一种新的分类方法,以平衡精英与专家运动员之间的差异,基于复合多尺度复杂性指数和森林排名
Yuqi Cheng1,2, Dawei Wu3, Ying Wu1,3
1School of Exercise and Health, Shenyang Sport University, Shenyang, China.
PloS one
|January 30, 2025
概括
评估精英运动员的平衡是具有挑战性的. 使用质量中心连续指数 (CMCI) 和机器学习的新方法,特别是排名森林,与传统方法相比,显著改善了平衡能力歧视.
科学领域:
- 运动科学 运动科学 运动科学
- 生物力学 生物力学
- 人类运动分析 人类运动分析
背景情况:
- 精准的平衡评估对精英运动员至关重要,但仍然具有挑战性.
- 传统的压力中心 (CoP) 指标缺乏共识,可能无法捕捉复杂的姿势控制.
- 现有的方法很难区分运动员的微妙平衡变化.
研究的目的:
- 调查评估精英运动员平衡控制的新参数.
- 将传统平衡指标的有效性与新的多维方法进行比较.
- 评估机器学习算法来对运动员平衡能力进行分类.
主要方法:
- 收集了精英运动员和自由式滑雪运动员在各种表面和视觉条件上的压力中心 (CoP) 数据.
- 计算了质量中心连续指数 (CMCI) 和传统时间域特征 (AP/ML位移,长度,倾斜).
- 应用后勤回归,SVM,天真贝叶斯和排名森林分类器,使用ROC分析进行评估.
主要成果:
- 传统的时间域CP特征不足以进行准确的平衡歧视.
- 质量中心连续指数 (CMCI) 显示出卓越的表现.
- CMCI和排名森林分类器的组合实现了最高的灵敏度 (0.95) 和特异性 (0.35).
结论:
- 使用CMCI和机器学习的非线性,多维方法在评估运动员复杂的姿势控制方面非常有效.
- 这种方法提供了比传统技术更强大,更准确的平衡能力评估.
- 这些发现表明,在体育科学中开发先进的平衡评估工具是一个有希望的方向.
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