## Members

Keith Khadar, Alexander Salinas, Eli Campos, Gabriella Conde

## Inspiration

Last year, Keith had a freshman roommate named Cayson who had played high school football until a knee injury sidelined him. While his condition improved in college—allowing him to walk, he couldn’t run. Keith remembered how he often had to make a 30-minute walk to his physical therapist. It was through witnessing his struggle and through Keiths experience working on medical devices in Dream Team Engineering, a Club at the University of Florida dedicated to improving patient care, and our curiosity to work on real world problems with AI, that we began to think about this issue.

## What it does

Our device tracks an injured athlete's movements and provides personalized advice, comparable to that of a world-class physical therapist, ensuring the patient recovers effectively and safely. Our device helps users perform physical therapy exercises at home safely while AI analyzes their movements to ensure they operate within their expected range of motion and effort values.

## How we built it

Using web technologies (Angular, Python, Tune) and microcontrollers (Flex-sensor + ESP32) to track, give insights, and show improvement over time.

## Challenges we ran into

Bluetooth Implementation: Establishing a reliable and efficient Bluetooth connection between the microcontroller and our web application proved more complex than anticipated.
Sleeve Assembly: Designing and constructing a comfortable, functional sleeve that accurately houses our sensors while maintaining flexibility was a delicate balance.
Data Interpretation: Translating raw sensor data into meaningful, actionable insights for users required extensive algorithm development and testing.
Cross-platform Compatibility: Ensuring our web application functioned seamlessly across various devices and browsers presented unexpected complications. Specifically browser as well as

## Accomplishments that we're proud of

Seamless Bluetooth Integration: We successfully implemented robust Bluetooth communication between our hardware and software components, enabling real-time data transfer.
Real Time Digital Signal Processing: Our team developed sophisticated algorithms to analyze data from our sensors, providing a comprehensive view of the user's movements and progress.
Intuitive User Interface: We created a user-friendly interface that clearly presents complex data and personalized recommendations in an easily digestible format.
Rapid Prototyping: Despite time constraints, we produced a fully functional prototype that demonstrates the core capabilities of our concept.
Tune AI Integration: We are proud of our connection to tune ai and using their llama ai model to provide insights into the patients movements.

## What we learned

Full-Stack Development: We gained valuable experience in integrating frontend and backend technologies, particularly in using Python for backend operations and Angular for the frontend.
Interdisciplinary Collaboration: We learned the importance of effective communication and teamwork when combining expertise from various fields (e.g., software development, hardware engineering, and physical therapy).
Real-world Problem Solving: This experience reinforced the value of addressing genuine societal needs through innovative technological solutions.

## What's next for Glucose

Enhanced Sensor Array: Integrate additional sensors (e.g., accelerometers, gyroscopes) for more comprehensive movement tracking and analysis.
Machine Learning Integration: Implement more advanced ML algorithms to improve personalization and predictive capabilities of our advice engine.
Clinical Trials: Conduct rigorous testing with physical therapists and patients to validate and refine our system's effectiveness.
Mobile App Development: Create dedicated iOS and Android apps to increase accessibility and user engagement.
Expanding Use Cases: Explore applications beyond athletic injuries, such as rehabilitation for stroke patients or elderly care. Another use case is to help correct diagnostic error. We will have a lot of data on how the patient moves and we can train machine learning models to then analyze that data and affirm the diagnostic that the doctor gave.
## Inspiration

We have a desire to spread awareness surrounding health issues in modern society. We also love data and the insights in can provide, so we wanted to build an application that made it easy and fun to explore the data that we all create and learn something about being active and healthy.

## What it does

Our web application processes data exported by Apple health and provides visualizations of the data as well as the ability to share data with others and be encouraged to remain healthy. Our educational component uses real world health data to educate users about the topics surrounding their health. Our application also provides insight into just how much data we all constantly are producing.

## How we built it

We build the application from the ground up, with a custom data processing pipeline from raw data upload to visualization and sharing. We designed the interface carefully to allow for the greatest impact of the data while still being enjoyable and easy to use.

## Challenges we ran into

We had a lot to learn, especially about moving and storing large amounts of data and especially doing it in a timely and user-friendly manner. Our biggest struggle was handling the daunting task of taking in raw data from Apple health and storing it in a format that was easy to access and analyze.

