Emotion recognition system using short-term monitoring of physiological signals
Emotion recognition system using short-term monitoring of physiological signals
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DOI：
10.1007/bf02344719
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发表时间：
2004-05-01
影响因子：
3.2
通讯作者：
Kim, SR
中科院分区：
文献类型：
--
作者：
Kim, KH;Bang, SW;Kim, SR
报道了一种基于生理信号的情感识别系统。该系统的开发作为一个用户独立的系统，从多个主题获得的生理信号数据库的基础上运行。输入信号为心电图、皮肤温度变化和皮肤电活动，这些信号都是在没有太大不适感的情况下从体表获取的，可以反映情绪对自主神经系统的影响。该系统包括预处理、特征提取和模式分类阶段。设计了预处理和特征提取方法，以便从短段信号中提取特定于情感的特征。虽然特征被仔细地提取，但是它们的分布形成了分类问题，在聚类之间具有大的重叠，并且在聚类内具有大的方差。采用支持向量机作为模式分类器解决了这一难题。50名受试者的正确分类率分别为78.4%和61.8%，分别为三个和四个类别的识别。
A physiological signal-based emotion recognition system is reported. The system was developed to operate as a user-independent system, based on physiological signal databases obtained from multiple subjects. The input signals were electrocardiogram, skin temperature variation and electrodermal activity, all of which were acquired without much discomfort from the body surface, and can reflect the influence of emotion on the autonomic nervous system. The system consisted of preprocessing, feature extraction and pattern classification stages. Preprocessing and feature extraction methods were devised so that emotion-specific characteristics could be extracted from short-segment signals. Although the features were carefully extracted, their distribution formed a classification problem, with large overlap among clusters and large variance within clusters. A support vector machine was adopted as a pattern classifier to resolve this difficulty. Correct-classification ratios for 50 subjects were 78.4% and 61.8%, for the recognition of three and four categories, respectively.