AI Fitness Coach

Inspiration

AI Fitness Coach was inspired by the idea that anyone should be able to work out safely without needing a trainer or internet. Many people struggle with exercise form, so we set out to create an on-device AI that can guide users in real time, using only their mobile camera. By combining pose detection with offline AI, the project aims to make smart, accessible fitness available to everyone.

What it does

AI Fitness Coach uses your mobile device’s camera to track your body movements in real time and guide you through exercises safely. It detects your pose, analyzes your posture, and gives instant feedback—like telling you to straighten your back or bend your knees more. The app also counts repetitions, monitors form accuracy, and works completely offline using optimized on-device AI. In short, it turns any Arm-powered mobile device into a smart, personal workout assistant that helps you exercise correctly and confidently—without needing the internet.

How we built it

We built AI Fitness Coach using MediaPipe Pose to track 33 body keypoints and TensorFlow Lite to run fast, offline AI on Arm devices. We calculate joint angles, detect movement patterns, and give real-time form feedback. The UI overlays the pose skeleton, counts reps, and updates instantly. Everything is optimized to run smoothly on-device with no internet required.

Challenges we ran into

One of the biggest challenges was achieving smooth, real-time pose detection on mobile hardware without draining performance. We had to optimize MediaPipe and TFLite models, reduce latency, and fine-tune angle calculations for accurate feedback. Ensuring stable detection in different lighting conditions and camera angles was another difficulty. Building a clean UI that updates instantly while keeping everything offline also required careful testing and optimization.

Accomplishments that we're proud of

One of the biggest challenges was achieving smooth, real-time pose detection on mobile hardware without draining performance. We had to optimize MediaPipe and TFLite models, reduce latency, and fine-tune angle calculations for accurate feedback. Ensuring stable detection in different lighting conditions and camera angles was another difficulty. Building a clean UI that updates instantly while keeping everything offline also required careful testing and optimization.

What we learned

We learned how powerful on-device AI can be when models are optimized correctly for Arm hardware. Working with MediaPipe and TensorFlow Lite taught us a lot about real-time pose tracking, quantization, and reducing latency. We also gained experience in handling camera input, calculating joint angles, and designing UI that responds instantly. Overall, we discovered how to turn complex AI workflows into something fast, efficient, and fully offline on mobile devices.

What's next for AI Fitness Coach

Next, we plan to add support for more exercises, improve form analysis accuracy, and introduce personalized workout plans. We also aim to make the app adapt to different body types and experience levels. In the future, we’d like to add voice feedback, progress tracking, and smarter pose correction using more advanced on-device models—all while keeping the experience fully offline and optimized for Arm devices.

Built With

• apikey
• openai