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

机译:树林:通过EDA测量中性,压力和模拟驾驶条件期间的辅助装置的辅助装置

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摘要

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 observed between neutral-anger and neutral-stress groups, whereas no 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.
机译:认知,情感和情绪是精神生理信号研究中最受研究的最受欢迎之一。心率,皮肤导电和皮肤温度是了解自主神经系统的流行措施。这些措施与交感神经和副交感神经系统紧密相关,这调节了人类的情绪。压力和愤怒影响驾驶任务并有助于大量的道路崩溃。本研究利用了电台活性(EDA)来区分压力和驾驶员在执行模拟驾驶任务的同时将压力和愤怒区分。参加了20个健康的科目,并通过涉及人类受试者的研究委员会批准了实验议定书,普拉特拉马来西亚。 EDA信号的平均功率谱密度(PSD)在情绪群体之间进行统计学比较,重复测量Anova和Bonferroni后Hoc测试。在中性愤怒和中性应激组之间观察到显着差异(P <0.01),而在应激血管群之间没有显着差异(P& 0.01)。在十倍交叉验证时,带支持向量机(SVM)分类器的情感群体之间实现了有希望的分类准确性。

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