code-editor-visual-studio
dao
djang
django
ethereum
git
gitlab
javascript
ocean
pycharm
python
solidity
vue
Try it out
VideoWiki.pt
marvelapp.com
miro.com
github.com
|
VideoWiki (TCH protocol)
|
Project VideoWiki is an Open Collaboratory Content Editing Platform that enables rapid Creation, Modification, Protection, and Monetization of Immersive Content driven by a decentralized community.
|
['Bhaskar Dutta', 'Nataliia Rzhevska', 'Shivam Dhawan', 'Chinmay Jain', 'Puneet Gupta']
|
['Second Place', 'Community Choice']
|
['ai', 'ai-model-using-python-&-django-front-end-using-vue-stack-ethereum-as-public-blockchain-network-javascript-libraries-of-daostack-and-ocean-protocol-code-versioning-control-gitlab-code-editor-visual-studio', 'dao', 'djang', 'django', 'ethereum', 'git', 'gitlab', 'javascript', 'ocean', 'pycharm', 'python', 'solidity', 'vue']
| 1
|
10,025
|
https://devpost.com/software/moonjelly
|
The mint page in MoonJelly.
Searching the Ocean Market in MoonJelly.
Alerts in MoonJelly.
Bookmarks in MoonJelly.
The Slate (Filecoin) integration in MoonJelly.
Inspiration
Last year's Ocean hackathon featured a project called Jellyfish, which was a browser extension that let the user publish and search the Ocean commons. With the advent of datatokens and the Ocean market, we recognized that this abandoned project desperately needed to be updated. By creating a repository _ designed _ to be extended by other developers, and recreating the simplicity of the original project's prototype, MoonJelly has been shaped to become a powerful tool for members of the Ocean ecosystem.
What It Does
When designing MoonJelly, we focused on three main ideas: filling the ocean, browsing the ocean, and developer extendibility. To accomplish this, we created core functionality:
Mint Panel to upload/publish datasets & price datatokens.
Market Panel (and a context menu button) to search the Ocean market for datatokens.
An Alert system that regularly searches the Ocean market for new datasets based on your keywords, and notifies you of any high-quality sets of interest.
Wallets & Bookmarks to keep track of which datasets may be of interest.
Modules of functionality that can be enabled and disabled by the user.
The modules feature is where the extendibility of MoonJelly shines. Developers can view MoonJelly's documentation and create modules by contributing to the GitHub repository. Modules can hook into the functionality of the rest of the project, such as modifying inputs of the Mint or Market pages, or deciding which panel the extension should immediately go to on open.
To provide an example of the modules feature, we created 3 modules.
Slate (Filecoin) Integration
The Slate Integration module reduces interoperability friction between the Ocean and Filecoin ecosystems by letting users easily publish datasets hosted on Slate. We have created an
extra video
to focus on the module's features. The module (requires a Slate API key) will:
Add a button to the
Slate interface
that sends the details of an uploaded object and adds it to MoonJelly's Mint Panel for easy publishing of datasets.
Add a new Slate Integration panel.
Allows users to browse the contents of their slates from the extension's interface. Provides links to the slates, the objects in the slates, and allows users to send the details of an uploaded object to MoonJelly's Mint Panel.
1inch Exchange Integration
The 1inch Exchange module utilizes the 1inch API and Ocean's Aquarius API to monitor the price fluctuations of datatokens based on any cryptocurrency on 1inch Exchange. We have created an
extra video
to focus on the 1inch Exchange features. The module will:
Display the price of bookmarked datatokens based on whichever cryptocurrency you choose.
Allow the user to modularly create alerts for when the price of a datatoken relative to any 1inch supported cryptocurrency reaches a certain threshold.
Easily direct the user to the 1inch exchange.
Ocean Pools for MoonJelly
The Ocean Pools module allows users to view the statistics of their bookmarked pools. We have created an
extra video
to focus on the features. The module will:
Display pie charts and line graphs to represent pool data.
Allow users to view all the information of pools normally found on the Ocean Market.
How We Built It
Similar to the original Jellyfish, the MoonJelly extension is based off of React. By importing Ocean's React library, the extension was able to make use of Ocean's functionality within the extension's panel. Other functionality, such as that of the Slate module, used other external REST APIs.
While we took many of the design choices from the original Jellyfish repository, nearly 100% of the functional code had to be rewritten since the original project dealt with the ocean commons instead of the new datatoken-powered market. We also converted the project from scss to css, as we were more familiar with it, and we know that developers can have issues with installing the scss library.
Challenges We Ran Into
New technologies are always difficult to work with because there isn't as high of a level of support as systems that have many years of developers working with them. That being said, the developers on the Ocean discord channel (shoutout for Alex) and especially the Slate slack (shoutout to cake) were quite helpful at times.
Accomplishments That We're Proud Of
While we lamented the difficulty of working with new technologies in the previous section, it's also a point of pride. Creating this extension with the functionality that it provides would not be as impressive if it was not hard. We are proud of our product, and we think that there will be people who will seriously use this product. We tried to build it for
long-term support
, not just a hackathon.
Open-Source Contributions
If you like the direction of the project and are a developer that wants to contribute, then by all means do so! If you think that there's react components that should be updated/added to the project, or if you think that the css needs some fixing up, then feel free to make a branch on the
GitHub repository
.
If you want to add your own new functionality, then you should make a module. Their functionality must be togglable. We're looking for new modules for other integrations, like for Google Drive or Kaggle.
What's Next For MoonJelly
There were a lot of ideas that we had to scrap for sake of time, funding, or lack of experience, we hope to continue adding features with support from the community.
In regards to Filecoin, we are also considering expanding our integration by allowing users to use their own buckets and have MoonJelly manage their Ocean publishing.
In general, we hope to add modules like that of Slate to help reduce the difficulty of flow between cloud storage services and Ocean Protocol, as well as improving the built-in algorithms for alert-based market curation. Unfortunately, some modules like Google Drive require understanding of the Google APIs as well funding for long-term support, so we hope that the community will support the continuation of the project!
Our next focus is making allowing the extension to dynamically switch networks & improving the modules development workflow. We have open project boards on Github
here
, as well as open issues
here
.
Built With
css
javascript
ocean-protocol
react
slate
Try it out
github.com
|
MoonJelly | The Next Ocean Chrome Extension
|
The next open-source chrome extension for easy curation & minting to the ocean market.
|
['Kevin Xu', 'Jeremy Boetticher']
|
['Third Place', '1inch Exchange Bonus Prize']
|
['css', 'javascript', 'ocean-protocol', 'react', 'slate']
| 2
|
10,025
|
https://devpost.com/software/dataunion-app
|
Every effort counts!
Contribution flow
Usage of the data token
Solution in the wild
The team
Upload, describe and tag images
Statistics Dashboard
Dataset on the Ocean marketplace
Inspiration
Data is the new oil in the 21st century and big cooperations have already realised that to their advantage. In this project we are giving the power and profit of data back to the people that create it. By using a reward system based on datatokens the contributors become immediate shareholders in data unions.
Our first Data Vault is for images. It is available on the Ocean Protocol marketplace. The contributors upload them, annotate them and then these contributions are verified. They are rewarded with datatokens so they become shareholders in the dataset. This combination of mechanics creates an intrinsic motivation to contribute positively.
We want to give everyone the ability to use their data for a better future and their own profit.
What does our dataset do and how does it work?
This dataset represents a collection of images and annotations. The data is uploaded, annotated and verified by the dataset shareholders in exchange for datatoken rewards.
Participants invest their time to gain shares in the dataset and improve its quality.
The goal of this dataset is to be used for AI training.
DataUnion.app will run Data Bounties that are looking for specific data and give extra rewards. The reward distributions will be in intervals and not directly for now but these distributions will be announced on our webpage.
The long term goal of this dataset is to contain millions of crowdsource verified and annotated images. DataUnion.app will also facilitate the training of algorithms. DataUnion.app algorithms will be available via the Ocean Marketplace and we will do an airdrop of 10-20% of the datatokens to the holders of the corresponding vault. We are aiming to create the option of Universal Data Income. Especially for countries with low average salaries compared to e.g. Europe.
As an example we are working on a trash detection algorithm with
Project.BB
right now and collecting data for that use case. When the algorithm will be published the contributors of these images will get datatokens in the initial pool of the algorithm.
