Design and Analysis of a Closed-Loop Emotion Regulation System Based on Multimodal Affective Computing and Emotional Markov Chain

In our daily lives, emotions are extremely important. However, predicting and regulating emotion is still a critical problem to be solved in the research of the human-computer interaction (HCI). In this study, we explore this problem using a regulation method. A novel multimodal affective computing algorithm is proposed and implemented in an emotion regulation system. The selection of music stimuli and the modeling of dynamic emotions are done using emotional Markov chains. This regulation system can monitor the user’s emotion and play music, selected by the regulation policy until the user can maintain the desired emotion. Our system was verified by two experiments. In the first experiment, by predicting the participants’ affective states, we tested the precision of our multimodal affective computing system. In the second experiment, we tested the regulation algorithm embedded in a closed-loop regulation system by comparing it with playing music without feedback. The results suggest that participants can regulate and maintain the desired affective state by using the emotion regulation system.