Using Learnable Physics for Real-Time Exercise Form Recommendations
This addresses the need for accessible, real-time guidance in fitness and rehabilitation to reduce injury risks and enable self-practice, though it is incremental in applying existing methods to a new domain.
The paper tackles the problem of providing real-time feedback on exercise form by developing a pipeline that diagnoses technique issues and offers corrective recommendations, achieving high sensitivity and specificity in evaluations on six exercises.
Good posture and form are essential for safe and productive exercising. Even in gym settings, trainers may not be readily available for feedback. Rehabilitation therapies and fitness workouts can thus benefit from recommender systems that provide real-time evaluation. In this paper, we present an algorithmic pipeline that can diagnose problems in exercise techniques and offer corrective recommendations, with high sensitivity and specificity in real-time. We use MediaPipe for pose recognition, count repetitions using peak-prominence detection, and use a learnable physics simulator to track motion evolution for each exercise. A test video is diagnosed based on deviations from the prototypical learned motion using statistical learning. The system is evaluated on six full and upper body exercises. These real-time recommendations, counseled via low-cost equipment like smartphones, will allow exercisers to rectify potential mistakes making self-practice feasible while reducing the risk of workout injuries.