Login with your Metamask Ethereum wallet - this is your ID, we don't care who you are but that you contribute
Upload images via our website - of course don't upload any problematic data and we use mechanisms to stop fraud
Annotate images via our website - there will be examples and tutorials
Verify images via our website - not your own and verification will be provided from multiple parties before acceptance
Claim your rewards when you gathered enough to justify the Gas costs to send out the tokens
Tokenomics
We will mint more tokens to reward contributors but all of these events as well as the sale of pool equity and the spendings will be documented on our website. We want to be as transparent as possible as we want to strengthen the trust of the community into the project. In the long term we want to migrate to a DAO in the style of the OceanDAO to facilitate longest term growth.
There will be Data Bounties that are looking for specific data and will give extra rewards that are sponsored by the the bounty creators.
What did we accomplish in the hackathon?
Before the hackathon there was only an idea and a vision but no code and no team.
After the hackathon we are a team that build a lot of things:
Community of over 100 people in Telegram and Discord
The code that allows the login via Metamask and the upload of images as well as a Dashboard
Website for the project
First pitch sessions with potential customers and investors
Strategic team that is creating a Whitepaper
Dataset on the marketplace
Pitch deck
Roadmap
We are planning our roadmap in many different directions:
Upload, verification and annotation mechanisms
Create a mobile application that includes these mechanisms
Add other data vaults e.g. text, sound, and 3D objects
Increase decentralisation of our solution by moving the control to smart contracts
Allow addition of data while it resides on different storage
Algorithm training and sales via Ocean Marketplace - have the data providers become shareholders of that as well
Recruit more people and facilitate development via grants
Community
Please join our community on
Telegram
, on
Discord
and follow us on
Twitter
.
Built With
docker
flask
ocean
python
react.js
Try it out
dataunion.app
github.com
|
DataUnion.app
|
DataUnion.app is giving the power and profit of data back to the people that create it. By using a reward system based on datatokens the contributors become immediate shareholders in data unions.
|
['Akshay Patel', 'Nuno Brito Lopes', 'Sarah Kay', 'Ekpenyong Okpo', 'Robin Lehmann']
|
['Honorable Mention']
|
['docker', 'flask', 'ocean', 'python', 'react.js']
| 3
|
10,025
|
https://devpost.com/software/tokenized-power-balancing
|
EnergyWeb meets Ocean Market
I-REC devices
Data token pools on Ocean Market
Inspiration
We aim for a decarbonized energy future and we are inspired by the Energy Web Foundation's statement on their website: "Energy Web is accelerating a low-carbon, customer-centric electricity system by enabling any energy asset owned by any customer to participate in any energy market."
As we enter the era of the data economy we see a lot of innovative solutions built on decentralized platforms like the Energy Web. Ocean protocol is first mover in this field by tokenizing data assets and building service portfolios around datasets like compute-to-data and automated market making using the Balancer protocol.
Our solution is based on the premise that we can bridge both protocols for the sake of strengthening either one of them. The Energy Web is building trust, leveraging verifiable certificates of renewable energy production using an SSI infrastructure whereas Ocean focuses on data assets at the center of their value proposition.
What it does
An excellent use case for the proposed bridge is the balancing of power, according to natural flows of electricity. You need to consume power the moment it is generated, therefore the energy grid is architected around energy planning partners. They have the responsibility for balancing consumption and production of power at each hour of the day. Renewable energy sources are quite challenging here because of the intermittency of power generation. To aim for decarbonization we need a near-realtime power production forecast. Needless to say, the availability of reliable (metered) data is key to this.
For this purpose we are going to use the trust network of EnergyWeb to identify renewable energy producing devices and leverage the data infrastructure of Ocean to curate and verify production data of these devices. A simulation model shows the effects of this integration.
How we built it
The solution is a 3-step process:
Register producing devices in EnergyWeb and collect metered data
Peg the device to an Ocean Market datatoken pool having the metered data as the underlying dataset
Signal device behavior by Stakers, Auditors and Optimizers using the datatoken pool
ad 1.) we cloned the EW-DOS
origin repo
and managed to get a dashboard with devices. Unfortunately to register a device ourselves, you need to have undergone some formal approval process so that's too inconvenient for this submission. However, we are able to simulate having a device registered and attach metered data to it, therefore we used the
data.csv
file of the solar-simulator package as an example.
ad 2.) Building upon the solar-simulator package, we used the
i-rec registered devices
to add a dropdown field in the Ocean Market front-end populated with these devices in order to have an EnergyWeb registered device use their metered data as a proxy for the datatoken pool. Selecting a device will populate all meta-data fields of the Publishing form, having a basic integration between EnergyWeb and Ocean Market.
ad 3.) This is where the actual Tokenized Power Balancing job is done. Once we have a datatoken pool with metered data as an underlying dataset, stakeholders are going to use it to add value.
Stakers
signal curation by investigating the behavior of the power producing device as inferred by EnergyWeb EACs (and the claims thereof), but also by relying on other stakeholders like
Auditors
verifying metered data with sell orders in EnergyWeb Exchange and
Optimizers
making power production forecasts based on this metered data and possibly other datatoken pools having weather history and forecast datasets. For a detailed interplay between these stakeholders, see
our documentation
.
The output of 3. is accomplished by using an energyweb branch of the
tokenspice2 simulation model
looking primarily at the effects of the Optimizer.
Challenges we ran into
Energy Web
EW-DOS toolkit
is not as friendly as we hoped for. In order to get new devices registered, we could not find out how to do that easily without getting stuck in some accreditation process. So we have to assume we can register a new device and call the Ocean API to publish a data token pool on the fling.
Also,
tokenspice2
is still a WIP. We had a lot of issues getting it up and running and still we ran into trouble with a new setup of the SimState. Fortunaltely we did manage to code up the Energy Web agents as stakeholders into the simulation. The coding up of the EW Staker agents is then up to the strategy but alas, no time left to really have some implementation running.
Accomplishments that we're proud of
The idea is solid and reasonably straightforward to implement once we get all the bugs out of
tokenspice2
.
Nice thing about data token pools is that we can have a pool for both the EWPublisherAgent (device pegged to Ocean) with an
underlying dataset
and a pool for the EWOptimizerAgent with the
forecasts
as an underlying dataset.
Both datasets have a static URL (S3 bucket) and are updated every hour through a
crontab
entry for the sake of the simulation. We can hook up these sets again for real implementation in Ocean Market pools, updated with real-time and verified (metered) data.
What we learned
Integrating 2 innovative implementations is hard...
What's next for Tokenized Power Balancing
Update the tokenspice2 model to really have some Energy Web simulations going on.
Port tokenspice2 to cadCAD to have more Optimizer policies in play
Get in touch with Energy Web engineers to work on the EW-DOS
Built With
oceanmarket
python
tokenspice
Try it out
github.com
|
Tokenized Power Balancing
|
Balancing power within a microgrid of consumers and producers using as many renewable energy sources as possible. Peg power devices to Ocean Data Markets and curate according to power behavior.
|
['Marc Minnee', 'Shawn Anderson']
|
['Honorable Mention', 'Energy Web Foundation (EWF) Bonus Prize']
|
['oceanmarket', 'python', 'tokenspice']
| 4
|
10,025
|
https://devpost.com/software/dounty-a-data-bounty-marketplace
|
logo
homepage
bounty details page
Inspiration
Data bounty platforms are apps where data consumers can request data they need and set the price they are wiling to pay for it. This is totally opposite to the value proposition of traditional data marketplaces. By putting a bounty for. a given task, users can save efforts to collect needed data from various sources. Considering that everything is public and available on the internet. This would easily take days if not weeks. No to forget, there is definitely an opportunity cost in terms of time and effort.
What it does
Dounty is a data bounty marketplace where bounty poster can post bounty with a reward in OCEAN tokens. Bounty Workers can then fulfil this bounty by publishing bounty work onto this bounty dapp built on Ocean Protocol and asking Bounty Posters to purchase their work. Once Poster consumes some worker's work, they pay them pre-agreed bounty reward in OCEAN.
