Using Learnable Physics for Real-Time Exercise Form Recommendations

TL;DR

This paper introduces a real-time exercise form recommendation system using learnable physics and pose recognition to diagnose and correct exercise techniques, aiming to improve safety and effectiveness in fitness and rehabilitation.

Contribution

It presents a novel pipeline combining pose recognition, motion tracking, and a learnable physics simulator for real-time exercise evaluation and correction.

Findings

High sensitivity and specificity in diagnosing exercise issues

Effective real-time recommendations using low-cost devices

Validated on six different exercises

Abstract

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.