Application of Intelligent Sensor Network in the Assessment of Table Tennis Teaching and Training Intensity, Training Volume, and Physical Fitness

Abstract

Sensor technology has been deeply into the sports industry, with the help of sensors to monitoring and collection data for the physical training in real-time. In table tennis, wearable sensors can record the amount of training, movement essentials, and the number of strokes of both hands and assist in the testing and evaluation of table tennis. This paper analyzes the application of wearable sensors in table tennis training activities and the details of signal collection and feature extraction. At the same time, machine learning technology often used to recognize and test table tennis training data, and a support vector machine (SVM) is one of the representative classifiers. Applying the processed signal data to the classification and testing of SVM can effectively identify the movement and evaluate the training effect and athletes’ physical fitness. The integration of intelligent sensors and table tennis can effectively improve the evaluation efficiency and quality in the process of teaching and training.