What's next for Dounty - a data bounty marketplace
1) Add support for payment via other coins
2) Add support for 3box profiles
Built With
node.js
ocean.js
oceanprotocol
react
Try it out
dounty.xyz
github.com
|
Dounty - a data bounty marketplace
|
Dounty is a data bounty marketplace where bounty poster can post bounty with a reward in OCEAN tokens. Bounty Workers fulfil this bounty by publishing bounty on Ocean Protocol and poster consumes it.
|
['Kunal Damedhar']
|
['Honorable Mention', 'Best data bounty app - Special Project Bonus Prize', 'Best marketplace app with novel curation - Special Project Bonus Prize']
|
['node.js', 'ocean.js', 'oceanprotocol', 'react']
| 5
|
10,025
|
https://devpost.com/software/reprice-data-pools
|
Inspiration
Ocean Protocol has delivered awesome way not only to monetise data but also allow community to stake on that data via data pools (Balancer AMM). Only problem here is popular data pools have attracted a lot of staking. As a result, datatoken prices are going through roofs. This generally shows great interest by community in participation of data economy but this also comes with a disadvantage of no one consuming data at such inflated prices. Thereby, bringing marketplace model to a halt.
What it does
This dapp is designed to solve the above problem. It does one thing and does it well - "allow datatoken owners to reduce price of their datatoken in data pools without diluting Liquidity Provider's pool shares". This creates a win-win scenario where community can keep staking without worrying about datatoken price inflation. And consumers can buy data they want at the nominal rates.
Built With
javascript
node.js
oceanprotocol
react
web3
Try it out
github.com
priceyourpool.xyz
|
Re-price Your Datatokens in Pools
|
Data consumption only happens at proper datatoken prices. Use this app, to re-price your datatoken to lower value whenever price increases drastically without diluting LP pool shares.
|
['Calvin "crypto" King']
|
['Balancer Bonus Prize']
|
['javascript', 'node.js', 'oceanprotocol', 'react', 'web3']
| 6
|
10,025
|
https://devpost.com/software/streamr-streams-ocean-market
|
Start stream publishing by click the SELL button
Fill out the form and click Publish
The form for data set infos
Publishing is done
Published stream snapshot on Ocean Market
Inspiration
To make it as easy as possible for Streamr users to publish streams on Ocean Marketplace, I decided to build a Chromium extension. Streamr users can publish their streams directly in the Streamr Core application without using other websites or services. The extension for Chromium browsers is seamlessly integrated into the look & feel of the Streamr Core app.
What it does
The browser extension injects the functionality and design for stream publishing directly into the Streamr Core app. This is how the publishing function feels natively implemented.
Challenges
The visual and technical integration into the Streamr Core app was a major challenge.
During the development the focus was on the implementation of a bridge between Streamr Network, Sia and Ocean Protocol. The publishing process of a Streamr stream explained in three single steps:
Extract static content from a selected Streamr stream
Upload the stream snapshot to Skynet (Sia), get an encrypted file url
Use Ocean Protocol to publish the data set on Ocean Market
Accomplishments that I'm proud of
Visually and technically matches the Streamr Core app
Publishing process status messages
TypeScripted source code
Very easy to use
What I learned
How the Ocean Protocol is structured and how
ocean.js
should be used. The community on Discord is great.
And of course I got to know about Streamr Network and the Streamr Client. I even created and published some streams, e.g.
Ethereum Gas Prices
.
What's next
The following features are planned for one of the following releases:
Generate a sample file
Time range (timestamp - timestamp) for stream snapshots
Consideration of multiple partitions for streams
Prefill form input fields with stream infos
Built With
chrome
css
javascript
ocean.js
streamr-client
typescript
Try it out
github.com
|
Streamr ➡️ Ocean Market
|
Publish and monetize your Streamr Streams on Ocean Market. Directly from the Streamr Core app.
|
[]
|
['Streamr Bonus Prize']
|
['chrome', 'css', 'javascript', 'ocean.js', 'streamr-client', 'typescript']
| 7
|
10,025
|
https://devpost.com/software/dolphyn
|
Wallet Screen
In case you don't have enough LOVDOL-69 token
Import private key
Holder's transactions
Token list
Top holders
Dolphyn
ERC-20 token's holder tracking application.
Inspiration
We see Datatoken to be an excellent development of Ocean in bringing defi strengths into the data economy. However, we wonder if a tool to model transactions for datatoken is available. It is easier for investors or even those who want a data set for the training model. And that is where
Dolphyn
was born.
What it does
As we introduced, in the first version, Dolphyn will help users to view information about the datatoken - The large holders (whale), transactions of that datatoken at the present time, transactions in and out of an address. However, in order to use the above facilities, users need to own at least 1 Lovely Dolphin Token - LOVDOL-69 (Radar Token).
Moreover, this data is also stored for traders who can research the investment trend of the community. That means you Just own 1 Lovely Dolphin Token, you can access all data via:
https://market.oceanprotocol.com/asset/did:op:33Fc2c1aD7abf1f3491722836d9D18784F85d3A2
How I built it
Transaction data: We don't have the budget to run a full node, so we used InfluxDB to be able to store transaction data by collecting data from Etherscan.
User Interface: For convenience can help users to update their data. We have been working towards mobile development (mobile-first) - and It is also easy to upgrade the version after owning more Radar tokens, users can receive notifications when owner their data migration.
Challenges I encountered
I think the first is a matter of time and manpower, we know this contest is a bit late, and there is not enough manpower to build a complete application.
We wanted to build more with many charts and graphs, the web interface, but time couldn't allow for all.
Accomplishments that I'm proud of
We are confident that this is a very practical application with the datatoken ecosystem and clear use cases we have built.
The last thing is the spirit of work, we worked non-stop until the final hours of the competition.
What I learned
We learned more about the data economy that is being built by the ocean, and also learned how to collaborate on teamwork from idea proposal to complete application development.
What's next for Dolphyn
Firstly, we will refine the web interface so that users have more choices when using our application.
Secondly, we will improve the display of data holding charts, such as bubble charts. Notifying users when a large amount of a deal has occurred, or a transaction comes from top holders.
Lastly, we will provide premium usage packages for accounts by owning multiple Radar tokens. In addition, Radar token holders will be given priority to use the new features of the product.
Built With
flask
flutter
Try it out
github.com
|
Dolphyn
|
ERC20 Holder Tracking App
|
['Dnah Berberin']
|
['Best datatoken analytics app - Special Project Bonus Prize']
|
['flask', 'flutter']
| 8
|
10,025
|
https://devpost.com/software/numerai-signals-marketplace
|
As I browsed Numerai Signals and submitted some signals, I realised there might be additional ways to make money from the signals while also providing this great source of information to all the data scientists in the world using the Ocean protocol. So I created a dedicated marketplace for Numerai Signals. There you can publish your signals and sell them for a price of your choosing or create an automated market pool.
I also created a small website modification for signals dot numerai which adds a button to sell signals and redirect to the ocean marketplace!
Built With
numerai
ocean
Try it out
github.com
|
Numerai Signals Marketplace
|
Sell your Numerai signals on a dedicated Ocean marketplace
|
[]
|
['Best integration into Numerai Signals data service - Special Project Bonus Prize']
|
['numerai', 'ocean']
| 9
|
10,025
|
https://devpost.com/software/oceancaller
|
OceanCaller
Solution
Inspiration
Centralized apps like truecaller help us get the owner of unknown mobile numbers .They do it by collecting contacts of people who have there apps installed , and then the apps serves the information to the one who requires this information
But in this case the data published gets no benefit as well as the collected data gets shared with everyone hence hampering privacy
How I built it
We have utilized decentralized data exchange using Ocean protocol and Filecoin to solve this problem
User will publish his/her contacts which include name and phone number to filecoin . This contacts dataset will then be published to ocean protocol .We will now just persist the mobile numbers but not the names with the corresponding DID on our database
Now whenever a user comes and enters a mobile number whose owner is to be fetched then we will fetch the DID of the corresponding mobile number from our database, we then will buy the dataset from Ocean protocol using the user's wallet (buyers wallet) and then fetch the owners mobile number and name from the dataset with is obtained using Ocean protocol and then we will return the result.
What's next for OceanCaller
Integrating compute service for ocean caller to fetch the mobile number.
Built With
ethereum
filecoin
mongodb
node.js
ocean
python
react-native
Try it out
github.com
expo.io
|
OceanCaller
|
Share Contacts Decentralised Way
|
['Arpit Srivastava', 'Aniket Dixit', 'Aman Raj']
|
['Best integration into phone data as a data service - Special Project Bonus Prize']
|
['ethereum', 'filecoin', 'mongodb', 'node.js', 'ocean', 'python', 'react-native']
| 10
|
10,025
|
https://devpost.com/software/the-predictors-app
|
could not afford gas costs $97.07
How we work with Ocean dataTokens
Inspiration
Predictions markets are growing in popularity and crowdsourced data can provide wisdom and insights to people, businesses, organizations, with predictions on markets, assets, events, really anything you want to know from a collective consciences.
