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Edge Computing-Based Athletic Ability Testing for Sports.

Authors :
Chen Yang
Hui Ma
Source :
EAI Endorsed Transactions on Scalable Information Systems; 2024, Vol. 11 Issue 3, p1-11, 11p
Publication Year :
2024

Abstract

INTRODUCTION: After the 2008 Olympic Games, China has gradually become a prominent sports country, but there is still a certain distance from a sports power. China should improve the level of sports ability testing while continuously strengthening the construction of sports power. At present, the method of sports professional athletic ability tests in China can not be better combined with algorithms, so it is crucial to study the athletic ability test of edge computing. OBJECTIVES: To improve the ability of sports testing of sports majors in China, to improve the technical level of the construction of China's sports power, to solve the problem that China's sports ability testing cannot be better combined with algorithms, and to solve the problem that China's physical education disciplines cannot be well applied to computer technology. METHODS: Use the motor function theory and edge computing to establish the model needed, test the athletic ability of swimming sports according to the model, and analyze the advanced level and shortcomings of China's swimming sports with measurement according to the results of the athletic ability test. RESULTS: Firstly, edge computing and other algorithms are more accurate for professional athletic ability testing of swimming sports, and improving the iteration level of algorithms can improve the problem of the inconspicuous effect of sports testing; secondly, edge algorithms combined with traditional testing tools can calculate athletic ability more accurately in athletic ability testing. CONCLUSION: China should vigorously improve the level of edge computing and other algorithms to improve the problem of China's sports disciplines not being able to apply computer technology well and technically improve the level of sports training. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20329407
Volume :
11
Issue :
3
Database :
Complementary Index
Journal :
EAI Endorsed Transactions on Scalable Information Systems
Publication Type :
Academic Journal
Accession number :
175950186
Full Text :
https://doi.org/10.4108/eetsis.4730