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학술지 Classification of Three Different Emotion by Physiological Parameters
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저자
장은혜, 박병준, 김상협, 손진훈
발행일
201204
출처
대한인간공학회지, v.31 no.2, pp.271-279
ISSN
1229-1684
출판사
대한인간공학회
협약과제
11SF1100, 시각 생체 모방 소자 및 인지 시스템 기술 개발, 정명애
초록
Objective: This study classified three different emotional states(boredom, pain, and surprise) using physiological signals.Background: Emotion recognition studies have tried to recognize human emotion by using physiological signals. It is important for emotion recognition to apply on human-computer interaction system for emotion detection. Method: 122college students participated in this experiment. Three different emotional stimuli were presented to participants and physiological signals, i.e., EDA(Electrodermal Activity), SKT(Skin Temperature), PPG(Photoplethysmogram), and ECG (Electrocardiogram) were measured for 1 minute as baseline and for 1~1.5 minutes during emotional state. The obtained signals were analyzed for 30 seconds from the baseline and the emotional state and 27 features were extracted from these signals. Statistical analysis for emotion classification were done by DFA(discriminant function analysis) (SPSS 15.0) by using the difference values subtracting baseline values from the emotional state. Results: The result showed that physiological responses during emotional states were significantly differed as compared to during baseline. Also, an accuracy rate of emotion classification was 84.7%. Conclusion: Our study have identified that emotions were classified by various physiological signals. However, future study is needed to obtain additional signals from other modalities such as facial expression, face temperature, or voice to improve classification rate and to examine the stability and reliability of this result compare with accuracy of emotion classification using other algorithms. Application: This could help emotion recognition studies lead to better chance to recognize various human emotions by using physiological signals as well as is able to be applied on human-computer interaction system for emotion recognition. Also, it can be useful in developing an emotion theory, or profiling emotion-specific physiological responses as well as establishing the basis for emotion recognition system in human-computer interaction.
KSP 제안 키워드
Accuracy Rate, Baseline values, Classification rate, College students, Discriminant functions, Electrodermal activity, Emotion Detection, Emotion classification, Emotional states, Function analysis, Human Emotions