What it does
With The Predictors APP you can win prizes and earn Ocean token rewards from making correct predictions. Token rewards can be traded for cash an other tokens. As you level up your accuracy and make more correct predictions on the app your rewards ratio will increase. Train your abilities and see if you can become a Master Predictor. Or join just to ask for predictions, with The Predictors APP you also can get predictions from the crowd. Find out what others are predicting is going to happen in the marketplace or other future outcomes. Crowdsource the data you need to make better decisions with The Predictor App.
How we built it
We are using Gravity Forms with Zapier APIs to connect prediction data in Google sheets. We wanted to connect it to a Ocean dataToken but it costs $18.79 to create the token
https://etherscan.io/tx/0x9ad034120946d833fc7532a20b71af578c66837f304242f0adf09f9cbd030b61
and I was unaware it was going to have more transactions $97.07 involved and for way more ETH than I had in the wallet. We were both new to Ocean and the level of developer skills needed to get everything done we hoped. But are still learning and want to take The Predictors App to the next level on Ocean Protocol. Neither one of us are backend coders, but we are still working to get CoinMarketCap API integrated to provide our live prices. Budgets are tight right now so finishing the publish process of our Ocean dataTokens will have to wait until we can afford it or if we win a prize we will use the funds to finish the data publishing, and hire someone help get CoinMarketCap API working with WordPress.
What's next for The Predictors App
Reaching more people to let them know they can make predictions using the app and get rewarded for contributing prediction data to our datasets. Updating our proof of concept that is focused on crypto only right now, there are big markets beyond crypto prices, that businesses, organizations, and people interested in crowdsourcing predictions about and they can benefit for the wisdom of the crowd. Affiliate modeling for passing on the earnings from our prediction data to our Predictors our app creates a way for people to get Ocean Tokens through our rewards system benefiting the predictors with the highest accuracy.
Built With
css3
html5
javascript
ocean-datatokens
Try it out
thepredictors.app
|
The Predictors App
|
Win prizes and earn token rewards from making correct predictions. As you level up your accuracy the rewards you can win increase. Become a Master Predictor? Or ask for predictions from the crowd!
|
['Boone Bergsma', 'Nahara Johnson']
|
[]
|
['css3', 'html5', 'javascript', 'ocean-datatokens']
| 11
|
10,025
|
https://devpost.com/software/reefnet
|
Gigamesh Garage Ventures
Inspiration
Starkware Veedo VDF, Ocean Protocol, Money Streaming, NoteStream, Aztec Protocol, zkSync, RANDAO
Summary
Privacy Preserving Metadata Proof Powered Data Streaming Token Vaults on Ocean Protocol
Problems
Lack of provenance in Data Streaming Market Places
Lack of metadata verifiability in Data Streaming Market Places
Lack of fairness in Data Trading and Data Monetisation Markets
What it does
Integration of Data Streaming Tokens with Metadata Mixing Proofs
Implementation of Data Streaming Standards usig ERC 1620
Implementation of Verifiable Delay Functions for DAO Security
Implementation of Zero Knowledge Proof Powered Rollups for Scalability
Implementation of Token Vaults as a yield aggregator for Balancer Pools
How I built it
Fork of the Ocean Protocol Smart Contracts
Data Tokens using Ocean Protocol Framework
Data Streaming Standards based on ERC 1620
Token Vault Integration to Balancer Pools
Data Diligence Proofs using Circom Circuits
Metadata Proofs using Zokrates ZKP Toolkit
Data Streaming Indexing using Starkware Veedo VDF
Data Stream Security using Starkware Veedo VDF
Data Streaming Management using VDF Protected RANDAO
Data Token Transactions using Simple zkRollups
Challenges I ran into
Solidity Version Compatiability Issues
Zokrates Library Integration Issues
Circo Circuit Compilation Issues
Accomplishments that I'm proud of
Token Vault Integration on Balancer Pools
Data Streaming System on ERC 1620
Data Stream Management DAO using RANDAO
What I learned
Ocean Protocol, Balancer Protocol, ERC 1620
What's next for ReefNet
Development of Zero Knowledge Metadata Oracles
Development of Metadata Mixing NFTs
Development of Data - Metadata Token Swaps
Built With
circom
html
javascript
node.js
rics
solidity
wasm
zokrates
Try it out
github.com
|
ReefNet
|
Privacy Preserving Metadata Proof Powered Data Streaming Token Vaults with zkRollups & zkTokens on Ocean Protocol
|
['Gokul Alex']
|
[]
|
['circom', 'html', 'javascript', 'node.js', 'rics', 'solidity', 'wasm', 'zokrates']
| 12
|
10,025
|
https://devpost.com/software/decentralized-container-market
|
Decentralized Container Market
A marketplace to find, publish and trade container image license in the Ocean Network.
https://decentralized-container-market.netlify.app/
https://github.com/viraja1/decentralized_container_market
Other Tools for Decentralized Container Market
Decentralized Docker Hub
Using Decentralized Docker Hub, you can easily push and pull docker images from IPFS and filecoin. It is powered by Powergate. It also has support for ENS domain names.
https://github.com/viraja1/decentralized_docker_hub
Decentralized Docker Hub Registry
Decentralized Docker Hub Registry allows you to push and pull docker images from IPFS. It provides a native docker integration via a custom docker registry server (v2). It is powered by Textile Hub and Textile Buckets. It also has support for Fleek Space Daemon. Decentralized Docker Hub Registry has support for encryption and team sharing via Textile Hub.
https://github.com/viraja1/decentralized_docker_hub_registry
Decentralized License Generator
Generate or verify license for Ocean Protocol Datatokens (Container Image License)
https://decentralized-license-generator.netlify.app/
https://github.com/viraja1/decentralized_license_generator
How to use Decentralized Container Market
Publisher Side
Publish the docker image using Decentralized Docker Hub or Decentralized Docker Hub Registry
Create a url with the license details and documentation regarding how to pull the published docker image
Publish the license for the Container image using Decentralized Container Market -
https://decentralized-container-market.netlify.app/publish
Buyer Side
Check the Decentralized Container Market -
https://decentralized-container-market.netlify.app/
Select the container image license to be purchased
Purchase the container image license using OCEAN token (buyer gets datatoken for license)
Get the url with the license details and documentation
Pull the docker image using the documentation
Generate license code for docker image using Decentralized License Generator
Use the license code for verification whenever required (container could include a flow for license verification before usage)
Built With
filecoin
ipfs
ocean
react
Try it out
decentralized-container-market.netlify.app
github.com
github.com
github.com
decentralized-license-generator.netlify.app
github.com
|
Decentralized Container Market
|
A marketplace to find, publish and trade container image license in the Ocean Network.
|
['Viraj Anchan']
|
[]
|
['filecoin', 'ipfs', 'ocean', 'react']
| 13
|
10,025
|
https://devpost.com/software/ghar
|
Inspiration
We went behind the essence of datatokens ,that is ownership/access of particular data. With the concept of datatoken we thought of creating tokens that provide us access/ownership to data of any real world asset.The next big thing was Real Estate therefore we tried to tokenize Real Estate here in our project.
In reality, commercial real estate holds a significant role in the overall world economy. According to MSCI, the global real estate market increased from $8.9 trillion in 2018 to $9.6 trillion in 2019. Although it seems like the industry is doing quite well, it actually consists of many frictions and liquidity issues.We are trying to solve them with datatoken as one of its leading solution
What it does
This is a decentralized app designed to create , transfer and authenticate your datatokens, which in this case is Real Estate DataTokens. We also have provided the user with the option to check the timestamp of a particular token in the time of any dispute.We are here trying to tokenize all the real estate document information so as to give right of ownership of property to the owner of the token.This requires special attention therefore we went ahead and made datatokens using ocean protocol.We can induce data economy by tokenizing different real world things/assets.Datatoken will then permit just one person to have ownership of the particular real world asset.
Advantages of such system -
- Easy auctioning of property
- Easy and efficient verification of real estate
- Buying and selling of property can be done with minimal paperwork
- In the time of social isolation showing documents is difficult but having authentic token is easy
- Due to app being decentralized ,it is not under any central authority which makes it great for the end user
- Eradicating corruption norms and high broker fees , instead the dealing can be done with two parties all by themselves
- Access to information and paperwork can be transferred with minimalistic cost
How we built it
We initially started with a react based application for voting and had added like a separate voting platform in the ocean market app but soon we realized it was useless, then we shifted to this idea of tokenizing real world assets.
