GSR signals features extraction for emotion recognition

Over the years, the recognition of emotion has become more efficient, diverse, and easily accessible. In general, emotion recognition is conducted in four main steps which are signal acquisition, preprocessing, feature extraction, and classification. Galvanic skin response (GSR) is the autonomic activation of sweat glands in the skin when an individual gets triggered through emotional stimulation. The paper provides an overview of emotion recognition, GSR signals, and how GSR signals are analyzed for emotion recognition. The focus of this research is on the performance of feature extraction of GSR signals. Therefore, related sources were identified using combinations of keywords and terms such as feature extraction, emotion recognition, and galvanic skin response. Existing emotion recognition methods were investigated which focused more on the different feature extraction methods. Research conducted has shown that feature extraction method in time–frequency domain has the best accuracy rate overall compared to time domain and frequency domain. Current GSR-based technology also has the potential to be improved more toward the implementation of a more efficient and reliable emotion recognition system

GSR Signals Features Extraction for Emotion Recognition

Over the years, the recognition of emotion has become more efficient, diverse, and easily accessible. In general, emotion recognition is conducted in four main steps which are signal acquisition, preprocessing, feature extraction, and classification. Galvanic skin response (GSR) is the autonomic activation of sweat glands in the skin when an individual gets triggered through emotional stimulation. The paper provides an overview of emotion recognition, GSR signals, and how GSR signals are analyzed for emotion recognition. The focus of this research is on the performance of feature extraction of GSR signals. Therefore, related sources were identified using combinations of keywords and terms such as feature extraction, emotion recognition, and galvanic skin response. Existing emotion recognition methods were investigated which focused more on the different feature extraction methods. Research conducted has shown that feature extraction method in time–frequency domain has the best accuracy rate overall compared to time domain and frequency domain. Current GSR-based technology also has the potential to be improved more toward the implementation of a more efficient and reliable emotion recognition system