Suppr超能文献

在足球动态环境中增强生物物理肌肉疲劳模型

Enhancing Biophysical Muscle Fatigue Model in the Dynamic Context of Soccer.

作者信息

Skoki Arian, Ivić Stefan, Ljubic Sandi, Lerga Jonatan, Štajduhar Ivan

机构信息

Department of Computer Engineering, Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia.

Department of Fluid Mechanics, Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia.

出版信息

Sensors (Basel). 2024 Dec 19;24(24):8128. doi: 10.3390/s24248128.

Abstract

In the field of muscle fatigue models (MFMs), the prior research has demonstrated success in fitting data in specific contexts, but it falls short in addressing the diverse efforts and rapid changes in exertion typical of soccer matches. This study builds upon the existing model, aiming to enhance its applicability and robustness to dynamic demand shifts. The objective is to encapsulate the complexities of soccer dynamics with a streamlined set of parameters. Our refined model achieved a slight improvement in the R2 score in the maximum hand-grip test, increasing from 0.87 to 0.89 compared to the existing model. It also demonstrated dynamic change robustness in a soccer-specific 1 min drill and 15 min treadmill protocol extracted from the literature. Through individualized fitting on a 10-repetition 80 m sprint test for a soccer player, the model exhibited R2 scores between 0.62 and 0.80. Furthermore, when tested with actual soccer match data, it maintained a robust performance, with the average R2 scores ranging from 0.70 to 0.72. The proposed approach holds the potential to advance the understanding of tactical decisions by correlating them with real-time physical performance, offering opportunities for more informed strategies and ultimately enhancing team performance.

摘要

在肌肉疲劳模型(MFMs)领域,先前的研究已证明在特定情况下拟合数据是成功的,但在应对足球比赛中典型的多样化努力和快速变化的 exertion 方面存在不足。本研究基于现有模型进行构建,旨在提高其对动态需求变化的适用性和稳健性。目标是用一组简化的参数来概括足球动态的复杂性。我们改进后的模型在最大握力测试中的 R2 得分略有提高,与现有模型相比,从 0.87 提高到了 0.89。它还在从文献中提取的特定于足球的 1 分钟训练和 15 分钟跑步机协议中展示了动态变化稳健性。通过对一名足球运动员的 10 次重复 80 米冲刺测试进行个性化拟合,该模型的 R2 得分在 0.62 至 0.80 之间。此外,当用实际足球比赛数据进行测试时,它保持了稳健的性能,平均 R2 得分在 0.70 至 0.72 之间。所提出的方法有可能通过将战术决策与实时身体表现相关联来推进对战术决策的理解,为更明智的策略提供机会,并最终提高团队表现。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/73f7/11678957/530bea70804d/sensors-24-08128-g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验