Related papers: Design of Recognition and Evaluation System for Table Tennis Players' Motor Skills Based on Artificial Intelligence

Design of Recognition and Evaluation System for Table Tennis Players' Motor Skills Based on Artificial Intelligence

URL: http://arxiv.org/abs/2309.07141v1
Date: Mon, 4 Sep 2023 14:58:56 GMT
Title: Design of Recognition and Evaluation System for Table Tennis Players' Motor Skills Based on Artificial Intelligence
Authors: Zhuo-yong Shi, Ye-tao Jia, Ke-xin Zhang, Ding-han Wang, Long-meng Ji, and Yong Wu
Abstract summary: This paper improves wearable devices of table tennis sport, and realizes the pattern recognition and evaluation of table tennis players' motor skills through artificial intelligence. A sliding window is made to divide the collected motion data into a characteristic database of six table tennis benchmark movements. The hierarchical evaluation system of motor skills is established with the loss functions of different evaluation indexes.
Score: 3.4701250324316146
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
Abstract: With the rapid development of electronic science and technology, the research on wearable devices is constantly updated, but for now, it is not comprehensive for wearable devices to recognize and analyze the movement of specific sports. Based on this, this paper improves wearable devices of table tennis sport, and realizes the pattern recognition and evaluation of table tennis players' motor skills through artificial intelligence. Firstly, a device is designed to collect the movement information of table tennis players and the actual movement data is processed. Secondly, a sliding window is made to divide the collected motion data into a characteristic database of six table tennis benchmark movements. Thirdly, motion features were constructed based on feature engineering, and motor skills were identified for different models after dimensionality reduction. Finally, the hierarchical evaluation system of motor skills is established with the loss functions of different evaluation indexes. The results show that in the recognition of table tennis players' motor skills, the feature-based BP neural network proposed in this paper has higher recognition accuracy and stronger generalization ability than the traditional convolutional neural network.

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