Pose Tutor: An Explainable System for Pose Correction in the Wild

Under the new norm of working from home, demand for fitness from home is on the rise. Different exercise forms solve different fitness needs for different people. Yoga gives flexibility and relieves stress. Pilates strengthens the muscles. Kung Fu brings balance. It is not feasible for everyone to h...

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Bibliographic Details
Published in:	2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) pp. 3539 - 3548
Main Authors:	Dittakavi, Bhat, Bavikadi, Divyagna, Desai, Sai Vikas, Chakraborty, Soumi, Reddy, Nishant, Balasubramanian, Vineeth N, Callepalli, Bharathi, Sharma, Ayon
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01-06-2022
Subjects:	Computer vision Conferences Muscles Pattern recognition Predictive models Skeleton Training
Online Access:	Get full text
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Summary:	Under the new norm of working from home, demand for fitness from home is on the rise. Different exercise forms solve different fitness needs for different people. Yoga gives flexibility and relieves stress. Pilates strengthens the muscles. Kung Fu brings balance. It is not feasible for everyone to hire a personal trainer. In this paper, we develop Pose Tutor, an AI-based explainable pose recognition and correction system. Pose Tutor combines vision and pose skeleton models in a novel coarse-to-fine framework to obtain pose class predictions. An angle-likelihood mechanism is used to explain which human joints maximally caused the pose class predictions and also correct any wrongly formed joints. Even without keypoint level training, Pose Tutor shows promising results on Yoga-82, Pilates-32, and Kungfu-7 datasets. Additionally, user studies conducted with multiple domain experts validate the explanations provided by our framework.
ISSN:	2160-7516
DOI:	10.1109/CVPRW56347.2022.00398