Emotion Recognition Based on Multiple Physiological Signals.
10.3969/j.issn.1671-7104.2020.04.001
- Author:
Shali CHEN
1
;
Liuyi ZHANG
2
;
Feng JIANG
1
;
Wanlin CHEN
1
;
Jiajun MIAO
1
;
Hang CHEN
1
Author Information
1. College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027.
2. Department of Psychology and Behavioral Science, Zhejiang University, Hangzhou, 310027.
- Publication Type:Journal Article
- Keywords:
emotion recognition;
multiple physiological signals;
support vector machine
- MeSH:
Arousal;
Emotions;
Galvanic Skin Response;
Humans;
Photoplethysmography;
Support Vector Machine
- From:
Chinese Journal of Medical Instrumentation
2020;44(4):283-287
- CountryChina
- Language:Chinese
-
Abstract:
Emotion is a series of reactions triggered by a specific object or situation that affects a person's physiological state and can, therefore, be identified by physiological signals. This paper proposes an emotion recognition model. Extracted the features of physiological signals such as photoplethysmography, galvanic skin response, respiration amplitude, and skin temperature. The SVM-RFE-CBR(Recursive Feature Elimination-Correlation Bias Reduction-Support Vector Machine) algorithm was performed to select features and support vector machines for classification. Finally, the model was implemented on the DEAP dataset for an emotion recognition experiment. In the rating scale of valence, arousal, and dominance, the accuracy rates of 73.5%, 81.3%, and 76.1% were obtained respectively. The result shows that emotional recognition can be effectively performed by combining a variety of physiological signals.
