Citation

Zhang C, Tu F. Comput. Intell. Neurosci. 2022; 2022: e2432309.

Copyright

(Copyright © 2022, Hindawi Publishing)

DOI

10.1155/2022/2432309

PMID

35875735

PMCID

PMC9303085

Abstract

In recent years, China's competitive sports have developed rapidly. Among them, football is a sport with high energy consumption, high intensity, strong antagonism, and high speed. As a result, injuries are common in football practice. It is impossible to do adequate football training in order to avoid such incidents. At the moment, football injury incidents are common, severely limiting the full growth of the sport in China. This work investigates the safety management of football training using machine learning and an information coverage-centralized genetic technique. To begin, this article describes in detail the machine learning and information coverage-centralized genetic algorithm, summarizes the classification of machine learning models, and introduces the verification and evaluation process of machine learning models and trusted information coverage models as an important theoretical basis for football training safety management. Then, the genetic algorithm based on information coverage concentration is used in football training to analyze the safety risk of football training and the analysis of training speed type. The results show that the human factor accounts for the highest proportion in football training safety accidents, accounting for 28.75%. In the analysis of football training speed, the average passing time of medium strength accounts for the highest proportion of 39.75%. In football training, in order to ensure the safety of training, the combination of medium strength and high strength can be adopted to avoid training injury.

Keywords: Soccer; Canadian football

Language: en

Keywords

Humans; Safety Management; *Athletic Injuries; *Football/injuries; Genetic Techniques; Machine Learning