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全球压力检测框架结合了减少的一组HRV特征和随机森林模型

Kamana Dahal¹, Brian Bogue-Jimenez¹, Ana Doblas¹

¹Department of Electrical and Computer Engineering, The University of Memphis, Memphis, TN 38152, USA.

Sensors (Basel, Switzerland)

|June 10, 2023

概括

本研究介绍了一种使用八个心率变化 (HRV) 特性和随机森林算法的全球压力检测模型. 该模型在识别个人压力方面达到99%以上的准确性,即使是通过全球训练.

科学领域:

生物医学工程生物医学工程
数据科学数据科学数据科学
心理生理学心理生理学

背景情况:

慢性压力显著影响全球成年人的健康,导致诸如心脏病和焦虑等严重疾病.
使用可穿戴设备的现有压力检测方法缺乏标准化的功能集,并且通常依赖于个人特定的培训.
可穿戴的腕带被广泛采用,为改善压力监测提供了机会.

研究的目的:

开发和验证全球压力检测模型,使用统一的功能集和机器学习算法.
为了研究将八个心率变化 (HRV) 特性与随机森林 (RF) 算法用于压力检测的有效性.
建立一个可以在全球范围内训练但根据个人表现进行评估的压力检测模型.

主要方法:

选择了八个最佳HRV功能,使用最小冗余最大相关性 (mRMR) 方法进行高效的模型训练.
实施了随机森林 (RF) 算法的全球培训框架,包括所有主题的数据.
使用WESAD和SWELL开放访问数据库,单独和组合,验证了拟议的全球压力模型.

主要成果:

全球压力监测模型在识别个人特异性压力事件方面实现了超过99%的准确性.
通过选择最有信息的HRV特征,mRMR方法有效地减少了培训时间.
该模型在不同数据集中表现出强的性能,证实了其可通用性.

结论:

在各种数据上训练的全球压力检测模型可以准确地识别单个压力事件.
选择的HRV功能和RF算法的集成为非侵入性,高精度压力监测提供了一个有希望的方法.
未来的研究应该集中在对这一全球压力监测框架的现实应用测试上.

关键词:

这是一个ECGECGECGECGECG.人力资源车的特点 HRV的特点功能选择功能选择全球培训全球培训个人测试个人测试个人测试机器学习是机器学习.压力检测压力检测穿戴式设备是一种可穿戴的设备.

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