Challenges we ran into
Communication gap due to the holidays
energy web foundation error about the did registry module
late start of our project
Accomplishments that we are proud of
I was able of deduce the basic work of oceanjs and work with web3js to replicate the same.
What's next for Ghar
Adding an auctioning system(smart contract or otherwise)
Conversion of these datatokens to secret datatokens
Searching of property
Create dynamic cost/ocean fees depending on the particular pool and datatoken
Built With
bootstrap
css3
datatokens
html5
javascript
solidity
web3js
webpack
Try it out
ghar.netlify.app
github.com
|
Ghar
|
Tokenizing all the real world assets
|
['Vineet Kumar']
|
[]
|
['bootstrap', 'css3', 'datatokens', 'html5', 'javascript', 'solidity', 'web3js', 'webpack']
| 14
|
10,025
|
https://devpost.com/software/sell-subscription
|
Sell Subscription on Ocean
Problem:
Working as an online teacher either for yoga, music or for coding is difficult when it comes collecting payment because of many reasons.
Single point of control.
No any decentralised platform where they can sell their skillsets.
Single point of failure.
Settling/reconciling the accounts very difficult.
The auditing process is very time-consuming.
Lots of manual work involved in HR, Manager, Accountant part.
Solution
A humble attempt to build a decentralized unified platform where one can sell their skills on Ocean marketplace through a plugin.
Enter the details about your work.
Assign the theme
Put the payment.
Automate the payment on an hourly/weekly/daily/monthly basis.
Many more..
Project details
Plugin: Plugin is developed where user can host thier live skills like music class, yoga class, teaching. This will be hosted on IPFS.
Backend: Market developed on top of ocean.
How to run
Clone the repo.
Open your chorme/brave broswer.
Select the chrome extension.
Once the extension is loaded, restart the browser.
Click on plugin. Get the link and share on ocean marketplace.
Resources
Ocean:
https://oceanprotocol.com/build
Built With
ocean
react
solidity
|
Sell Subscription
|
Sell Subscription on Ocean using filecoin and Sceret Network
|
['ocean pro']
|
[]
|
['ocean', 'react', 'solidity']
| 15
|
10,025
|
https://devpost.com/software/universal-medical-history-umh-app
|
Overview of your current medical condition
Booking a new visit with the doctor
All your previous medical visits
Your previous medical visit details - 1
Your previous medical visit details - 2
Search for doctors
Publish Data
Doctor Dashboard - All Patients
Doctor Dashboard - All appointments with filtering
Patient's medical history profile
Patient's medical history profile (with filters)
1. Inspiration
With the advent of Machine Learning and AI, the value for data has skyrocketed in recent times and it’s projected to increase in the future. Data has become a valuable asset for several applications ranging from self-driving cars to predicting the stock market. The quality of applications depends on the richness of the dataset which is often centralized and private.
Data related to healthcare is highly valuable as it can be used to better understand diseases, advance treatment, provide personalized care, improve diagnosis, and many more. Despite its benefits, medical data collection is challenging, and a major concern is the privacy of the patients which makes it difficult to scale.
Personal medical history is also important for the doctors when you visit them as they would have a better idea of what your body has gone through and can prescribe drugs accordingly. But today, it is usually maintained in paperwork (which makes it hard to search for something) and the hospitals that maintain digital records, confine it within themselves. We often switch places and doctors, so this makes it really hard for the new doctor at a new place to get to know about your medical history in detail.
So, our idea was to develop an application that collects medical data, give the doctors a platform to access their patients’ records and find what they want easily, and for the users to publish their basic healthcare data to be made available for use by the researchers, government agencies, policymakers, insurance corporations, and much more.
2. What it does
2 A. The Universal Medical Health App is a web application that you can use for:
Search for doctors in your city and make an appointment at the doctor’s office.
Track prescriptions and upload medical test reports to a decentralized storage network, powered by Filecoin (Slate).
Publish the medical data on the ocean market place without compromising the privacy (keeping the contact details private), and make some money when it is purchased by researchers, insurance corporations, and others who might need healthcare records.
2 B. If you’re a doctor, you can do the following as well:
Check the appointments that you have for the day.
Manage all your previous and upcoming visits at one place.
Check your patients’ medical history (including their family history, if available).
Fill in the details of the patient visit.
Prescribe drugs, update the vaccinations provided, prescribe medical tests, and check the medical report when available.
Various filters are provided for you to find what you are looking for in your patients’ history.
3. How we built it
UMH is built out of an open-source stack, so there will be no need for paying a licensing fee to anyone when you use it. For the back end, we used Django (which is an open-source web framework for Python). For the dashboard, an open-source bootstrap-based dashboard was forked and edited to cater to the needs. The database used is Postgres, and the database for testing is an SQLite database.
Slate (by Filecoin) was accessed through their REST APIs and is used for uploading the medical test reports, as well as hosting the excel sheet data to be sold using Ocean Protocol.
When the snapshot of the data is stored on the Filecoin network, the URL generated was used to create a data asset to be sold on the Ocean Marketplace, and this process was enabled by Ocean’s library for Python (ocean.py).
We also used the Heroku cloud platform to host our Django application.
4. Challenges we ran into
We are relatively new to the concept of blockchains and the use of datatokens in exchange for services. So, we had to spend time understanding the theory.
5. Accomplishments that we're proud of
The satisfaction that our application has the potential to impact many lives is something that we are proud of the most. We believe that advancements in the field of healthcare technology will have an impact on saving lives. The fact that our application enables doctors to get critical information about patients, and support research on healthcare by making the basic medical data necessary for healthcare research available through our platform for being used by researchers who might use the data to end up solving a disease makes us proud of what we have created.
6. What we learned
We learned more of the theory behind blockchain technologies, the merits of decentralized applications over the traditional centralized ones, and the impact sharing data could create while maintaining privacy at the same time. We also learned about the Ocean Protocol, which we can use in other open-source projects to make the data publicly available, and using Filecoin for storing important files securely and not losing them.
7. What's next for Universal Medical History (UMH App)
Enabling compute-to-data services and adding a secret contract on top of that.
Creating a decentralized medical insurance corporation that uses the medical history of the users applying for insurance in order to calculate the cost for it (Powered by DAOstack).
Giving a place for the users who are not able to pay the medical bills to put up a request for their bill to be paid by the OCEAN tokens generated by the monthly overall medical history data published to the OCEAN marketplace by the UMH platform.
A cross-platform mobile app using flutter.
Face recognition for the mobile app in order for the doctors to identify the patient and look into their medical history when the patient is unconscious during an emergency visit (such as from a car accident). Also, the platform will send a text message to the patient's parents in that situation.
8. Instructions for running
There are two ways in which you can access the UMH app:
8 A. Through the hosted web app on the Heroku cloud platform
Please use the following link to access the web app online:
https://universalmh.herokuapp.com/
_ note: we are using the free tier of Heroku cloud for running UMH. So, it might take some time to load initially _
8 B. Running the Django application locally by cloning the GitHub repository:
Clone
UMH app's GitHub repository
Create a project on
infura
and get the project ID.
Set up with
Slate
:
Sign Up.
Get the developer API key.
Create a Slate.
Get the Slate ID.
Then, create a Python virtual environment, and activate it:
run the following command within the cloned repository
virtualenv <your_env_name>
and then run
source <your_env_name>/bin/activate
Export the following environment variables (note: you can change the
rinkeby
to
ropsten
or
mainnet
if you are using those chains) by running the following commands:
export AQUARIUS_URL=https://aquarius.rinkeby.oceanprotocol.com
export ENV_TYPE=local_test
(if you change it to deployment, follow the
step 7
)
export NETWORK_URL=https://rinkeby.infura.io/v3/<your_infura_project_id>/
export PROVIDER_URL=https://provider.rinkeby.oceanprotocol.com
export SLATE_AUTH_CODE=<your_slate_developer_api_key>
export SLATE_ID=<the_id_of_the_slate_that_you_created>
Install the requirements onto your virtual environment by using
pip3 install -r requirements.txt
_ (Optional) If you’ve set the ENV_TYPE as production, and you are using a Postgres database, please export the following environment variables: _
export DB_HOST=<your_database_host_address>
export DB_NAME=<name_of_your_database>
export DB_PORT=<the_port_of_your_PostgreSQL_database>
export DB_USER=<database_user_name>
export DB_PASS=<password_of_the_database_user>
Run the following commands to make database migrations:
python3 manage.py makemigrations
python3 manage.py migrate
Run the Django server on your localhost at port 8000 by using the command:
python3 manage.py runserver
Built With
bootstrap
css3
django
filecoin
heroku
html
html5
javascript
ocean-protocol
oceanprotocol
postgresql
python
Try it out
universalmh.herokuapp.com
github.com
|
Universal Medical History (UMH App)
|
A platform for people to book doctor visits and track prescriptions, doctors to track patients' medical history, and selling of medical history data to support medical research, insurance, and more
|
['Suraj S Jain', 'Sushranth Hebbar']
|
[]
|
['bootstrap', 'css3', 'django', 'filecoin', 'heroku', 'html', 'html5', 'javascript', 'ocean-protocol', 'oceanprotocol', 'postgresql', 'python']
| 16
|
10,025
|
https://devpost.com/software/ocean-datatoken-yield-farming
|
Inspiration
Liquidity mining approach of several DeFi projects.
