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Recurrent Neural Networks model for injury prevention within a professional rugby union club: a proof of concept over one season

Article dans une revue avec comité de lecture
Auteur
DUFFULER, Maxence
1001017 Institut de Biomécanique Humaine Georges Charpak [IBHGC]
ccBOURGAIN, Maxime
1001017 Institut de Biomécanique Humaine Georges Charpak [IBHGC]
ccHADDAD, Zehira
HERAUD, Renaud
ccBLANCHARD, Sylvain
581100 Laboratoire de Biomécanique Appliquée [LBA UMR T24]
ccROUCH, Philippe
1001017 Institut de Biomécanique Humaine Georges Charpak [IBHGC]

URI
http://hdl.handle.net/10985/26561
DOI
10.1016/j.clinbiomech.2025.106600
Date
2025-06
Journal
Clinical Biomechanics

Résumé

Background In professional rugby, injury prevention and player availability are major challenges. Sports analytics use data from trainings and matches to address these issues. This study leveraged comprehensive daily data from a professional rugby club to predict players' readiness for training. Using this metric helped assess its effectiveness in predicting intrinsic injuries and improving injury prevention strategies. Methods Models including logistic regression, decision trees, and Long Short-Term Memory-based neural networks, were evaluated for their predictive accuracy and ability to discern patterns indicative of injury risks or readiness for physical activities. Findings The study demonstrated that long-short term memory and convolutional one-dimension models outperform traditional machine learning methods in analyzing players' physical conditions. This approach may support earlier identification of injury risks and inform workload management. Using model evaluation and interpretability techniques, including Local Interpretable Model-Agnostic Explanations (LIME) module, the study provided a framework for sports scientists, coaches, and medical staff to mitigate injury risks and optimize training sessions. Interpretation As a preliminary exploration, this study paves the way for further research into the integration of machine learning and neural networks in sports science, promising transformative impacts on injury prevention strategies in rugby.

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  • Institut de Biomécanique Humaine Georges Charpak (IBHGC)

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