## Accomplishments that we're proud of

We're proud of the completed product that we came to despite early struggles to find the best approach to the challenge at hand. An architecture this complicated with so many moving components - large data, authentication, user experience design, and security - was above the scope of projects we worked on in the past, especially to complete in under 48 hours. We're proud to have come out with a complete and working product that has value to us and hopefully to others as well.

## What we learned

We learned a lot about building large scale applications and the challenges that come with rapid development. We had to move quickly, making many decisions while still focusing on producing a quality product that would stand the test of time.

## What's next for Open Health Board

We plan to expand the scope of our application to incorporate more data insights and educational components. While our platform is built entirely mobile friendly, a native iPhone application is hopefully in the near future to aid in keeping data up to sync with minimal work from the user. We plan to continue developing our data sharing and social aspects of the platform to encourage communication around the topic of health and wellness.
## Inspiration

All three of are international students who converged on this idea based on a shared experience: our grandmothers, who dreamed of coming to see us graduate, could not make the long trip due to loss of mobility. We have also seen our parents, who are around 60 years old, unable to enjoy the things they used to with us when we were kids: playing basketball, backpacking, skiing, leaving those cherished moments as memories. We realised that even though children born today are expected to live maybe 100+ years, their quality of life in their later years depends on how we take care of our bodies today. We did extensive research into how earlier life experiences affect mobility and realised that this is an uphill battle that starts today. We thus developed an accessible and intuitive approach to tracking and understanding factors that go into your present and future mobility.

## What it does

Hercules can see and understand your pain using only your iPhone camera and/or a voice note describing your pain, therefore simplifying understanding pains in the body to even those who aren't health literate. He allows you to log any fleeting, acute, or chronic pain, all while seamlessly tracking your activity using wearable data. He will create a long-term log correlating the two, helping develop an exercise routine based on the user's specific needs and limitations, ensuring that they say safe but active enough to promote long-term mobility health.

## How we built it

We first did in-depth research on what affects mobility health in the future and what we can do to alleviate effects of it deteriorating early on. We mapped out user journeys and decided that our target users would be everyone, with an emphasis on those who still have the power to prevent future mobility issues
Our product is a react-native app built in JavaScript/Node.js, and utilizes the OpenAI API, namely Whisper for speech-to-text of user voice input, GPT-3.5 Turbo for semantic understanding of that text, GPT-4 Vision for image recognition and reasoning, and OpenAI TTS for text-to-speech for Hercules' voice. We used Figma to mock up the app design while we built the bare-bones user flow, then added the styling.
Additionally, although we did not have time to implement this into our app, we worked on developing a "Mobility Score" that uses wearable data to evaluate an individual's mobility, which was inspired by the Oura Ring's "Sleep Score" and "Activity Score" measurements and equations.

## Challenges we ran into

We ended up having to design and implement a lot of screens, which had to be done after implementing the image recognition and AI speech software. We also realised that there simply isn't enough data that measures changes in one's mobility long-term, and how acute pains could manifest into more serious conditions with old age. This actually made us more excited about building the product however, as it could allow us to accumulate such data, especially for those historically underrepresented in medical data such as women and minorities.
Technically, the main challenge we ran into was that OpenAI's GPT-4 vision model was not recognizing body parts in some cases from the pictures we sent it. We eventually realized that it was due to OpenAI's API automatically cropping our image if it wasn't in the correct aspect ratio, and were able to fix it by implement cropping client-side.

## Accomplishments that we're proud of

We were able to design and implement 80% of our screens that would be required for a fully functional product. We also managed to make Hercules a very helpful and personable AI agent that makes the user feel at ease. We are most proud that we have created a tool for our generation to enjoy the later stages in life more than our older family members ever did.

## What we learned

We understand mobility physiology far better, as well as designing not only for older generations but the older generations of the future. We also learned that the biggest causes of the health span - lifespan gap are lack of health literacy and access to personalised care, both of which could now be solved given new technologies such as AI agents, which is what we envision for our product.

## What's next for Hercules

We would love to develop personalised fitness programs for those who have very specific pains and injuries but not the means to go to a doctor or physical therapist. We would likely offer these programs at a very low cost and keep them accessible in terms of ease of following them. We also want to introduce educational aspects of monitoring pain and habit-building to ensure users are maintaining the actions needed to preserve their mobility until later in life. Finally, a key indicator of mobility health is nutrition, so we want to incorporate a nutrition journal that's as easy to use as the pain log, using just a picture for Hercules to tell the user about their eating habits as it relates to their long-term mobility health.