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Sensors 2018, 18(12), 4253;

Design of User-Customized Negative Emotion Classifier Based on Feature Selection Using Physiological Signal Sensors

Graduate Program of Biomedical Engineering, Yonsei University, Seoul 03722, Korea
Department of Medical Engineering, Yonsei University College of Medicine, Seoul 03722, Korea
Author to whom correspondence should be addressed.
Received: 30 October 2018 / Revised: 30 November 2018 / Accepted: 1 December 2018 / Published: 3 December 2018
(This article belongs to the Special Issue Sensors for Biosignal Processing)
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First, the Likert scale and self-assessment manikin are used to provide emotion analogies, but they have limits for reflecting subjective factors. To solve this problem, we use physiological signals that show objective responses from cognitive status. The physiological signals used are electrocardiogram, skin temperature, and electrodermal activity (EDA). Second, the degree of emotion felt, and the related physiological signals, vary according to the individual. KLD calculates the difference in probability distribution shape patterns between two classes. Therefore, it is possible to analyze the relationship between physiological signals and emotion. As the result, features from EDA are important for distinguishing negative emotion in all subjects. In addition, the proposed feature selection algorithm showed an average accuracy of 92.5% and made it possible to improve the accuracy of negative emotion recognition. View Full-Text
Keywords: emotion; Kullback-Leibler divergence; physiological signal emotion; Kullback-Leibler divergence; physiological signal

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Lee, J.; Yoo, S.K. Design of User-Customized Negative Emotion Classifier Based on Feature Selection Using Physiological Signal Sensors. Sensors 2018, 18, 4253.

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