Automatic Emotions Assessment Using Heart Rate Variability Analysis and 2D Regression Models of Emotions

Objective assessment of emotions is a challenging task. In this research, a new approach for detection and quantification of four emotions (pleasure, sadness, anger, and joy) is introduced based on the 2D regression models of emotions. ECG signal of 70 healthy female volunteers was recorded while the participants were stimulated by four different colors for five minutes. Induced arousal and valence levels were identified using the Self-Assessment Manikin test. Based on extracted features from Poincare Plot of heart rate series, 1- the label of emotion is determined based on 2-D emotion models using a rule- based approach, and 2- the strength of emotion is calculated in percentages using proposed mathematical formula. The results confirm the findings of previous studies that showed that each color is related to one emotion. The mean square error for estimation of arousal and valence levels were 0.0393 and 0.0536, respectively. In emotion classification, sensitivity of 96.78%, specificity of 99.4%, and the accuracy of98.75% were achieved. The proposed method for measuring emotions can be of great help to psychiatrists to identify mental disorders. Using this method, one can measure and express the strength of emotions individually and has a comparative criterion for emotions in different individuals.