通过机器学习识别运动和体育特征应用于心率变化
Tony Estrella1,2, Lluis Capdevila1,2
1Sport Research Institute, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain.
Sports (Basel, Switzerland)
|February 25, 2025
概括
使用机器学习的心率变化 (HRV) 分析有效地识别运动特征并区分运动员. 这项研究提出了新的HRV衍生指数,用于加强训练计划中的运动评估.
科学领域:
- 运动科学 运动科学 运动科学
- 生物医学工程 生物医学工程
- 数据科学数据科学数据科学
背景情况:
- 心率变化 (HRV) 是生理状态的一个关键的非侵入性指标.
- 机器学习 (ML) 为复杂的HRV数据集提供了先进的分析能力.
研究的目的:
- 使用HRV和ML算法识别运动特征.
- 开发模型来分类运动员与非运动员,并识别团队中的个人足球运动员.
主要方法:
- 开发了两个模型:M1用于运动员分类 (856名运动员,494名非运动员) 和M2用于足球运动员识别 (105名球员,514名队友).
- 应用机器学习算法:随机森林 (RF),极端梯度增强 (XGBoost) 和支持向量机器 (SVM).
- 使用SHAP值用于模型解释.
主要成果:
- 在M1中,SVM取得了最高的性能 (精度=0.84,ROC AUC=0.91).
- 随机森林在M2中表现最好 (准确率=0.92,ROC AUC=0.94).
- 拟议的运动和足球识别指数来源于HRV数据.
结论:
- 机器学习算法 (SVM,RF) 可以有效地生成基于HRV的指数,用于运动员的识别.
- 这些指数有助于区分运动员和识别特定的运动个人资料.
- 建议将HRV评估系统地整合到训练方案中,以加强运动评估.
关键词:
在SHAP中,价值是SHAP值.运动员运动员运动员心率变化的心率变化.机器学习是机器学习.随机的森林随机的森林运动个人资料 运动个人资料支持矢量机器的支持矢量机器.团队运动是团队运动.训练负载训练负载训练负载更多相关视频
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