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Detecting emotions from everyday body movements

The work presented in this paper focuses on the development of a computational model for describing and detecting affective content in everyday body movements. The approach is based on the extraction and analysis of dynamic motion qualities as opposed to limiting itself to the denotative meanings of body posture and gestures. To this end, a database of affective everyday motions such as knocking and walking has been analysed. Our approach makes use of a segmentation technique which can divide complex motions into a set of automatically derived motion primitives. The parsed motion is then analysed in terms of dynamic features which are shown to encode affective information. In order to adapt our algorithm to personal movement idiosyncrasies we developed a new approach for deriving unbiased motion features. We demonstrate that the resulting performance of our algorithm is similar to that of humans who took part in a comparable psychological experiment.