Emotion classification based on physiological signals induced by negative emotions: Discriminantion of negative emotions by machine learning algorithm

Recently, the one of main topic of emotion recognition or classification research is to recognize human's feeling or emotion using various physiological signals. It is one of the core processes to implement emotional intelligence in human computer interaction (HCI) research. The purpose of this study was to identify the best algorithm being able to discriminate negative emotions, such as sadness, fear, surprise, and stress using physiological features. Electrodermal activity (EDA), electrocardiogram (ECG), skin temperature (SKT), and photoplethysmography (PPG) are recorded and analyzed as physiological signals. And emotional stimuli used in this study are audio-visual film clips which have examined for their appropriateness and effectiveness through preliminary experiment. For classification of negative emotions, five machine learning algorithms, i.e., LDF, CART, SOM, Naïve Bayes and SVM are used. Result of emotion classification shows that an accuracy of emotion classification by SVM (100.0%) was the highest and by LDA (50.7%) was the lowest. CART showed emotion classification accuracy of 84.0%, SOM was 51.2% and Naïve Bayes was 76.2%. This can be helpful to provide the basis for the emotion recognition technique in HCI.