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arXiv · 2208.03257

3D Pose Based Feedback for Physical Exercises

Ziyi Zhao·Sena Kiciroglu·Hugues Vinzant·Yuan Cheng·Isinsu Katircioglu·Mathieu Salzmann·Pascal Fua
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Abstract

Unsupervised self-rehabilitation exercises and physical training can cause serious injuries if performed incorrectly. We introduce a learning-based framework that identifies the mistakes made by a user and proposes corrective measures for easier and safer individual training. Our framework does not rely on hard-coded, heuristic rules. Instead, it learns them from data, which facilitates its adaptation to specific user needs. To this end, we use a Graph Convolutional Network (GCN) architecture acting on the user's pose sequence to model the relationship between the body joints trajectories. To evaluate our approach, we introduce a dataset with 3 different physical exercises. Our approach yields 90.9% mistake identification accuracy and successfully corrects 94.2% of the mistakes.

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Ziyi Zhao, Sena Kiciroglu, Hugues Vinzant, Yuan Cheng, Isinsu Katircioglu, Mathieu Salzmann, Pascal Fua. 2022-08-05. 3D Pose Based Feedback for Physical Exercises. https://arxiv.org/abs/2208.03257

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