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Sports Injury Identification Method Based on Machine Learning Model

With the increasingly fierce competition in international competitive sports, the momentum of special training has increased. Sports injuries are becoming more and more serious, which restricts the further improvement of the level of athletes. How to solve the problem of prevention, treatment, and r...

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Detalles Bibliográficos
Autores principales: Liu, Zexu, Zhang, Jun, Wu, Di
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9377869/
https://www.ncbi.nlm.nih.gov/pubmed/35978906
http://dx.doi.org/10.1155/2022/2794851
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author Liu, Zexu
Zhang, Jun
Wu, Di
author_facet Liu, Zexu
Zhang, Jun
Wu, Di
author_sort Liu, Zexu
collection PubMed
description With the increasingly fierce competition in international competitive sports, the momentum of special training has increased. Sports injuries are becoming more and more serious, which restricts the further improvement of the level of athletes. How to solve the problem of prevention, treatment, and rehabilitation of sports injuries, so as to ensure the normal training and competition of athletes, is an important part of sports work. Machine learning can solve large-scale data problems that cannot be solved by human beings at present and has strong self-learning ability, self-optimization ability, and strong generalization ability. Therefore, the purpose of this study is to understand the characteristics of rhythmic gymnastics injuries and analyze their causes by investigating the injury status of elite rhythmic gymnasts. According to the characteristics of the project, the injury characteristics of the athletes themselves, and other factors, using scientific qualitative and quantitative indicators, the injury risk of key athletes in rhythmic gymnastics was evaluated. It also provides theoretical and practical references for preventing sports injuries, formulating and implementing sports injury rehabilitation programs. The experimental results show that the female vaulting risk in the five risk categories fluctuates from 179.62 to 365.8, ranking the first in the risk of acute sports injury.
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spelling pubmed-93778692022-08-16 Sports Injury Identification Method Based on Machine Learning Model Liu, Zexu Zhang, Jun Wu, Di Comput Intell Neurosci Research Article With the increasingly fierce competition in international competitive sports, the momentum of special training has increased. Sports injuries are becoming more and more serious, which restricts the further improvement of the level of athletes. How to solve the problem of prevention, treatment, and rehabilitation of sports injuries, so as to ensure the normal training and competition of athletes, is an important part of sports work. Machine learning can solve large-scale data problems that cannot be solved by human beings at present and has strong self-learning ability, self-optimization ability, and strong generalization ability. Therefore, the purpose of this study is to understand the characteristics of rhythmic gymnastics injuries and analyze their causes by investigating the injury status of elite rhythmic gymnasts. According to the characteristics of the project, the injury characteristics of the athletes themselves, and other factors, using scientific qualitative and quantitative indicators, the injury risk of key athletes in rhythmic gymnastics was evaluated. It also provides theoretical and practical references for preventing sports injuries, formulating and implementing sports injury rehabilitation programs. The experimental results show that the female vaulting risk in the five risk categories fluctuates from 179.62 to 365.8, ranking the first in the risk of acute sports injury. Hindawi 2022-08-08 /pmc/articles/PMC9377869/ /pubmed/35978906 http://dx.doi.org/10.1155/2022/2794851 Text en Copyright © 2022 Zexu Liu et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Liu, Zexu
Zhang, Jun
Wu, Di
Sports Injury Identification Method Based on Machine Learning Model
title Sports Injury Identification Method Based on Machine Learning Model
title_full Sports Injury Identification Method Based on Machine Learning Model
title_fullStr Sports Injury Identification Method Based on Machine Learning Model
title_full_unstemmed Sports Injury Identification Method Based on Machine Learning Model
title_short Sports Injury Identification Method Based on Machine Learning Model
title_sort sports injury identification method based on machine learning model
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9377869/
https://www.ncbi.nlm.nih.gov/pubmed/35978906
http://dx.doi.org/10.1155/2022/2794851
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