用机器学习来估计从心肺呼吸信号感知到的劳动力强度等级的特征重要性
Runbei Cheng1, Phoebe Haste1, Elyse Levens1
1Natural Interaction Lab, Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom.
Frontiers in sports and active living
|October 9, 2024
概括
呼吸系统的特征,如每分钟通风 (VE),比心率 (HR) 更重要,用于通过机器学习来估计感知劳动力 (RPE). 结合心血管和呼吸系统数据可以改善RPE预测.
科学领域:
- 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:
- 感知运动的评分 (RPE) 是对运动强度的一种主观测量.
- 准确的RPE估计可以帮助优化训练和防止过度劳累.
研究的目的:
- 为了确定呼吸系统特征与心率 (HR) 在机器学习模型中估计RPE的相对重要性.
- 为了比较不同机器学习模型在预测RPE方面的有效性.
主要方法:
- 20名参与者 (18-43岁) 穿着COSMED K5便携式代谢分析仪进行了Yo-Yo间歇性恢复测试.
- 在测试期间记录了RPE;HR,分钟通风 (VE),呼吸频率 (Rf) 和氧气消耗 (VO2) 被测量.
- 线性,随机森林和多层感知器模型被训练使用8个特征,包括生理和人口统计数据.
主要成果:
- 随机森林模型实现了最高的准确性 (RMSE: 1.849,MAE: 1.461 ± 1.133) 使用离开一个主体的交叉验证.
- 分钟通风 (VE) 被确定为所有测试模型中RPE估计的最关键特征,其次是心率 (HR).
结论:
- 呼吸系统测量,特别是 VE,对于基于机器学习的 RPE 估计非常重要.
- 未来使用可穿戴传感器进行RPE预测的研究应该整合心血管和呼吸系统数据,以提高准确性.
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