Footprint Extraction and Sports Training Action Recognition Based on Wireless Network Communication

Abstract

The combination of scientific and technological achievements and sports has found new opportunities to change people’s sports habits. Sport training takes up an increasing proportion of people’s lives. In order to improve the efficiency of sports training and standardize the training actions of players, this article is based on wireless network communication and uses different types of recognition methods in the field of action recognition to build basic classifications. Iterative mutual training to improve generalization performance can reduce the cost of labeling and realize the complementary advantages of different recognition methods, thereby improving the recognition accuracy of human actions. Finally, the algorithm is used to recognize human movements. This method can effectively overcome the problem of differential degradation of base classifiers in the iterative process of collaborative training and further improve the accuracy of human action recognition. The experimental results prove that the motion recognition of wireless network communication proposed in this paper can effectively improve the accuracy of athletes’ movements, which is more than 20% higher than traditional methods, and, under the guidance of standardized movements, can reduce athletes’ sports injuries.

Footprint Extraction and Sports Dance Action Recognition Method Based on Artificial Intelligence Distributed Edge Computing

“…It can be seen from Figure 7 that the overall mean of the extraction accuracy of the footprint morphological features of the sample data by the recognition method based on edge computing technology is 98.98 [29,30]. The overall mean of the extraction accuracy of the footprint morphological features of the sample data by the identification method based on traditional technology is 86.84 [31,32] It can be seen from Figure 8 that the overall mean of the extraction accuracy of the footprint length feature of the sample data by the identification method based on edge computing technology is 99.12. The overall mean of the extraction accuracy of the footprint length feature of the sample data by the identification method based on traditional technology is 87.63.…”