What it does
This is a smart contract for ocean's data-token yield farming that provide a liquidity mining opportunity for a users.
Challenges I ran into
Integrate a liquidity mining approach into the OceanV3's pool (Ocean-DataToken)
What I learned
The structure of the Ocean's DataToken.
How to integrate a liquidity mining function.
What's next for Ocean DataToken Yield Farming
Add liquidity pool of the Ocean Governance Token (OGC).
Implement a governance structure by using the Ocean Governance Token (OGC).
Built With
balancer
datatoken
ocean-protocol
solidity
truffle
web3.js
Try it out
github.com
|
Ocean DataToken Yield Farming
|
This is a smart contract for ocean's data-token yield farming by using liquidity mining approach.
|
['Masanori Uno']
|
[]
|
['balancer', 'datatoken', 'ocean-protocol', 'solidity', 'truffle', 'web3.js']
| 17
|
10,025
|
https://devpost.com/software/filecoin-ocean-marketplace-integration
|
I forked the Ocean Market and integrated a Filecoin uploader (using the textile buckets API), making it much easier for users to upload their data to the marketplace.
Check it out, run "yarn install" and "yarn start". You will see the ocean marketplace as you know it. When you click publish, you can now see "Upload to filecoin" above the file inputs. Clicking it will open your file browser where you can choose a file. Choose a file and notice the input loader is spinning while the file is uploading as I fully integrated the process into the react state. When the file is done uploading to filecoin, the textile url of the file is automatically filled into the url input. You can now click "Add file" as usual and proceed with your publication. Looking at the url, you can see that the file has been properly uploaded.
Finally, I preserved the commits history so if you like this integration, you can simply merge it into the original repo.
Built With
filecoin
ocean
Try it out
github.com
|
Filecoin / Ocean market integration
|
Upload files to Filecoin from the ocean marketplace.
|
[]
|
[]
|
['filecoin', 'ocean']
| 18
|
10,025
|
https://devpost.com/software/databounty-io
|
DataBounty.io is a marketplace for data processing tasks. Data scientists can submit tasks like identifying an image of a dog or cat in order to train a data model. In exchange, users are paid for executing these tasks. But what if users just enter random responses? To counter this, I introduced control questions. Each task is divided in 10 questions. Out of these, 7 are questions which answer is unknown, 3 of them are control questions which answer is known. If the user does not reply correctly to these control questions, the whole set of 10 questions is discarded and the user is not paid. If the control questions are correct, we consider the whole batch of 10 questions correct, pay the the user and proceed to the next batch of 10 questions.
Additional development will include publishing the completed dataset to the Ocean Marketplace.
Built With
ocean
typescript
Try it out
databounty.io
|
DataBounty.io
|
Make money filling up data processing tasks!
|
[]
|
[]
|
['ocean', 'typescript']
| 19
|
10,025
|
https://devpost.com/software/defiantdex
|
Inspiration
What it does
DeFiantDEX
is a decentralized exchange with automated price discovery of data using
Ocean
datatokens in
Automated Market Maker (AMM)
pools.
How I built it
Challenges I ran into
Accomplishments that I'm proud of
What I learned
What's next for DeFiantDEX
Built With
javascript
Try it out
bitbucket.org
|
DeFiantDEX
|
DeFiantDEX is a decentralized exchange with automated price discovery of data using Ocean datatokens in Automated Market Maker (AMM) pools.
|
['Warp Smith']
|
[]
|
['javascript']
| 20
|
10,025
|
https://devpost.com/software/zod
|
Empty
Built With
javascript
ocean
|
Empty
|
Empty
|
[]
|
[]
|
['javascript', 'ocean']
| 21
|
10,025
|
https://devpost.com/software/the-auger-network-2uvmxz
|
seach for data
data sets
data submission
data overview
Inspiration
We are designing a future where providers of digital information within the SDG market are paid, resulting in a stronger middle class than ever before. Where the providers of raw materials ‘digital information’ are the new middle class at the back of a micropayment system, the futuristic economics. An ‘information age accounting’ that is complete and honest, ensuring that as much information as possible is valued in economic terms. The people who input the data used to improve predictions of how much electricity we need are anonymous and off the books. The act of cloud computing is shrinking the economy by pretending these people do not exist.
What it does
The Auger Network
is a data marketplace to sell, buy and curate quality data, built on decentralized blockchain technology.
The platform will enable people who contributed, even minutely to a database that allows, say, an electricity demand prediction algorithm, to perform a task, then a nano-payment, proportional both to the degree of contribution and the resultant value, will be due to the person.
It brings all the capabilities of data sharing in a Web3 native architecture that makes it easier for people to publish, auto-price, and sell their data. So you might just engage in a virtual workplace, set up some sensors for your vertical farm, upload your DNA, all this information will end-up fuelling some algorithm because of your existence. And the list can go on across all SDG's and not just limited to energy used as an example. It also extends to all other exponential technologies as they converge with AI somehow in addressing the SDG's.
How we built it
The Data Marketplace was built with a suite of four technical tools, mainly; Ocean Protocol Commons Marketplace, Web3, NodeJs, Typescript and ReactJS. Our communications were primarily through Telegram.
Challenges we ran into
Exploring the API and ensuring that data is displaying elegantly and securely. Inconsistent energy availability during rainy summer days in Africa.
There were also issues regarding successful transactions through MetaMask and the Ocean Protocol Network leading to initial trouble in publishing and accessing datasets.
Accomplishments that we're proud of
Working in building a future world of abundance, where everyone will be motivated to be open and generous with their data and get paid for it is incredible, I could not have asked for a better job. Doing that in alignment with the SDG's is like a cherry on top. “Skating where the puck’s going, not where it’s been” is super cool!!.
What we learned
On the technical side, we gained more experience with React Native and Typescript for building the front-end of the application and using the Commons Marketplace as a starting point. Generally, we have learned that if you put your heart into doing something and with the
right team
, you eventually succeed, as we were able to build this application by communicating entirely on the Internet. That "the future is faster than you think!" what we had on paper a few months ago, is coming alive like a blink!.
What's next for The Auger Network
We have only just begun, we are a long way out to reaching our vision. The team will commence further development of the platform and of course leverage off the Ocean family for other functionalities and tools. The project is also an incredible nudge for, fuelling the prevalent data question and starting rapid experimentation on how this new business model "post free service for data" will work.
Built With
daostack
erc20
metamask
node.js
ocean
typescript
web3
Try it out
github.com
|
The Auger Network
|
The Auger Network with the help of Web3 will unleash data as assets, enabling contribution, nano-payment.
|
['Dickens Juma']
|
[]
|
['daostack', 'erc20', 'metamask', 'node.js', 'ocean', 'typescript', 'web3']
| 22
|
10,025
|
https://devpost.com/software/concept-of-using-substrate
|
screenshot
Inspiration
I was inspired by Parity tutorial model and guide which was very well constructed.
What it does
I guess it helps to flow over polkadot network many extrinsics which can be placed into either singed or unsigned smart contracts generally speaking.
How I built it
I was working on Fedora 33. Needed to install cargo compiler basically.
Challenges I ran into
Fedora distro didnt have example Open-SSL tools. I needed to read carefully error messages for easier tracking issues.
Also Rust compiler required to be switched into Nightly version. That mode give opportunity to work on experimental projects.
Sadly i couldnt finish mine idea like i wanted. I was thinking about creating a hybrid which could derive Rabbit MQTT possibillities of communication and summon some those 90'ties vibe with pagers. Framework could basically inform us by beeping that we have received new transfer. Communication could be setup on hardware wallets. Of course that entails massive rework of those hardware wallets and revolutionize snippet utility. Now snippets wont be only for taking part of code. They could change with hashes that our wallet receive. What a thrilling attempt would be to implement Shor algorithm for recognizing hashes and categorizing them like author pointed in blog.
