爬上阶梯:一种排名方法来预测倦怠
Alvise Dei Rossi1,2, Davide Marzorati1, Radoslava Švihrová1,3
1Department of Innovative Technologies, Institute of Digital Technologies for Personalized Healthcare, University of Applied Sciences and Arts of Southern Switzerland, Lugano, Switzerland.
Frontiers in digital health
|January 19, 2026
概括
这项研究探讨了使用可穿戴设备数据预测倦怠,发现排名模型对监测身体和认知疲劳充满希望. 需要进一步的研究,以不显而易见地评估情绪疲劳.
科学领域:
- 职业健康 职业健康 职业健康
- 心理评估 心理评估
- 可穿戴技术可穿戴技术
背景情况:
- 像Shirom-Melamed Burnout Measure (SMBM) 这样的验证工具评估了燃烧,但依赖于自我报告,限制了持续监测和引入偏见.
- 该SMBM通过身体疲劳 (PF),认知疲劳 (CW) 和情绪疲劳 (EE) 来衡量能量耗尽.
研究的目的:
- 调查使用可穿戴设备的被动生理数据,不显而易见地预测倦怠风险的可行性.
- 评估不同的机器学习方法 (分类,回归,学习到等级) 来预测倦怠.
- 开发和评估一个罗式循环神经网络,用于连续的可穿戴数据分析.
主要方法:
- 使用了239名工人的9个月数据集,包括可穿戴生理数据,人口统计和职业信息.
- 基于聚合和顺序模型进行比较,用于预测SMBM分量级分数.
- 开发了一个罗式循环神经网络,优化了从顺序可穿戴数据对对风险估计.
主要成果:
- 二元分类和回归模型在预测倦怠次级方面表现适度至微不足道.
- 基于等级的指标表明,PF和CW的相对倦怠严重程度可以从可穿戴信号中部分推断出来.
- 式循环神经网络与PF和CW燃烧得分的顺序性质的调整得到了改善 (斯皮尔曼的 ρ 分别为0.29和0.25).
结论:
- 来自可穿戴设备的被动生理数据,特别是当使用基于排名的技术进行分析时,显示了对不显眼的倦怠风险监测的潜力.
- 基于排名的方法,如拟议的米复杂神经网络,为评估身体和认知倦怠组件提供了一个有希望的途径.
- 预测情绪疲可能需要整合超越当前可穿戴生理信号的额外数据模式.
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