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
相关论文
共 50 条
  • [1] Machine Learning in Education: Predicting Student Performance Using Long Short-Term Memory Networks
    Alanya-Beltran, Joel
    2024 INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING, COMMUNICATION AND APPLIED INFORMATICS, ACCAI 2024, 2024,
  • [2] ANALYSIS AND COMPARISON OF LONG SHORT-TERM MEMORY NETWORKS SHORT-TERM TRAFFIC PREDICTION PERFORMANCE
    Dogan, Erdem
    SCIENTIFIC JOURNAL OF SILESIAN UNIVERSITY OF TECHNOLOGY-SERIES TRANSPORT, 2020, 107 : 19 - 32
  • [3] Early Prediction of Pressure Injury with Long Short-term Memory Networks
    Fang, Xudong
    Wang, Yunfeng
    Maeda, Ryutaro
    Kitayama, Akio
    Takashi, En
    SENSORS AND MATERIALS, 2022, 34 (07) : 2759 - 2769
  • [4] Predicting Marimba Stickings Using Long Short-Term Memory Neural Networks
    Chong, Jet Kye
    Correa, Debora
    AI 2022: ADVANCES IN ARTIFICIAL INTELLIGENCE, 2022, 13728 : 339 - 352
  • [5] On the Initialization of Long Short-Term Memory Networks
    Ghazi, Mostafa Mehdipour
    Nielsen, Mads
    Pai, Akshay
    Modat, Marc
    Cardoso, M. Jorge
    Ourselin, Sebastien
    Sorensen, Lauge
    NEURAL INFORMATION PROCESSING (ICONIP 2019), PT I, 2019, 11953 : 275 - 286
  • [6] Evolving Long Short-Term Memory Networks
    Neto, Vicente Coelho Lobo
    Passos, Leandro Aparecido
    Papa, Joao Paulo
    COMPUTATIONAL SCIENCE - ICCS 2020, PT II, 2020, 12138 : 337 - 350
  • [7] Long Short-Term Memory for Predicting Firemen Interventions
    Nahuis, Selene Leya Cerna
    Guyeux, Christophe
    Arcolezi, Heber Hwang
    Couturier, Raphael
    Royer, Guillaume
    Lotufo, Anna Diva Plasencia
    2019 6TH INTERNATIONAL CONFERENCE ON CONTROL, DECISION AND INFORMATION TECHNOLOGIES (CODIT 2019), 2019, : 1132 - 1137
  • [8] Predicting machine's performance record using the stacked long short-term memory (LSTM) neural networks
    Ma, Min
    Liu, Chenbin
    Wei, Ran
    Liang, Bin
    Dai, Jianrong
    JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS, 2022, 23 (03):
  • [9] Bidirectional Long Short-Term Memory Networks for predicting the subcellular localization of eukaryotic proteins
    Thireou, Trias
    Reczko, Martin
    IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, 2007, 4 (03) : 441 - 446
  • [10] Predicting Surface Air Temperature Using Convolutional Long Short-Term Memory Networks
    Wagle, Sanket
    Uttamani, Saral
    Dsouza, Sasha
    Devadkar, Kailas
    ICCCE 2019: PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON COMMUNICATIONS AND CYBER-PHYSICAL ENGINEERING, 2020, 570 : 183 - 188