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.

翻译：良好的姿势和形态对于安全有效的锻炼至关重要。即使在健身房环境中，教练也可能无法随时提供反馈。因此，康复治疗和健身训练可以从提供实时评估的推荐系统中受益。本文提出了一种算法流程，能够以高灵敏度和特异性实时诊断运动技术中的问题并提出纠正性建议。我们使用MediaPipe进行姿态识别，利用峰值显著性检测计数重复次数，并使用可学习物理模拟器追踪每项运动的运动演变。通过统计学习，基于与原型学习运动的偏差对测试视频进行诊断。该系统在六项全身和上肢运动上进行了评估。这些通过智能手机等低成本设备提供的实时建议，将使锻炼者能够纠正潜在错误，从而实现自我练习，同时降低运动损伤的风险。