Design of Recognition and Evaluation System for Table Tennis Players' Motor Skills Based on Artificial Intelligence
This work addresses the need for comprehensive wearable device analysis in specific sports like table tennis, though it is incremental as it improves existing devices and methods.
The paper tackled the problem of recognizing and evaluating table tennis players' motor skills using wearable devices and AI, achieving higher recognition accuracy and stronger generalization with a feature-based BP neural network compared to traditional convolutional neural networks.
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.