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Conference Paper
Three Differential Emotions and Classification Using Physiological Signals
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- Authors
- Byoung-Jun Park, Eun-Hye Jang, Sang-Hyeob Kim, Chul Huh, Jin-Hun Sohn
- Issue Date
- 2012-12
- Citation
- International Conference on Machine Learning and Computing (ICMLC) 2012, pp.55-59
- Language
- English
- Type
- Conference Paper
- Abstract
- In HCI researches, human emotion classification has done by machine learning algorithms based on physiological signals. In this study, we discuss the comparative results of emotion classification by several algorithms which classify three different emotional states (happiness, neutral, and surprise) using physiological features. 217 students participated in this experiment. While three kinds of emotional stimuli are presented to participants, physiological signal responses (EDA, SKT, ECG, RESP, and PPG) were measured. Participants rated their own feelings to emotional stimuli on emotional assessment scale after presentation of emotional stimuli. The emotional stimuli had 96% validity and 5.8 point efficiency on average. There were significant differences of autonomic nervous system responses among three emotions by statistical analysis. The classification of three differential emotions was carried out 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. The result of LDA showed that the accuracy of classification in three different emotions was 83.4%. 75.5% and 55.6% have obtained as the accuracy of classification by SVM and MLP, respectively. 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 those results to obtain more stability and reliability, as comparing with the accuracy of emotions classification by using other algorithms.
- KSP Keywords
- Autonomic nervous system(ANS), Emotional states, Human emotions, Machine Learning Algorithms, Physiological features, Physiological signals, Point efficiency, Statistical Analysis, Support VectorMachine(SVM), assessment scale, difference value