Cargando…
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...
Autores principales: | , , |
---|---|
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 |
_version_ | 1784768423676346368 |
---|---|
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. |
format | Online Article Text |
id | pubmed-9377869 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT liuzexu sportsinjuryidentificationmethodbasedonmachinelearningmodel AT zhangjun sportsinjuryidentificationmethodbasedonmachinelearningmodel AT wudi sportsinjuryidentificationmethodbasedonmachinelearningmodel |