A machine learning model for emotion recognition from physiological signals. (January 2020)

• Record Type: Journal Article
• Authors: Domínguez-Jiménez, J.A.; Campo-Landines, K.C.; Martínez-Santos, J.C.; Delahoz, E.J.; Contreras-Ortiz, S.H.
• Is Part Of: Biomedical signal processing and control. Volume 55(2020)
• Publication Date: 2020-01
• Subjects: Emotion recognition -- Physiological signals -- Biosignal processing -- Machine learning -- Affective computing; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2019.101646
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: The model was effective for recognition of sadness, happiness and neutral emotions. Emotion recognition was possible from galvanic skin response features. A support vector machine with linear kernel showed the best classification results. Abstract: Emotions are affective states related to physiological responses. This study proposes a model for recognition of three emotions: amusement, sadness, and neutral from physiological signals with the purpose of developing a reliable methodology for emotion recognition using wearable devices. Target emotions were elicited in 37 volunteers using video clips while two biosignals were recorded: photoplethysmography, which provides information about heart rate, and galvanic skin response. These signals were analyzed in frequency and time domains to obtain a set of features. Several feature selection techniques and classifiers were evaluated. The best model was obtained with random forest recursive feature elimination, for feature selection, and a support vector machine for classification. The results show that it is possible to detect amusement, sadness, and neutral emotions using only galvanic skin response features. The system was able to recognize the three target emotions with accuracy up to 100% when evaluated on the test data set.