< Data token ->
< Some rights to object or property >
Everything require to be categorized to make our wallet display usefull and comfortable for user.
Accomplishments that I'm proud of
I know that i had to test app, write clever code but i wanted to participate in such event especially after winning 3rd place in Sony Ericcson hackathon in Cracow which pumped into mine mind new energy.
What I learned
Installation of Kubernetes, what are Pods.
Installation of MetalMask wallet wasnt that hard at all!.
I manage to a grasp a tiny bit of blockchain conception.
In Rust which i learn a while now, i got acknowledged thanks to author about what are turbo fishes, error handling which require invoking phrase Ok (()) after writing function.
After hearing ERC-20 acronym i wont knod without any understandment. Its a huge step forward.
To realize how huge it is i had to read about Market stocks in Greece during Antiquity.
Hackathons based on economical knowledge are hard.
They require to be very carefull especially when you work on those smart contracts and using such sensitive data must always end up in good hands.
What's next for Concept of using substrate
NEAR Contracts.
Concept for me, a 30 yrs old boomer whose mentality stuck in 90's demand to get a grip and do something more accurate than i announced earlier. Those patterns which could be implemented on hardware wallet will require a chip simmilar to Apple for example which can trigger machine learning apps.
I was thinking about M1 chip.
Peter Jagiello
Built With
fedora
rust
substrate
Try it out
github.com
|
Concept of using substrate
|
rust, substrate polkadot
|
['Piojagcodes Jagiello Peter']
|
[]
|
['fedora', 'rust', 'substrate']
| 23
|
10,025
|
https://devpost.com/software/fitbit-data-union
|
Inspiration
At last year's Ocean Hackathon,
I won third place for my chrome extension that allowed users to capture their browsing history and sell it on the ocean marketplace
. I then discovered that a company called Swash had "borrowed" my idea to create a
Data Union business on Streamr
. Swash captures the browsing histories from thousand of users then stream it live and sell it on the Streamr marketplace to data scientists. The users then receive a fraction of the price as payment for providing their data. As Swash already implemented my idea, I looked around to find what else could a random person like me stream live for revenues. I stoped searching when reaching my Fitbit tracker on my wrist!
What it does
This project is a Data Union for merging and crowdsourcing thousands of users’ Fitbit data and shares profits with them. Fitbit data includes daily activities, steps, consumed calories, heart-rate, etc. To start earning, users simply connect their Fitbit account to the platform then receive profits directly into their wallet.
How I built it
The frontend is a simple interface that allows users to go through the Fitbit Oauth 2 authorization process and send their access token and wallet payment address to the server. The server then stores that data in memory along with all the other users. The server has a cron job that runs every 10 minutes to fetch activities, steps, food intakes, gps locations etc. from Fitbit. The data is then aggregated with other users' data and streamed to the Streamr Data Union where it can be sold for 10 DATA coins per day. Users are then payed everyday a fraction of that price to their wallet.
Challenges I ran into
As the price of Ether has been very volatile over the past few days, when I tried to deploy the Data Union on
streamr.network/
the deployment price of the smart contract was $1,600! I therefore was not able to afford a mainnet deployment and had to use the
Localhost dev environment provider by Streamr
. Unfortunately the experience has been very painful as it took me days to make it work... One of the main issue I had was how to connect Metamask to the local ganache instance created by streamr as there is little to no documentation about it. By default, Streamr want to connect to Chain ID 8995 with is nowhere to be found. I had to try many experiments before understanding that I had to connect Metamask to localhost:8545 then change the default chain ID to 8995. Then I couldn't deploy the smart contract because I didn't have any fund on that specific network created by streamr which I couldn't get any mnemonic from like with a on a normal ganache instance. After hours of going through documentation and github repos I found a faucet repo that contained a file with a private key. This account had 999999 ETH on the local ganache session and I was finally able to deploy the Data Union. From there, everything else went smoothly.
Accomplishments that I'm proud of
I have many times considered quitting this project with all the issues I had with Streamr Docker Dev environment but I am proud that I kept going and solved all the issues one after the other.
What I learned
I learned a lot about Streamr and Data Unions.
What's next for Fitbit Data Union
Deploying the data union to the mainnet would be the first next step and also deploying the app on FitnessDataUnion.com which I have just purchased. Also I didn't have enough time to automate payments to users but that is just regular erc20 token transfer which is easy enough. Finally, after some time and and a few hundreds of user data, it would be great to export everything in one file and put it on sale on the Ocean Marketplace (this is an ocean hackathon after all ^^)
Built With
streamr
typescript
Try it out
github.com
|
Fitness Data Union
|
This is a Data Union for merging and crowdsourcing thousands of users’ Fitbit data and shares profits with them.
|
[]
|
[]
|
['streamr', 'typescript']
| 24
|
10,025
|
https://devpost.com/software/virtualtrack-q5mbew
|
na
Built With
na
|
na
|
na
|
[]
|
[]
|
['na']
| 25
|
10,026
|
https://devpost.com/software/dsaas-decentralized-storage-as-a-service
|
Our login page
Home Page of our website
Managing our database entry
Showing hash
Inspiration
As a team, we have always been interested in security, and we understand just how important security is to every individual who trusts another entity with their private information. However, most database storage services are centralized, which means that if a data breach occurs, it can severely hurt the financial ecosystem solely due to entitlements. The Equifax data breach in 2017 caused up to $400,000,000 of damage, and the Facebook Cambridge Analytica Breach caused a loss of approximately $87,000,000. Although these numbers are significant, they are nothing compared to potential future losses, now that the cloud storage market is predicted to reach $97.4 billion by 2022 according to AMR. Data breaches of mega companies could cause losses of billions of dollars, which is why it is important for database storage services to migrate to a decentralized environment. This is why we created NoDeSQL.
What it does
NoDeSQL is a non-relational, decentralized database service that provides users with all of the regular functions of a normal database, but in a decentralized manner. Due to its intuitive interface and the efficiency of IPFS file-sharing, it maintains the convenience of traditional options such as MongoDB and Firebase, but allows for the security of Blockstack decentralization. It has the features of a typical NoSQL database like creating, adding to, and updating databases, but it remains decentralized to remove security threats. Due to IPFS's powerful file sharing system, this website has high efficiency and surpasses traditional options due to its powerful computation methods.
How we built it
The front end of our web application was built with React, and we used fetch api and axios to interact with the back end. We used IPFS to create the decentralized aspect of our project. We used GAIA storage to store each user's specific hash and Express for our backend.
Challenges we ran into
Some of the challenges we ran into were implementing the GAIA storage, as it was our first time doing so and reading the documentation was challenging. Also, using Visual Studio Code as our code editor, using the Live Share extension added bugs to our code, making the entire process very difficult.
Accomplishments that we're proud of
We are very proud of having the entire back-end fully functional and the website fully deployed. This is the first time we have been able to deploy a project of ours and it has always been a goal of ours, and it has become a valid indicator in showing our work is completely done and properly complete.
What we learned
We learned how to use GAIA storage, which was very interesting to implement and we also learned about how insecure current database services are, and how we can make a decentralized approach to storing data intuitive for the common user and for web3 developers.
What's next for NoDeSQL
We would like to implement other QoL features that are present in other NoSQL databases that make using other centralized forms of databases so convenient and easy.
Built With
blockstack
css
express.js
fetchapi
gaia
html5
ipfs
javascript
node.js
react
Try it out
github.com
nodesql.tech
|
NoDeSQL
|
A web application that allows users to manage a decentralized database in a private, trustless manner.
|
['Aditya Keerthi', 'Daniel Yu', 'Arnav Tripathi']
|
['3rd Place', '1st Place - 1517 Grant']
|
['blockstack', 'css', 'express.js', 'fetchapi', 'gaia', 'html5', 'ipfs', 'javascript', 'node.js', 'react']
| 0
|
10,026
|
https://devpost.com/software/keep6-p846kn
|
Keep 6 Logo
Arduino Mega Wiring
RFID Read From 6+ Feet (Blue Light = Safe)
RFID Read From <6 Feet (White Light = COVID-19)
Flutter App Settings Page With RFID Input
Flutter App Add Whitelisted User Page
Flutter App Safe Social Distancing Interface
Flutter App Social Distancing Violation Interface
Website Map With Plotted Densely Packed Locations and Lat/Long Table
Website Sign Up Page
Firebase Database Entries For Backend Data Storage
Inspiration
Without a doubt, the most pressing global issue right now is the fight against COVID-19. Currently the battle against this global pandemic is fought on two fronts. While our healthcare heroes seek to end the disease one patient at a time, the rest of us must do our part to keep the global community safe and healthy. All across the world, people are quarantining themselves and practicing social distancing when going outside. According to the Center for Disease Control and Prevention, social distancing is defined by two main parts: not gathering in groups and staying at least 6 feet apart. With people still leaving their houses, whether to run essential errands or just to exercise, it becomes near impossible to keep track of everybody that has come near you. We wanted to create a widespread platform for people to be able to track the coronavirus status of the people that they have come near through factors like recency of interaction, proximity of interaction, and latest COVID-19 testing results.
