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

Authors
 JeeEun Lee  ;  Sun K. Yoo 
Citation
 SENSORS, Vol.18(12) : E4253, 2018 
Journal Title
 SENSORS 
Issue Date
2018
Keywords
Kullback-Leibler divergence ; emotion ; physiological signal
Abstract
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.
Files in This Item:
T201805391.pdf Download
DOI
10.3390/s18124253
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Medical Engineering (의학공학교실) > 1. Journal Papers
Yonsei Authors
Yoo, Sun Kook(유선국) ORCID logo https://orcid.org/0000-0002-6032-4686
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/167187
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