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ORIGINAL RESEARCH article

Front. Digit. Health
Sec. Health Informatics
Volume 6 - 2024 | doi: 10.3389/fdgth.2024.1503831
This article is part of the Research Topic Unveiling Complex Medical Interdependencies Through High-Order Correlation Mining View all 5 articles

Male-Assisted Training and Injury Patterns: Hypergraph-Enhanced Analysis of Injuries in Women's Water Polo

Provisionally accepted
Xuehui Feng Xuehui Feng 1,2*Zhibin Wang Zhibin Wang 1,2*Zheng Wang Zheng Wang 1,2*Chen He Chen He 1,2*Hongxing Xun Hongxing Xun 3*Yuanfa Chen Yuanfa Chen 4*Jie Ding Jie Ding 1,2*Gen Chen Gen Chen 1,2*Zhe Liu Zhe Liu 1,2*Yang Jiahui Yang Jiahui 5
  • 1 Key Laboratory of Sports Trauma and Rehabilitation of General Administration of Sport of the People's Republic of China, Beijing, China
  • 2 National Research Institute of Sports Medicine (NRISM), Beijing, China
  • 3 Hunan Institute of Sports Science, Hunan, China
  • 4 Guangxi Sports Trauma Hospital, Guangxi, China
  • 5 Harbin Institute of Technology, Harbin, China

The final, formatted version of the article will be published soon.

    The aim of this study is to compare the injury patterns of female water polo players before and after the implementation of the Male-Assisted Female Training (MAFT) program. By exploring from the perspectives of pattern analysis and classification, we comprehensively investigate the key factors influencing these changes in water polo athletes and propose corresponding countermeasures. In terms of pattern analysis, we employed a Hypergraph Neural Network (HGNN) for pattern extraction. We constructed a hypergraph from the initialized raw data, where each node represents an athlete and the node dimensions represent the corresponding highorder relational embedding information. Through the use of the graph Laplacian operator to aggregate neighborhood features, we ultimately visualized the structural and feature differences of the hypergraphs constructed under different influencing factors, identifying the key influencing factors. This greatly supported the preventive measures we proposed for the MAFT training mode.While pattern recognition analysis provides insights into the key factors causing differences, it is challenging to analyze the critical factors leading to different types of injuries. Therefore, from this perspective, we also explored the influencing factors of injury types before and after the MAFT mode from the classification accuracy standpoint. Our approach focuses on improving classification accuracy by introducing graph structure regularization techniques to prevent overfitting on our relatively small dataset. This enhancement allows us to more effectively identify key features that distinguish changes in injury types. From these identified key features, we further analyze the preventive measures to be taken before and after the implementation of the MAFT program.

    Keywords: Hypergraph, High-order Connection, injury patterns, Women's Water Polo, Male-Assisting-Female-Training

    Received: 29 Sep 2024; Accepted: 14 Nov 2024.

    Copyright: © 2024 Feng, Wang, Wang, He, Xun, Chen, Ding, Chen, Liu and Jiahui. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Xuehui Feng, Key Laboratory of Sports Trauma and Rehabilitation of General Administration of Sport of the People's Republic of China, Beijing, China
    Zhibin Wang, Key Laboratory of Sports Trauma and Rehabilitation of General Administration of Sport of the People's Republic of China, Beijing, China
    Zheng Wang, Key Laboratory of Sports Trauma and Rehabilitation of General Administration of Sport of the People's Republic of China, Beijing, China
    Chen He, Key Laboratory of Sports Trauma and Rehabilitation of General Administration of Sport of the People's Republic of China, Beijing, China
    Hongxing Xun, Hunan Institute of Sports Science, Hunan, China
    Yuanfa Chen, Guangxi Sports Trauma Hospital, Guangxi, China
    Jie Ding, Key Laboratory of Sports Trauma and Rehabilitation of General Administration of Sport of the People's Republic of China, Beijing, China
    Gen Chen, Key Laboratory of Sports Trauma and Rehabilitation of General Administration of Sport of the People's Republic of China, Beijing, China
    Zhe Liu, Key Laboratory of Sports Trauma and Rehabilitation of General Administration of Sport of the People's Republic of China, Beijing, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.