## Inspiration

Every year, millions of people suffer from concussions without even realizing it. We wanted to tackle this problem by offering a simple solution that could be used by everyone to offer these people proper care.

## What it does

Harnessing computer vision and NLP, we have developed a tool that can administer a concussion test in under 2 minutes. It can be easily done anywhere using just a smartphone. The test consists of multiple cognitive tasks in order to get a complete assessment of the situation.

## How we built it

The application consists of a react-native app that can be installed on both iOS and Android devices. The app communicates with our servers, running a complex neural network model, which analyzes the user's pupils. It is then passed through a computer vision algorithm written in opencv. Next, there is a reaction game built in react-native to test reflexes. A speech analysis tool that uses the Google Cloud Platform's API is also used. Finally, there is a questionnaire regarding the symptoms of a user's concussion.

## Challenges we ran into

It was very complicated to get a proper segmentation of the pupil because of how small it is. We also ran into some minor Google Cloud bugs.

## Accomplishments that we're proud of

We are very proud of our handmade opencv algorithm. We also love how intuitive our UI looks.

## What we learned

It was Antonio's first time using react-native and Alex's first time using torch.

## What's next for BrainBud

By obtaining a bigger data set of pupils, we could improve the accuracy of the algorithm.
## Inspiration

Ever join a project only to be overwhelmed by all of the open tickets? Not sure which tasks you should take on to increase the teams overall productivity? As students, we know the struggle. We also know that this does not end when school ends and in many different work environments you may encounter the same situations.

## What it does

tAIket allows project managers to invite collaborators to a project. Once the user joins the project, tAIket will analyze their resume for both soft skills and hard skills. Once the users resume has been analyzed, tAIket will provide the user a list of tickets sorted in order of what it has determined that user would be the best at. From here, the user can accept the task, work on it, and mark it as complete! This helps increase productivity as it will match users with tasks that they should be able to complete with relative ease.

## How we built it

Our initial prototype of the UI was designed using Figma. The frontend was then developed using the Vue framework. The backend was done in Python via the Flask framework. The database we used to store users, projects, and tickets was Redis.

## Challenges we ran into

We ran into a few challenges throughout the course of the project. Figuring out how to parse a PDF, using fuzzy searching and cosine similarity analysis to help identify the users skills were a few of our main challenges. Additionally working out how to use Redis was another challenge we faced. Thanks to the help from the wonderful mentors and some online resources (documentation, etc.), we were able to work through these problems. We also had some difficulty working out how to make our site look nice and clean. We ended up looking at many different sites to help us identify some key ideas in overall web design.

## Accomplishments that we're proud of

Overall, we have much that we can be proud of from this project. For one, implementing fuzzy searching and cosine similarity analysis is something we are happy to have achieved. Additionally, knowing how long the process to create a UI should normally take, especially when considering user centered design, we are proud of the UI that we were able to create in the time that we did have.

## What we learned

Each team member has a different skillset and knowledge level. For some of us, this was a great opportunity to learn a new framework while for others this was a great opportunity to challenge and expand our existing knowledge. This was the first time that we have used Redis and we found it was fairly easy to understand how to use it. We also had the chance to explore natural language processing models with fuzzy search and our cosine similarity analysis.

## What's next for tAIket

In the future, we would like to add the ability to assign a task to all members of a project. Some tasks in projects *must* be completed by all members so we believe that this functionality would be useful. Additionally, the ability for "regular" users to "suggest" a task. We believe that this functionality would be useful as sometimes a user may notice something that is broken or needs to be completed but the project manager has not noticed it. Finally, something else that we would work on in the future would be the implementation of the features located in the sidebar of the screen where the tasks are displayed.
## Inspiration

Touch screen devices have been used increasingly in the domain of cognitive healing
as an aid for children with autism, down-syndrome, and brain traumatic injury
To help treat amnesia, dementia, and post stroke symptoms
There generally is no one-size-fits all solution to help rehabilitate these victims
This is validated by a list of methods to treat amnesia from psychologytoday.com which includes hypnosis, energy psychology, cognitive therapy, nutrition and Technical Assistance from iPhone, iPad, tablet as potential treatment methods

## What it does

CORTEX features two exercises to help people of with different cognitive autistic people read and understand facial expressions and people who suffer cognitive impairment following intermediate and severe strokes.
The first group can you the game called \_ Sweet Emotion \_ to correctly identify people's facial expression with the power of their camera. The second game called \_ Magnifying Glass 2 \_ lets people point their phone at various objects and attempt to correctly identify it through a quiz like interface.

All the user's progress can be tracked via a separate web interface.

## How we built it

We used Swift to build the iOS app, the model Inceptionv3 for object classification, and CNNEmotions for expression classification. We used React to build the web app, and the app posts it's information to a Firebase backend which we use for authentication and as a database.

## Challenges we ran into

ML models weren't as accurate in portrait mode as expected. Design challenges since we designed for a group with special requirements.

## Accomplishments that we're proud of

Implemented accessibility services for illiterate, the object detection works quite well.

## What we learned

Learned many skills in Firebase, more advanced iOS development skills and web development skills

## What's next for Cortex

User testing, more background research, beginning to refine the exercises based on user input and academic research.