Emotion recognition system based on physiological signals with Raspberry Pi III implementation

Human machine interaction fieldhas potentialapplications in different domainssuch as medicine therapies for vulnerable persons. Thus, allowing the machine to identify and understand emotional states is one of the primordial stages for affective interactivity with Humans. Recent studies have proved that physiological signals contribute to recognize the emotion. In this paper, we aim to classify the affective states into two defined classes in arousal-valence model using peripheral physiological signals. For this aim, we explored the recent multimodal MAHNOB-HCI database that contains the bodily responses of 24 participants to 20 affective videos. After preprocessing the data and extracting features, we classified the emotion using the Support Vector Machine (SVM). The classification stage was implemented on Raspberry Pi III model B using Python platform. The obtained results are encouraging compared to recent related works.