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. 2022 Aug 8:2022:2794851.
doi: 10.1155/2022/2794851. eCollection 2022.

Sports Injury Identification Method Based on Machine Learning Model

Zexu Liu  1 , Jun Zhang  1 , Di Wu  1
Affiliations

Sports Injury Identification Method Based on Machine Learning Model

Zexu Liu et al. Comput Intell Neurosci. .

Retraction in

Abstract

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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Conflict of interest statement

The authors declare that there are no conflicts of interest.

Figures

Figure 1
Figure 1
Four steps to prevent sports injuries.
Figure 2
Figure 2
Causes of sports injuries.
Figure 3
Figure 3
Convolution operation process.
Figure 4
Figure 4
The max pooling process.
Figure 5
Figure 5
Function image.
Figure 6
Figure 6
Optimal interface for linearly separable samples.
Figure 7
Figure 7
Linear inseparable sample distribution.
Figure 8
Figure 8
Ranking figure of the total risk of acute sports injury events for high-level gymnasts.
Figure 9
Figure 9
Comparison of risk amounts of five types of risks in five projects. (a) The comparison figure of the risk amount of the five risk categories between men's vaulting and women's vaulting. (b) The comparison figure of the risk amount of the five risk types of the pommel horse, lifting ring, parallel bars, and balance beam projects.
Figure 10
Figure 10
Ranking diagram of the amount of risk brought by athletes themselves.
Figure 11
Figure 11
Risk ranking diagram from environmental aspects.
See this image and copyright information in PMC

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