Recognition of Hitting Action in Cyclic Anaerobic Volleyball by Acute Cooling Based on Improved Spatiotemporal Graph Convolutional Network

Abstract

Action recognition is the basis of intelligent sports training and has a unique role in improving athletes’ sports training ability. Traditional motion recognition cannot accurately identify changes in human skeletal motion, and the generalization ability is weak, which cannot meet the needs of modern sports. This paper is aimed at studying a new method of human motion recognition and better realizing the intelligent development of the Institute of Physical Education. This paper proposes an improved recognition mode of spatiotemporal graph convolutional network, builds a human skeleton feature recognition model, learns the changes of human skeleton motion, and masters the laws of human skeleton motion changes. This paper comprehensively compares the differences of action recognition of several different algorithms and proves the superiority of this method. Afterwards, a comprehensive comparison of 2D and 3D information data and a comparison of whole body and local action recognition methods were conducted to investigate the experience of athletes and trainers. The experimental results of this paper show that the spatiotemporal graph convolutional network can better realize the recognition of motion features, improve the sports training ability of athletes by 20%, and promote the development of sports.