Emotion expression in human-human interaction takes place via various types of information, including body motion. Research on the perceptual-cognitive mechanisms underlying the processing of natural emotional body language can benefit greatly from datasets of natural emotional body expressions that facilitate stimulus manipulation and analysis. The existing databases have so far focused on few emotion categories which display predominantly prototypical, exaggerated emotion expressions. Moreover, many of these databases consist of video recordings which limit the ability to manipulate and analyse the physical properties of these stimuli. We present a new database consisting of a large set (over 1400) of natural emotional body expressions typical of monologues. To achieve close-to-natural emotional body expressions, amateur actors were narrating coherent stories while their body movements were recorded with motion capture technology. The resulting 3-dimensional motion data recorded at a high frame rate (120 frames per second) provides fine-grained information about body movements and allows the manipulation of movement on a body joint basis. For each expression it gives the positions and orientations in space of 23 body joints for every frame. We report the results of physical motion properties analysis and of an emotion categorisation study. The reactions of observers from the emotion categorisation study are included in the database. Moreover, we recorded the intended emotion expression for each motion sequence from the actor to allow for investigations regarding the link between intended and perceived emotions. The motion sequences along with the accompanying information are made available in a searchable MPI Emotional Body Expression Database. We hope that this database will enable researchers to study expression and perception of naturally occurring emotional body expressions in greater depth.
ASJC Scopus subject areas
- Agricultural and Biological Sciences(all)
- Biochemistry, Genetics and Molecular Biology(all)