  Although psychological research indicates that bodily expressions convey
important affective information, to date research in emotion recognition
focused mainly on facial expression or voice analysis. In this paper we propose
an approach to realtime automatic emotion recognition from body movements. A
set of postural, kinematic, and geometrical features are extracted from
sequences 3D skeletons and fed to a multi-class SVM classifier. The proposed
method has been assessed on data acquired through two different systems: a
professionalgrade optical motion capture system, and Microsoft Kinect. The
system has been assessed on a "six emotions" recognition problem, and using a
leave-one-subject-out cross validation strategy, reached an overall recognition
rate of 61.3% which is very close to the recognition rate of 61.9% obtained by
human observers. To provide further testing of the system, two games were
developed, where one or two users have to interact to understand and express
emotions with their body.