What it does
Keep6 is a mobile app, Arduino hardware, and website based platform that encourages and monitors safe social-distancing. Using an RFID sensor, the platform is able to track the distance from the user to the other people around them. An ESP8266 module is then used for communication to a server which will log the distances and communicate them with the mobile app and website. The mobile app is used to determine whether the user has been within 6 feet of anyone and would therefore be at risk, while the website displays a Google Maps API detailing the most concentrated areas of people to avoid.
How we built it
Hardware
We used an Arduino Mega with an MFRC522 module for RFID distance functionality and an ESP8266 module for public server communication. A portable device was made for users to carry with them in their outdoors excursions. These RFID sensors are used to track the proximity of neighboring users and relay that to a server. Compared to Bluetooth and GPS location services, RFID distancing is accurate to the foot and has a very large range. Furthermore, because RFID is secure, does not track users’ data or absolute location, and only returns relative distances with other users, there are no privacy concerns for the user.
Server
The server is the middleman for the entire project. It handles all requests coming in from the arduino, iphone app, and the website. The first process that the server handles is users logging and signing in. From there the server gets a request from the arduino to update the device location. This information is then sent to a web socket that the iphone also connects to in order to view the location of other arduino devices in the area. The server then calculates the distance between devices in order to determine whether the user is within 6 feet of another user.
App
We created the app using the Flutter programming language and it allows you to connect to your RFID reader. This app will allow you to see if you are near someone else and notify you of your risk of COVID and the distance between you and the closest person. It also gives the user the option to set whitelists for people that are in their family by adding their email addresses. The app collects information on whether or not the user has been diagnosed with COVID-19 and accordingly updates the risk levels for those around them. All of the app communications are routes through the server to enhance security.
Website
We created the website using a Google Maps API paired with javascript code marking concentrated areas of people on the map. HTML/CSS was used for the formatting of the website which allows user login to view the personal coronavirus information in the form of maps and tables that is obtained through the server.
Challenges we ran into
We ran into numerous problems with wiring/programming the Arduino hardware for sensor reads and Wi-Fi communication. We were originally using a NodeMCU, but found out that it cannot handle Wi-Fi communication and RFID read simultaneously because of serial port limitations. After switching to the Arduino Mega and debugging server request issues, we were able to seamlessly implement the hardware. Furthermore, we had some issues with whitelist users on the backend because of the multiple links required for its functionality. However, we were able to develop a reliable algorithm for editing and parsing the storage structure to provide this functionality with no errors or miscommunications.
Accomplishments that we're proud of
We are especially proud that we were able to accomplish seamless Wi-Fi based server communication between 4 distinct components. Each of our 4 team members took on a component and worked together to mesh each piece together into a single, connected platform. With all of the user information, RFID identifiers, distances, and other data points that are passed between components as API parameters, we are proud that we created a platform that handles it all accurately and efficiently, all while working remotely.
What we learned
This was our first hackathon in a remote setting, so we had to learn to collaborate with each other through voice calls and coding live shares. While it was difficult at times to work on frontend/backend integration and hardware communication with other components, we were able to complete all of the functionality we originally envisioned.
What's next for Keep6
A potential next step for Keep6 would be to find a medically approved algorithm to determine the likelihood of infection for the user based on the data that the Keep6 server already tracks. Another important step to maximizing the effectiveness of this platform would be to expand to as many users as possible so that the server has more information. One way of doing this would be to make the RFID Arduino mechanism smaller and more convenient for users to carry around so that they would be more incentivized to use it.
Built With
amazon-web-services
arduino
c++
css
dart
firebase
flutter
html
javascript
node.js
react
Try it out
github.com
|
Keep6
|
A mobile app, Arduino hardware, and website based platform to encourage and monitor safe social-distancing
|
['Elias Wambugu', 'Arya Tschand', 'Albert Zou', 'Elias Wambugu', 'Sai Vedagiri']
|
['2nd Place', 'First Overall', '2nd Place']
|
['amazon-web-services', 'arduino', 'c++', 'css', 'dart', 'firebase', 'flutter', 'html', 'javascript', 'node.js', 'react']
| 1
|
10,026
|
https://devpost.com/software/aidistance
|
An image of our website- customizable definitions of "safe" for personal preferences!
Leaving the room!
Entering the room!
Visit the url pasted at the bottom of our project description for an explanation of our server-side code
Project Description
AIDistance is a website that allows users to see the amount of people in highly crowded areas. By analyzing security camera footage, AIDistance is able to get the precise number of people in a given location. All this information allows users to see when it is safe to visit certain locations, and allows people to naturally stagger their outings.
Misson
The 2020 Covid-19 global pandemic has revealed a lot about our safety measures as a nation. In these trying times, it is imperative that we uphold the guidelines set up by our health care professionals. However, life must go on, and to preserve the safety of our loved ones and those at risk, AI Distance proposes a solution. By monitoring active, public spaces, we ensure that no one place becomes too crowded to effectively maintain social distancing. Getting over this pandemic is a community effort, and we want to add a practical solution that can also be applied in different situations in the future.
Analyzing Security Camera Footage
AIDistance combines deep convolutional networks with a clean and efficient django UI. For the client side, designed for use on a Jetson Nano mini-computer with a Raspberry Pi-camera V2, we used the inception_v2_coco model from the tensorflow zoo library to detect humans in a real-time video feed from the Picam. Using novel algorithms applied on the model's output, we were able to extrapolate much more data than simply the basic bounding box provided by the model. Curiously, at close proximity, the model underwent performance issues, sometimes classifying a single human as two, with an arm or a leg being identified as a separate entity. We were able to efficiently solve this using several tools from NumPy to mathematically infer an erroneous overlap. We can also infer heading by keeping track of the historical movement of centerpoints of the various bounding boxes produced by the inception model. Along with various other tweaks, we were able to cut through a lot of the noise that we were receiving earlier in the project's history, such as random incorrect classifications. The data netted by rcnn.py is then fed straight to our server where it is handled and processed for users to easily access and manipulate.
The Website
The website for AI Distance is comprised of three main parts. The first part is the home page which details the goal and importance of the project, along with important information about the current world situation. The second part of the website is the Shops Nearby Page. This page allows users to see the population densities of nearby locations, and tells users if that location is safe to go to at this time. The page automatically gets data from security camera footage, or in this example, video taken from a Jetson Nano computer, and determines the amount of people in the store(This process is detailed in the first paragraph). It then displays this information in an easy to read, efficient way to the users. Finally, we also have the addLocation page which allows users to add their nearby shopping locations. For demonstration purposes, we only have video footage linked up to one location: Krogers. Obviosly, we hope to partner with businesses to include our software at their locations.
In addition, this website does have block-stack sign in. Users must sign in in order to have access to this information. However, on the public IP and for demonstration purposes, we disabled this functionality, so that everyone can see this information for the demo. Our video demo shows this sign in.
Video Explanations and Demos
https://www.youtube.com/watch?v=TkG2QDZwPHU
-Server side explanation
https://www.youtube.com/watch?v=00PF7QCuBIk
-Client side explanation
Built With
blockstack
css
firebase
html
javascript
numpy
python
scikit-learn
shell
tensorflow
Try it out
github.com
|
AIDistance
|
Project for DistanceHacks hackathon using deep convolutional networks and website integration to aid in social distancing.
|
['Liam Pilarski', 'Mehul Ghosal', 'Aryan Dugar']
|
['3rd Place', 'MLH - Best use of Blockstack']
|
['blockstack', 'css', 'firebase', 'html', 'javascript', 'numpy', 'python', 'scikit-learn', 'shell', 'tensorflow']
| 2
|
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