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基于传感器的多式穿戴式压力检测:机器学习管道,具有系统的特征选择和关键生物标志物洞察力

Shao Ming Ng¹, Jee-Hou Ho¹, Bee Ting Chan²

¹Mechanical, Materials and Manufacturing Engineering, University of Nottingham Malaysia, Jalan Broga, Semenyih, 43500, Malaysia.

Biomedical physics & engineering express

|March 3, 2026

概括

通过结合多个可穿戴传感器 (EDA,ECG,EEG) 和系统的特征选择,提高了精确的心理压力检测. 这种机器学习方法提高了应力分类的准确性,提供了更强大,更易于解释的解决方案.

科学领域:

生物医学工程生物医学工程
机器学习机器学习
可穿戴式传感器技术

背景情况:

随着人们对与压力相关的健康问题日益增长的认识,需要先进的,非侵入性的压力检测.
可穿戴式传感器为持续的生理监测提供了一个有希望的途径.
在多式联运应力检测模型中特征选择的作用需要进一步研究.

研究的目的:

开发和评估机器学习管道用于使用多式联络传感器数据检测心理压力.
评估系统性特征选择对模型性能的影响.
为了比较多式传感器融合与单式传感器融合的压力分类方法.

主要方法:

收集了来自17名参与者的生理数据 (皮电活动 (EDA),心电图 (ECG),脑电图 (EEG)).
实施了机器学习管道,包括数据预处理,特征提取和分类.
应用了四种特征选择方法 (ANOVA,Chi2,Kruskal-Wallis,MRMR) 并在SRAD数据集上进行外部验证.

主要成果:

多模式传感器融合使分类准确度提高了12.9%,达到95.9%.
特性选择提供了4.8%的平均准确度增长,其中Chi-squared (Chi2) 产生了最高的性能.
确定了关键的生物标志物:心电图 (中位数,平均值,根-平方平均值),脑电图 (β-α比率,相对α功率) 和EDA (平均值,相位活动总和).

结论:

系统的特征选择对于优化基于多式传感器的精神压力检测至关重要.
开发的管道提高了应力检测系统的准确性,稳定性和可解释性.
这些发现强调了综合生理传感和机器学习在心理健康监测方面的潜力.

关键词:

这是一个ECGECGECGECGECG.一个EDA,一个EDA.这是一个EEGEEGEEGEEGEEGEEGEEG.功能选择功能选择心理压力分类心理压力分类这是一个多式联络模式.

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