TheraCam

Group Productivity!

Inspiration

The idea for TheraCam stemmed from the need to make fitness safer and more accessible for elderly and middle-aged individuals. We noticed that many people want to stay active but lack access to personalized coaching that ensures proper form, which can lead to injuries and chronic pain. TheraCam aims to bridge this gap by providing real-time, form-correcting guidance that helps individuals exercise safely and build healthier habits.

What it does

TheraCam uses advanced object tracking and pose estimation technology to analyze a person’s form during various exercises. It provides instant feedback, alerting the user if their posture deviates from the recommended position, which helps to prevent injuries. Designed for accessibility, TheraCam is a personal coach that empowers users to work out confidently at home.

How we built it

We used software to bring TheraCam to life. Each device's camera module captures movements and with an open-source project we added analyzing algorithms to ensure patients have correct form. (OpenCV’s pose estimation and object-tracking algorithms). We integrated a frontend using Next.js that displays real-time feedback, alerting users when they need to adjust their form.

Challenges we ran into

Implementing precise pose estimation in real-time while ensuring accuracy was challenging. Handling various lighting conditions and different body types added to the complexity, as we wanted the model to be as inclusive and accurate as possible. Additionally, synchronizing the backend of Python with the front end in Next.js presented unexpected hurdles.

Accomplishments that we're proud of

We’re proud to have built a tool that makes exercising safer and more accessible, especially for groups that may not have professional fitness guidance. Successfully integrating the camera with OpenCV for accurate tracking and building a responsive frontend were key milestones. We’re also proud of the potential TheraCam has to positively impact users’ lives by promoting a safe, independent approach to fitness.

What we learned

This project taught us a lot about the challenges and nuances of computer vision, especially in real-world applications like exercise tracking. We gained a deeper understanding of how to enhance pose estimation for different body types and how to create a user-friendly interface that delivers important feedback in a simple way.

What's next for TheraCam

In the future, we plan to expand TheraCam’s capabilities to include additional exercises and customization options. We’re also considering integrating AI to provide more personalized feedback and insights based on individual progress. Adding a mobile app could increase TheraCam’s accessibility, allowing users to train with form-correction assistance from virtually anywhere.