Dynamic models for emotion estimation from physiological signals

Isabel Barradas

Abstract: The ultimate goal of Human-Machine Interaction is to make interaction as natural as possible. To accomplish this, the recognition of the user’s emotional state is considered an important factor. The field of emotion recognition and modelling has predominantly employed static machine learning approaches that ignore the dynamic nature of emotions. However, this dynamic character has recently been highlighted by the emergence of appraisal models (eg, Scherer’s Component Process Model, CPM). These recent developments of emotion theory have been combined with Dynamic Field Theory (a wellestablished framework in the field of embodied cognition) to model emotion intensity based on galvanic skin response changes. The present work aims for an extended approach that considers not only the intensity, but also the quality of emotions as well as the dynamic and simultaneous changes of both. To create a dynamic emotion model, we will record and analyse electrophysiological signals. In contrary to most studies in literature where the assessment of the subjective feeling is performed after the exhibition of a stimulus, we will assess the subjective feeling online (during the emotion elicitation and data collection). This will allow us to directly compare the recorded subjective feeling with the dynamic output of our model. The development of such a dynamic model will not only contribute for a better understanding of the emotional processes but will also benefit several real-world applications, such as gaming, mental health monitoring, and driving-assistance technologies.