Yogamified AI

Improve your yoga practice with real-time feedback and gamification! Our platform uses AI to track poses, offering instant corrections and scoring to keep you motivated and improving.

Comment

Inspiration

With the rise of at-home fitness, I was inspired to create a platform that makes yoga more engaging and effective. I wanted to combine the ancient yoga with modern technology to help users not only improve their poses but also stay motivated through a gamified experience. The goal was to create a tool that could provide real-time feedback and make yoga practice more interactive.

What it does

Yogamified AI is a real-time yoga pose detection and correction platform. It uses AI-powered pose estimation to track the user's movements and compare them against reference poses. The platform provides instant feedback, letting users know if they are performing the pose correctly or if adjustments are needed. To make the experience more engaging, Yogamified AI also includes a scoring system that rewards users for maintaining correct poses and tracks their progress over time.

How we built it

I built Yogamified AI using MediaPipe for real-time pose detection and OpenCV for video processing. The system was trained with reference poses by capturing and saving the pose landmarks as .npy files. These reference landmarks were used to compare live camera input or uploaded images against the user’s current pose. I implemented a feedback mechanism that evaluates the user's pose in real-time and displays the results directly on the video feed, including score and streak information to gamify the experience.

Challenges we ran into

One of the major challenges was ensuring the accuracy of pose detection, especially when dealing with different body types and environmental conditions such as lighting and background. Calibrating the pose comparison thresholds was another challenge, as I had to balance between being too strict and too relaxed in evaluating the poses. Additionally, achieving real-time processing while maintaining smooth performance was a technical hurdle that required optimization of the video processing.

Accomplishments that we're proud of

I am particularly proud of successfully integrating real-time pose detection with a gamified feedback system. The ability to provide instant, actionable feedback in a user-friendly way is something im excited about. I also managed to create a platform that’s accessible to users at all levels of yoga practice, from beginners to advanced practitioners. Another accomplishment is the scalability of the system; it can be easily expanded to include more poses and additional features.

What we learned

This project taught me a lot about the challenges of real-time video processing and the challenges of working with AI-powered pose detection.I learned how to fine-tune machine learning models for specific use cases, like yoga pose recognition, and the importance of providing user-friendly feedback in fitness applications. Additionally, I gained experience in optimizing software for performance, particularly in scenarios requiring real-time processing.

What's next for Yogamified AI

Moving forward, I plan to expand the range of yoga poses that Yogamified AI can detect and refine the accuracy of pose correction suggestions. Maybe also aim to introduce more gamification elements, such as challenges, levels, and leaderboards, to enhance user engagement. Another exciting direction is to develop a mobile app version, making Yogamified AI more accessible for users on the go.

Built With

Share this project:

Updates