Emotions play an important role in our everyday life, influencing our decision-making process, and also affecting our physiology. Several studies in literature have proposed successful classification models for emotion recognition combining multimodal physiological measures without dwelling on the physiological significance of the measures. Our study aims at finding cardiovascular indices related to the autonomic nervous system that can explain how autonomic control of the heart responds with respect to specific emotions: happiness, fear, relaxation and boredom. Pulse arrival time and pulse pressure measurements have been shown to be significantly separating the 4 emotions, especially along the arousal dimension as expected from previous findings. Importantly, these blood pressure related indices also yielded relevant insights into characterizing the valence dimension when looking at high and low arousal subsets. In addition, these measures were found to be correlated with classical autonomic indices and explanatory in the cardiovascular and autonomic changes elicited by different emotions. Autonomic indices were then used to train a basic support vector machine model obtaining four-class test accuracy in discriminating happiness, relaxation, boredom and fear equal to 44%, 67%, 55%, 44% respectively.
Analysis of the effect of emotion elicitation on the cardiovascular system
Zanet M;Paglialonga A;
2021
Abstract
Emotions play an important role in our everyday life, influencing our decision-making process, and also affecting our physiology. Several studies in literature have proposed successful classification models for emotion recognition combining multimodal physiological measures without dwelling on the physiological significance of the measures. Our study aims at finding cardiovascular indices related to the autonomic nervous system that can explain how autonomic control of the heart responds with respect to specific emotions: happiness, fear, relaxation and boredom. Pulse arrival time and pulse pressure measurements have been shown to be significantly separating the 4 emotions, especially along the arousal dimension as expected from previous findings. Importantly, these blood pressure related indices also yielded relevant insights into characterizing the valence dimension when looking at high and low arousal subsets. In addition, these measures were found to be correlated with classical autonomic indices and explanatory in the cardiovascular and autonomic changes elicited by different emotions. Autonomic indices were then used to train a basic support vector machine model obtaining four-class test accuracy in discriminating happiness, relaxation, boredom and fear equal to 44%, 67%, 55%, 44% respectively.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
