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

IF 1.8 3区医学 Q4 ENGINEERING, BIOMEDICAL

Clinical Biomechanics Pub Date : 2025-12-01 Epub Date: 2025-06-20 DOI:10.1016/j.clinbiomech.2025.106600

Maxence Duffuler , Maxime Bourgain , Zehira Haddad , Renaud Heraud , Sylvain Blanchard , Philippe Rouch

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引用次数: 0

Abstract

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.

查看原文本刊更多论文

职业橄榄球联盟俱乐部损伤预防的循环神经网络模型：一个赛季的概念验证。

背景：在职业橄榄球中，伤病预防和球员可用性是主要的挑战。体育分析使用来自训练和比赛的数据来解决这些问题。这项研究利用了一个专业橄榄球俱乐部的综合日常数据来预测球员的训练准备情况。使用该指标有助于评估其在预测内源性损伤和改进损伤预防策略方面的有效性。方法：模型包括逻辑回归、决策树和基于长短期记忆的神经网络，评估其预测准确性和识别指示损伤风险或体力活动准备模式的能力。研究结果：研究表明，长短期记忆和卷积一维模型在分析球员身体状况方面优于传统的机器学习方法。这种方法可以支持早期识别伤害风险，并为工作量管理提供信息。利用模型评估和可解释性技术，包括局部可解释模型不可知论解释（LIME）模块，该研究为体育科学家、教练和医务人员提供了一个框架，以减轻损伤风险并优化训练课程。解释：作为初步探索，本研究为进一步研究机器学习和神经网络在运动科学中的整合铺平了道路，有望对橄榄球伤害预防策略产生变革性影响。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Clinical Biomechanics 医学-工程：生物医学

CiteScore

3.30

自引率

5.60%

发文量

189

审稿时长

12.3 weeks

期刊介绍： Clinical Biomechanics is an international multidisciplinary journal of biomechanics with a focus on medical and clinical applications of new knowledge in the field. The science of biomechanics helps explain the causes of cell, tissue, organ and body system disorders, and supports clinicians in the diagnosis, prognosis and evaluation of treatment methods and technologies. Clinical Biomechanics aims to strengthen the links between laboratory and clinic by publishing cutting-edge biomechanics research which helps to explain the causes of injury and disease, and which provides evidence contributing to improved clinical management. A rigorous peer review system is employed and every attempt is made to process and publish top-quality papers promptly. Clinical Biomechanics explores all facets of body system, organ, tissue and cell biomechanics, with an emphasis on medical and clinical applications of the basic science aspects. The role of basic science is therefore recognized in a medical or clinical context. The readership of the journal closely reflects its multi-disciplinary contents, being a balance of scientists, engineers and clinicians. The contents are in the form of research papers, brief reports, review papers and correspondence, whilst special interest issues and supplements are published from time to time. Disciplines covered include biomechanics and mechanobiology at all scales, bioengineering and use of tissue engineering and biomaterials for clinical applications, biophysics, as well as biomechanical aspects of medical robotics, ergonomics, physical and occupational therapeutics and rehabilitation.