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http://dx.doi.org/10.6109/jkiice.2021.25.9.1183

Pose Classification and Correction System for At-home Workouts  

Kang, Jae Min (School of Computer Science and Engineering, Pusan National University)
Park, Seongsu (Department of Information Convergence Engineering, Pusan National University)
Kim, Yun Soo (Department of Information Convergence Engineering, Pusan National University)
Gahm, Jin Kyu (School of Computer Science and Engineering, Pusan National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.25, no.9, 2021 , pp. 1183-1189 More about this Journal
Abstract
There have been recently an increasing number of people working out at home. However, many of them do not have face-to-face guidance from experts, so they cannot effectively correct their wrong pose. This may lead to strain and injury to those doing home training. To tackle this problem, this paper proposes a video data-based pose classification and correction system for home training. The proposed system classifies poses using the multi-layer perceptron and pose estimation model, and corrects poses based on joint angels estimated. A voting algorithm that considers the results of successive frames is applied to improve the performance of the pose classification model. Multi-layer perceptron model for post classification shows the highest accuracy with 0.9. In addition, it is shown that the proposed voting algorithm improves the accuracy to 0.93.
Keywords
Machine learning; Pose classification; Pose correction; Video recognition; At-home workouts;
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