Grounding affective dimensions into posture features

Authors:
Andrea Kleinsmith;P. Ravindra De Silva;Nadia Bianchi-Berthouze
Affiliations:
Database Systems Laboratory, University of Aizu, Aizu Wakamatsu, Japan;Database Systems Laboratory, University of Aizu, Aizu Wakamatsu, Japan;Database Systems Laboratory, University of Aizu, Aizu Wakamatsu, Japan
Venue:
ACII'05 Proceedings of the First international conference on Affective Computing and Intelligent Interaction
Year:
2005

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Abstract

Many areas of today’s society are seeing an increased importance in the creation of systems capable of interacting with users on an affective level through a variety of modalities. Our focus has been on affective posture recognition. However, a deeper understanding of the relationship between emotions in terms of postural expressions is required. The goal of this study was to identify affective dimensions that human observers use when discriminating between postures, and to investigate the possibility of grounding this affective space into a set of posture features. Using multidimensional scaling, arousal, valence, and action tendency were identified as the main factors in the evaluation process. Our results showed that, indeed, low-level posture features could effectively discriminate between the affective dimensions.