基于机器学习的分类分析知识工作者的心理压力分析
Hyunsuk Kim1, Minjung Kim1, Kyounghyun Park1
1Mobility UX Research Section, Electronics and Telecommunications Research Institute, Daejeon, Republic of Korea.
Frontiers in public health
|November 29, 2023
概括
这项研究表明,可穿戴式手表可以使用心率 (HR) 等生物信号来分类压力. 精确的应力检测需要持续超过2-3分钟的生理数据测量.
科学领域:
- 生物医学工程 生物医学工程
- 可穿戴技术可穿戴技术
- 人与计算机的交互
背景情况:
- 工作场所的压力会对员工的幸福感和生产力产生负面影响.
- 客观压力测量具有挑战性,但不会破坏工作活动.
- 可穿戴设备提供了一种非侵入性的生理数据收集方法.
研究的目的:
- 用可穿戴手表的生物信号数据评估分类压力和非压力状态的性能.
- 评估工作活动期间不引人注目的压力监测的可行性.
- 为了确定压力检测的最佳特征和分类方法.
主要方法:
- 参与者戴着Galaxy Watch3,在诱导压力和放松任务期间收集心率 (HR) 和光电脉冲扫描数据.
- 使用k-NN,SVM,决策树 (DT),物流回归 (LR),随机森林 (RF) 和多层感知器 (MLP) 算法构建了分类模型.
- 使用Leave-One-Subject-Out交叉验证 (LOSO-CV) 来进行绩效评估.
主要成果:
- 最好的9个特征,包括HR指标 (平均值,最低值),NNI,SDNN,vLF,HF,LF,LF/HF比率和总功率,产生了最佳的分类性能.
- 最佳模型实现了0.817的精度和0.801.801的F1得分.
- 精确的压力状态区分需要超过2-3分钟的生理数据收集时间.
结论:
- 可穿戴生物信号监测是对工作环境中的压力和非压力状态进行分类的可行方法.
- 具体的HR和心率变化特征对于有效的压力分类至关重要.
- 充分的数据采样时间对于使用可穿戴技术可靠地检测压力至关重要.
关键词:
心率是指心率是如何发生的.知识工作者是知识工作者.机器学习是机器学习.精神上的压力,精神上的压力.摄影复合声学 (photoplethysmography) 是一种摄影仪.脉率变化的脉率变化.更多相关视频
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