Towards Automatic and Unobtrusive Recognition of Primary-Process Emotions in Body Postures

Recent years demonstrate an increased research in automatic recognition of emotions in whole-body gestures. However, most of them rely on emotional models that are still being contested or require an obtrusive way of collecting the data. We study primitive postures based on 7 primary-process and clinically measured emotions. We portray postures from theatre in front of the motion capture sensor and we conduct online surveys to discriminate primary-process emotions. We analyze low-level features from postural joints data and reveal RAGE patterns which we will use in future real-time affective interactions.