3D Pose Based Feedback for Physical Exercises

• URL: http://arxiv.org/abs/2208.03257v1
• Date: Fri, 5 Aug 2022 16:15:02 GMT
• Title: 3D Pose Based Feedback for Physical Exercises
• Authors: Ziyi Zhao, Sena Kiciroglu, Hugues Vinzant, Yuan Cheng, Isinsu Katircioglu, Mathieu Salzmann, Pascal Fua
• Abstract summary: We introduce a learning-based framework that identifies the mistakes made by a user. Our framework does not rely on hard-coded rules, instead, it learns them from data. Our approach yields 90.9% mistake identification accuracy and successfully corrects 94.2% of the mistakes.
• Score: 87.35086507661227
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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