Emotion sensing from physiological signals using three defined areas in arousal-valence model

Abstract

This paper aims to recognize the human emotional states into three defined areas in arousal-valence evaluation: Corresponding to calm, medium aroused, and excited, unpleasant, neutral valence and pleasant. And thanks to the relevance of the peripheral physiological signals in emotion recognition issue, we used in our contribution the multimodal dataset MAHNOB-HCI. In this database, there are emotional bodily responses of twenty four participants after watching twenty affective stimuli videos. In our work, we focused on the ElectroCardioGram (ECG), Galvanic Skin Response (GSR), Skin Temperature (Temp) and Respiration Volume (RESP). To accomplish our purpose, we pre-process the data, extract 169 features and finally, classify the emotional states by using the support vector machine (SVM). As a first step, we classify each signal to know the most relevant physiological signal for emotion assessing, then a level feature fusion is applied to compare our approach to related work. According to previous studies, our obtained results are promising and show that the respiration and electrocardiogram are the most relevant.