Title :
Emotion classification based on physiological signals induced by negative emotions: Discriminantion of negative emotions by machine learning algorithm
Author :
Jang, Eun-Hye ; Park, Byoung-Jun ; Kim, Sang-Hyeob ; Sohn, Jin-Hun
Author_Institution :
IT Convergence Technol. Res. Lab., Electron. & Telecommun. Res. Inst., Daejeon, South Korea
Abstract :
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.
Keywords :
behavioural sciences; emotion recognition; human computer interaction; learning (artificial intelligence); pattern classification; ECG; EDA; Electrodermal activity; HCI; PPG; SKT; classification research; core processes; discriminate negative emotions; electrocardiogram; emotion classification; emotion recognition; emotional intelligence; emotional stimuli; human computer interaction; machine learning algorithm; negative emotion discrimination; photoplethysmography; physiological features; physiological signals; skin temperature; Accuracy; Classification algorithms; Emotion recognition; Feature extraction; Physiology; Stress; Support vector machines; emotion classification; machine learning algorithm; negative emotion; physiological signal;
Conference_Titel :
Networking, Sensing and Control (ICNSC), 2012 9th IEEE International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4673-0388-0
DOI :
10.1109/ICNSC.2012.6204931