## Inspiration: Jin, our classmate, met with an accident last week. He had some mild concussions and physical injuries. Being an immigrant student in the US, he didn’t have access to relevant in-network data regarding health insurance. And even during the time of emergency, he had no answers to questions like “Which Healthcare facility to avail”, “How long will it take?” and “Will they accept my health insurance?”

## What it does: It utilizes the accelerometer in your phone when it detects gait abnormalities so it can preempt when you may need to go to the nearest hospital. An accompanying mobile app alerts you to the nearest hospitals that accept your insurance.

## How we built it: Front end using php, server-side programming language that controls the HTML, as well as FHIR API and Google MyMaps. We integrated mobile accelerometer to trigger application in emergency

## Challenges we ran into: As non-technical participants figuring out how to work with REST API’s. We first used manual data which wasn’t sufficient by the team’s standards. Once adopting APIs we struggled with not providing consistent data for each end point.

## Accomplishments: As a group of 4 design students that is majority international, we were able to build something that we need, figure out how to access a mobile phones inbuilt gyroscope and accelerometer to detect when an injury may have occurred using gait analysis. We learned how to use the FHIR API, deployed the InterSystems API and created a working an MVP in 24 hours!

## What we learned: We learned how to work in teams, distribution of work based on everyone’s skills, networking with an awesome bunch of people and sponsors!!, learning as much as possible throughout the process and obviously having fun while hacking :P!

## What's next for inSurely: Next step for inSurely is to integrate our findings on Gait analysis using phone accelerometer and gyroscope to track the movements of people when they get injured. It can further be expanded to detect Degenerative Pathology, Musculoskeletal disorders, Neurological Damages.
## Inspiration

The main inspiration behind this project was that we wanted to give users a clutter-free environment to find vital information when they need it. Health emergencies are time-sensitive and easily complicated due to lack of information access like allergen information, current medications, etc.

## What it does

MedFTW removes all doubt from the equation by giving users reliable access to information that is directly verified by healthcare providers. Using InterSystem's FHIR-based API, we give patients real-time information in a format that is easy to understand.

## How we built it

To make our application function as intended, it implements Intersystem's API that includes an extensive database of healthcare Patients, Practitioners, Patient Diagnostics, and other resources. The application fetches all the relevant data for a specific patient when the user inputs a valid patient ID at startup. Once a user gets into their personalized homepage, they will be able to access their health records, such as allergy restrictions (if any exist), personalized practitioners for them, immunization records, medical records, and care plans.

## Challenges we ran into

Our main issue was implementing the information we received from our API calls since the FHIR resource types had a complex organization and syntax. We also struggled to implement our back-end Python with our front-end Dart/Flutter so that both models communicated with each other. However, we were able to resolve both of these issues by thoroughly testing our code against errors, speaking to mentors and InterSystem representatives, and consulting the documentation for the resources we implemented.

## Accomplishments that we're proud of

We were really proud of the server that connected our backend Python code with our front end model. Another exciting feature that we were really happy that worked was the map feature that found hospitals near a certain user using their location and displayed them on a map. Also with just their ID, the app gives users the location and contact information of the practitioners nearest to them with a click of a button.

## What we learned

We learned how to use APIs to access data and retrieve certain data specific to our application. We also learned how to deploy a server on Heroku and use it connect our backend and frontend models.

## What's next for Med For The Win

Our primary next steps would be to implement security features to protect user data, and publish our app on the Google Play Store and Apple App Store to begin making a direct influence on people while also getting feedback for features. We also hope to optimize our code to analyze the API returns faster, and apply advanced data analysis and machine learning to develop predictive models for healthcare outcomes like prescription usage, physical therapy progress, etc.
## 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.