지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
이용수
초록· 키워드
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Objective: The aim of this study is to compare results of emotion recognition by several algorithms which classify three different emotional states(happiness, neutral, and surprise) using physiological features. Background: Recent emotion recognition studies have tried to detect human emotion by using physiological signals. It is important for emotion recognition to apply on human-computer interaction system for emotion detection. Method: 217 students participated in this experiment. While three kinds of emotional stimuli were presented to participants, ANS responses(EDA, SKT, ECG, RESP, and PPG) as physiological signals were measured in twice first one for 60 seconds as the baseline and 60 to 90 seconds during emotional states. The obtained signals from the session of the baseline and of the emotional states were equally analyzed for 30 seconds. Participants rated their own feelings to emotional stimuli on emotional assessment scale after presentation of emotional stimuli. The emotion classification was analyzed by Linear Discriminant Analysis(LDA, SPSS 15.0), Support Vector Machine(SVM), and Multilayer perceptron(MLP) using difference value which subtracts baseline from emotional state. Results: The emotional stimuli had 96% validity and 5.8 point efficiency on average. There were significant differences of ANS responses among three emotions by statistical analysis. The result of LDA showed that an accuracy of classification in three
different emotions was 83.4%. And an accuracy of three emotions classification by SVM was 75.5% and 55.6% by MLP. Conclusion: This study confirmed that the three emotions can be better classified by LDA using various physiological features than SVM and MLP. Further study may need to get this result to get more stability and reliability, as comparing with the accuracy of emotions classification by using other algorithms. Application: This could help get better chances to recognize various human emotions by using physiological signals as well as be applied on human-computer interaction system for recognizing human emotions.
different emotions was 83.4%. And an accuracy of three emotions classification by SVM was 75.5% and 55.6% by MLP. Conclusion: This study confirmed that the three emotions can be better classified by LDA using various physiological features than SVM and MLP. Further study may need to get this result to get more stability and reliability, as comparing with the accuracy of emotions classification by using other algorithms. Application: This could help get better chances to recognize various human emotions by using physiological signals as well as be applied on human-computer interaction system for recognizing human emotions.
목차
ABSTRACT
1. Introduction
2. Method
3. Results
4. Conclusion
Acknowledgements
References
Author listings
참고문헌 (26)
참고문헌 신청
1. [학술대회논문] - Arroyo-Palacios, J / 2008 / Towards a standardization in the use of physiological signals for affective recognition systems / Proceeding of the 6th International Conference of Methods and Techniques in Behavioral Research
2. [학술지(정기간행물)] - Bailenson, J. N / 2008 / Real-time classification of evoked emotions using facial feature tracking and physiological responses / International Journal of Human-Computer Studies 66 (5) : 303 ~ 317
3. [학술대회논문] - Calvo, R. A / 2009 / Effect of experimental factors on the recognition of affective mental states through physiological measures / Proceeding of 22nd Australasian Joint Conference on Artificial Intelligence
4. [학술지(정기간행물)] - Ekman, P / 1992 / An argument for basic emotions / Cognitive emotion 62 : 45 ~ 57
5. [학술지(정기간행물)] - Ekman, P / 1983 / Autonomic nervous system activity distinguishes among emotions / Science 221
2. [학술지(정기간행물)] - Bailenson, J. N / 2008 / Real-time classification of evoked emotions using facial feature tracking and physiological responses / International Journal of Human-Computer Studies 66 (5) : 303 ~ 317
3. [학술대회논문] - Calvo, R. A / 2009 / Effect of experimental factors on the recognition of affective mental states through physiological measures / Proceeding of 22nd Australasian Joint Conference on Artificial Intelligence
4. [학술지(정기간행물)] - Ekman, P / 1992 / An argument for basic emotions / Cognitive emotion 62 : 45 ~ 57
5. [학술지(정기간행물)] - Ekman, P / 1983 / Autonomic nervous system activity distinguishes among emotions / Science 221
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UCI(KEPA) : I410-ECN-0101-2013-530-001464833