在精英青年乒乓球运动员中使用机器学习识别神经肌肉和精神疲劳
Thibault Delumeau1, Thibault Deschamps2, Christophe Plot3
1Nantes Université, Oniris, CNRS, GEPEA, UMR 6144, F-44470, Carquefou, France. thibault.delumeau@univ-nantes.fr.
Scientific reports
|March 3, 2026
概括
在乒乓球中检测球员疲劳现在更准确了. 这项研究使用仪器式球拍来分析运动模式,在预测精神和身体疲劳状态方面达到80%以上的准确性.
科学领域:
- 运动科学 运动科学 运动科学
- 生物力学 生物力学
- 机器学习 机器学习
背景情况:
- 乒乓球表现受到球员疲劳的显著影响.
- 目前缺乏准确的,非侵入性的方法来检测运动员的疲劳.
- 了解疲劳对于优化训练和预防受伤至关重要.
研究的目的:
- 开发和验证一种方法来检测乒乓球运动员的精神和身体疲劳.
- 通过仪器化球拍数据分析球员运动模式.
- 将机器学习应用于基于手势分析的疲劳状态预测.
主要方法:
- 对精神和身体疲劳都使用了受控的疲劳诱导协议.
- 球员的运动被捕获使用带有加速度计和压力传感器的仪器球拍.
- 监督机器学习模型 (K-最近的邻居,随机森林) 被训练在标记的移动数据上.
主要成果:
- 疲劳诱导通过感知劳动力和最大自愿收缩评级的显著变化得到验证.
- K-最近邻居和随机森林模型实现了高精度.
- 二元分类达到~84%的认可;多类分类达到~82%.
结论:
- 仪器运动分析与受控疲劳诱导相结合,为疲劳预测提供可靠的数据.
- 这种方法为监测运动员疲劳提供了一种有希望的非侵入性方法.
- 使用机器学习的手势分析可以有效地预测乒乓球运动员的疲劳状态.
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