Performance of long short-term memory networks in predicting athlete injury risk

被引:0
|
作者
Tao, Hong [1 ]
Deng, Yue [1 ]
Xiang, Yunqiu [1 ]
Liu, Long [1 ]
机构
[1] Chongqing Presch Educ Coll, Sch Phys Educ, Chongqing 404047, Peoples R China
关键词
Athlete injury; risk prediction; long short-term memory network; performance analysis; temporal dependence; LSTM; ALGORITHM; AREA;
D O I
10.3233/JCM-247563Press
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Conventional approaches to forecasting the risk of athlete injuries are constrained by their narrow scope in feature extraction, often failing to adequately account for temporal dependencies and the effects of long-term memory. This paper enhances the Long Short-Term Memory (LSTM) network, specifically tailoring it to harness temporal data pertaining to athletes. This advancement significantly boosts the accuracy and effectiveness of predicting the risk of injuries among athletes. The network structure of the LSTM model was improved, and the collected data was converted into the temporal data form of the LSTM input. Finally, historical data labeled with injury labels were used to train the improved LSTM model, and gradient descent iterative optimization was used to adjust the parameters of the improved LSTM model. The improved LSTM network model was compared with the traditional athlete injury risk prediction model in terms of performance. The incorporation of enhanced LSTM networks for the analysis of temporal athlete data holds significant research significance. This approach has the potential to substantially enhance the accuracy and effectiveness of athlete injury risk prediction, contributing to a deeper understanding of the temporal dynamics influencing injuries in sports.
引用
收藏
页码:3155 / 3171
页数:17
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