Train
Smarter
A machine learning fitness app that watches you exercise through your phone camera. The AI analyzes your form in real-time, counts your reps, and adjusts workout difficulty based on your performance.
The Challenge
We built an app that uses Google MLKit to track body pose through the camera. The system analyzes movement accuracy, counts repetitions automatically, and adapts exercise difficulty based on user performance.
Core Architecture
We engineered a real-time pose detection pipeline that processes camera frames, calculates joint angles, and delivers instant feedback to guide users through complex physiotherapy routines.
ML-Powered Pose Tracking
Google MLKit analyzes body position through the camera, tracking joint angles and movement patterns to measure exercise accuracy in real-time.
Realtime Feedback & Reps
The system counts repetitions automatically and provides instant audio and visual cues when form breaks down, helping users maintain correct technique.
Adaptive Workouts
Exercise difficulty adjusts automatically based on performance metrics. The system increases intensity when users master movements and reduces load when form deteriorates.
The Impact
Real-time AI feedback and adaptive difficulty transformed user outcomes across movement accuracy, workout efficiency, and program completion.
ML pose tracking scored user form against reference movements in real time, correcting posture mid-set.
Adaptive difficulty adjusted reps and rest based on fatigue signals. Users reached training goals faster.
Nine out of ten users who started a program completed it. Personalized plans kept motivation high.
Post-workout cooldown routines adapted to muscle group usage, reducing soreness and downtime.
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