Grove: an auxiliary device for sympathetic assessment via EDA measurement of neutral, stress, and anger emotions during simulated driving conditions

J. K. Ooi, S. A. Ahmad, A. J. Ishak, K. Minhad, S. Ali, Y. Z. Chong
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引用次数: 3

Abstract

Cognition, emotion, and mood are one of the most researched topics in psychophysiological signal study. Heart rate, skin conductance, and skin temperature are popular measures of understanding autonomic nervous systems. These measures are tightly related to sympathetic and parasympathetic nervous system, which regulates human emotion. Stress and anger affect driving task and contribute to the high number of road crashes. This study utilised electrodermal activity (EDA) to differentiate stress and anger from the neutral emotion of drivers while performing a simulated driving task. Twenty healthy subjects participated and the experiment protocol was approved by Ethics Committee for Research Involving Human Subjects, Universiti Putra Malaysia. Mean power spectral density (PSD) of EDA signals were statistically compared between emotion groups with repeated-measures ANOVA and Bonferroni post hoc test. A significant difference (p 0.01) was noted between stress-anger groups. Promising classification accuracy was achieved between emotion groups with support vector machine (SVM) classifier at ten-fold cross-validation.
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格罗夫:一种辅助装置,通过EDA测量模拟驾驶条件下的中性、压力和愤怒情绪来评估交感神经
认知、情绪和情绪是心理生理信号研究中研究最多的课题之一。心率、皮肤电导和皮肤温度是了解自主神经系统的常用方法。这些措施与调节人类情绪的交感和副交感神经系统密切相关。压力和愤怒会影响驾驶任务,并导致大量的道路交通事故。这项研究利用皮肤电活动(EDA)来区分驾驶员在执行模拟驾驶任务时的压力和愤怒情绪与中性情绪。20名健康受试者参与,实验方案由马来西亚博特拉大学人体受试者研究伦理委员会批准。采用重复测量方差分析和Bonferroni事后检验比较情绪组间EDA信号的平均功率谱密度(PSD)。应激-愤怒组之间存在显著差异(p < 0.01)。支持向量机(SVM)分类器在十倍交叉验证下获得了良好的分类精度。
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