The nature of statistical learning theory
The nature of statistical learning theory
International Journal of Human-Computer Studies
A user-independent real-time emotion recognition system for software agents in domestic environments
Engineering Applications of Artificial Intelligence
International Journal of Human-Computer Studies
Emotion Recognition Based on Physiological Changes in Music Listening
IEEE Transactions on Pattern Analysis and Machine Intelligence
Emotion recognition with consideration of facial expression and physiological signals
CIBCB'09 Proceedings of the 6th Annual IEEE conference on Computational Intelligence in Bioinformatics and Computational Biology
Cognitive Techniques in Visual Data Interpretation
Cognitive Techniques in Visual Data Interpretation
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Toward Emotion Recognition in Car-Racing Drivers: A Biosignal Processing Approach
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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Physical and mental diseases were deeply affected by stress and negative emotions. In general, emotions can be roughly recognized by facial expressions. Since facial expressions may be controlled and expressed differently by different people subjectively, inaccurate are very likely to happen. It is hard to control physiological responses and the corresponding signals while emotions are excited. Hence, an emotion recognition method that considers physiological signals is proposed in this paper. We designed a specific emotion induction experiment to collect five physiological signals of subjects including electrocardiogram, galvanic skin responses (GSR), blood volume pulse, and pulse. We use support vector regression (SVR) to train the trend curves of three emotions (sadness, fear, and pleasure). Experimental results show that the proposed method achieves high recognition rate up to 89.2%.