基于机器学习的疲劳分类,使用心率变化和皮质醇:一种多模式方法来监测可穿戴健康状况
Joung Eun Kim1, Na Hyeon Kim2,3, Soo Kyung Choi2
1Department of Family Medicine, Seoul National University Bundang Hospital, Seongnam, Korea.
Digital health
|November 13, 2025
概括
使用可穿戴传感器预测疲劳正在取得进展. 结合皮质醇和心率变化 (HRV) 显示出相对于单独的HRV的微小改善,这表明更简单的模型具有更少的变量是有效的疲劳监测.
科学领域:
- 生物医学工程 生物医学工程
- 生理监测 生理监测
- 医疗保健中的机器学习
背景情况:
- 疲劳是一个关键的健康指标,需要准确的预测方法.
- 可穿戴设备为非侵入性疲劳评估提供了潜力.
- 皮质醇和心率变化 (HRV) 对疲劳的联合预测能力仍在研究中.
研究的目的:
- 为了评估一个多式联机机器学习模型,整合皮质醇和HRV来预测疲劳.
- 评估结合生理生物标志物用于疲劳监测的有效性.
- 探索可穿戴传感器数据在健康管理中的实用性.
主要方法:
- 分析了336名参与者的数据,使用疲劳严重程度量表 (FSS).
- 通过链式方程 (MICEs) 应用多变量归算,用于缺少的皮质醇数据.
- 开发一个TabNet深度学习模型,利用HRV和皮质醇数据预测疲劳水平.
主要成果:
- 一个仅使用HRV的模型获得了0.774的AUC;结合皮质醇的AUC为0.741.
- 特性重要性分析表明,在包括皮质醇的模型中,依赖于有限的一组变量.
- 一组减少的变量 (年龄,皮质醇,对数非常低频率) 实现了可比的预测性能 (AUC=0.759).
结论:
- 结合皮质醇和HRV的疲劳预测模型可以在更少的变量下保持预测能力.
- 结果支持这些模型在可穿戴设备中的实际实施.
- 减少传感器数量和计算负载可以实现精确的疲劳监测.
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