几种生物力学特征对疲劳在耗尽的跑步机运行过程中的个体特定敏感性
Christos Chalitsios1, Thomas Nikodelis2, Georgios Mavrommatis3
1Biomechanics Laboratory, Department of Physical Education and Sports Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece. cchalits@phed.auth.gr.
Scientific reports
|January 10, 2024
概括
跑步者将他们的运动模式独特地调整以对抗疲劳,上半身运动和负载率是关键指标. 这项研究强调了在疲下保持跑步状态的个人策略.
科学领域:
- 生物力学 生物力学
- 运动生理学 运动生理学
- 机器学习在体育科学中的应用
背景情况:
- 跑步性能随着疲劳而下降,需要补偿运动策略.
- 了解对象特定的疲劳反应对于个性化训练和伤害预防至关重要.
- 以前的研究经常使用组平均值,可能掩盖疲劳适应的个体差异.
研究的目的:
- 以特定对象的方式调查跑步运动特征对疲劳的敏感性.
- 为了确定关键的生物力学变量,随着跑步者接近疲劳而变化.
- 应用机器学习来分析与跑步疲劳相关的动力和动力学数据.
主要方法:
- 13名休跑步运动员在仪器跑步机上进行了详尽的跑步程序.
- 收集了呼吸系统数据,以确定第二通风门 (VT2) 作为疲劳标记.
- 机器学习 (随机森林) 和夏普利添加式扩展 (SHAP) 分析了VT2前后的动力/动力数据.
主要成果:
- 机器学习模型在区分VT2前后状态时实现了高分类精度 (0.8530.962).
- 上半身的斜平面角范围 (RTAPu) 是6/13跑步者最重要的特征.
- 在VT2周围增加的预测分散表明特征相互作用和个体疲劳应对机制.
结论:
- 个人跑步者采用不同的运动策略来减轻疲劳的影响并保持跑步状态.
- 干上部动力学和负载率是个体特定水平上运行疲劳的敏感指标.
- 机器学习,特别是SHAP分析,有效地识别了与跑步疲劳相关的关键生物力学特征.
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