상세정보
학술대회
Emotion Classification based on Bio-Signals: Emotion Recognition using Machine Learning Algorithms
- 발행일
- 201404
- 출처
- International Conference on Information Science, Electronics and Electrical Engineering (ISEEE) 2014, pp.1373-1376
- 협약과제
- 13SE1600, 시각 생체 모방 소자 및 인지 시스템 기술 개발, 정명애
- 초록
- Emotions are complex processes involving multiple response channels, including physiological systems, facial expressions and voices. Bio-signals reflect sequences of neural activity, which result in changes in autonomic and neuroendocrine systems induced by emotional events. Therefore in human-computer interaction researches, one of the most current interesting topics in emotion recognition is to recognize human's feeling using bio-signals. The aim of this study is to classify emotions (joy, sadness, anger, fear, surprise, and neutral) that human have often experienced in real life from multichannel bio-signals using machine learning algorithms. We have measured physiological responses of three-hundred participants for acquisition of bio-signals such as electrodermal activity, electrocardiograph, skin temperature, and photoplethysmo-graph during six emotions induction. Also, for emotion classification, we have extracted eighteen features from the signals and performed emotion classification using four algorithms, linear discriminant analysis, Na챦ve Bayes, classification and regression tree and support vector machine. The used algorithms were evaluated by only training, 10-fold cross-validation and repeated random sub-sampling validation. We have obtained recognition accuracy from 56.4 to 100% for only training and 39.2 to 53.9% for testing. Also, the result for testing showed that an accuracy of emotion recognition by Na챦ve Bayes was highest (53.9%) and lowest by support vector machine (39.2%). This means that Na챦ve Bayes is the best emotion recognition algorithm for basic emotions. This result can be helpful to provide the basis for the emotion recognition technique in human-computer interaction.
- KSP 제안 키워드
- Basic emotions, Classification and regression tree(CART), Cross validation(CV), Electrodermal activity, Emotion classification, Emotion recognition, Linear Discriminant Analysis(LDA), Machine Learning Algorithms, Multiple response, Neural activity, Physiological Systems