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학술지 Analysis of Physiological Signals for Recognition of Boredom, Pain, and Surprise Emotions
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저자
장은혜, 박병준, 박미숙, 김상협, 손진훈
발행일
201506
출처
Journal of Physiological Anthropology, v.34, pp.1-12
ISSN
1880-6791
출판사
Japan Society of Physiological Anthropology
DOI
https://dx.doi.org/10.1186/s40101-015-0063-5
협약과제
15MC3100, 정신 질환의 모니터링 및 징후 예측을 위한 피부 부착형 센서 모듈 개발, 김승환
초록
Background: The aim of the study was to examine the differences of boredom, pain, and surprise. In addition to that, it was conducted to propose approaches for emotion recognition based on physiological signals. Methods: Three emotions, boredom, pain, and surprise, are induced through the presentation of emotional stimuli and electrocardiography (ECG), electrodermal activity (EDA), skin temperature (SKT), and photoplethysmography (PPG) as physiological signals are measured to collect a dataset from 217 participants when experiencing the emotions. Twenty-seven physiological features are extracted from the signals to classify the three emotions. The discriminant function analysis (DFA) as a statistical method, and five machine learning algorithms (linear discriminant analysis (LDA), classification and regression trees (CART), self-organizing map (SOM), Na챦ve Bayes algorithm, and support vector machine (SVM)) are used for classifying the emotions. Results: The result shows that the difference of physiological responses among emotions is significant in heart rate (HR), skin conductance level (SCL), skin conductance response (SCR), mean skin temperature (meanSKT), blood volume pulse (BVP), and pulse transit time (PTT), and the highest recognition accuracy of 84.7 % is obtained by using DFA. Conclusions: This study demonstrates the differences of boredom, pain, and surprise and the best emotion recognizer for the classification of the three emotions by using physiological signals.
키워드
Emotion, Emotion recognition, Physiological signal
KSP 제안 키워드
Bayes algorithm, Classification and regression tree(CART), Discriminant functions, Electrodermal activity, Emotion recognition, Function analysis, Heart rate, Linear Discriminant Analysis(LDA), Machine Learning Algorithms, Physiological features, Physiological responses