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3D Pose Based Feedback For Physical Exercises

Zhao, Ziyi
•
Kiciroglu, Sena  
•
Vinzant, Hugues
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November 24, 2022
Proceedings of the Asian Conference on Computer Vision (ACCV)
16th Asian Conference on Computer Vision (ACCV 2022)

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 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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Zhao_3D_Pose_Based_Feedback_For_Physical_Exercises_ACCV_2022_paper.pdf

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