在足球的动态环境中增强生物物理肌肉疲劳模型
Arian Skoki1, Stefan Ivić2, Sandi Ljubic1,3
1Department of Computer Engineering, Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia.
Sensors (Basel, Switzerland)
|January 8, 2025
概括
这项研究改进了足球的肌肉疲劳模型 (MFMs),提高了在动态活动期间预测球员炼的准确性. 改进的模型更好地捕捉足球的需求,帮助战术决策和团队表现.
科学领域:
- 运动科学 运动科学 运动科学
- 生物力学 生物力学
- 运动生理学 运动生理学
背景情况:
- 现有的肌肉疲劳模型 (MFMs) 难以应对足球中的动态运动水平.
- 之前的研究表明,在比赛期间捕捉物理需求快速变化的局限性.
研究的目的:
- 增强现有的MFMs,以提高足球的稳定性和适用性.
- 开发一个精简的模型,封装足球特定的动态.
- 以实时物理性能与战术决策相关联.
主要方法:
- 改进了一个现有的肌肉疲劳模型.
- 在最大手柄测试,足球特有的钻头,跑步机协议和冲刺测试中测试了该模型.
- 使用实际足球比赛数据验证了模型.
主要成果:
- 在最大手柄测试中,从0.87到0.89实现了轻微的R2改善.
- 在足球特定的练习和跑步机协议中表现出对动态变化的强度.
- 在冲刺测试中展示了个性化的装配,R2得分从0.62到0.80.
- 在足球比赛数据上保持了强的表现,平均R2分数为0.700.72.
结论:
- 精致的MFM为足球特定的身体需求提供了更高的精度.
- 该模型能够处理动态劳动转换的能力提高了其实际应用.
- 这种方法可以通过将其与实时球员表现联系起来来告知战术决策,从而潜在地提高团队战略和结果.
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