Design of Recognition and Evaluation System for Table Tennis Players'
  Motor Skills Based on Artificial Intelligence
Zhuo-yong Shi Ye-tao Jia Ke-xin Zhang Ding-han Wang Long-meng Ji Yong Wu
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.