A Dynamic Bayesian Approach to Computational Laban Shape Quality Analysis

Abstract

Laban movement analysis (LMA) is a systematic framework for describing all forms of human movement and has been widely applied across animation, biomedicine, dance, and kinesiology. LMA (especially Effort/Shape) emphasizes how internal feelings and intentions govern the patterning of movement throughout the whole body. As we argue, a complex understanding of intention via LMA is necessary for human‐computer interaction to become embodied in ways that resemble interaction in the physical world. We thus introduce a novel, flexible Bayesian fusion approach for identifying LMA Shape qualities from raw motion capture data in real time. The method uses a dynamic Bayesian network (DBN) to fuse movement features across the body and across time and as we discuss can be readily adapted for low‐cost video. It has delivered excellent performance in preliminary studies comprising improvisatory movements. Our approach has been incorporated in Response , a mixed‐reality environment where users interact via natural, full‐body human movement and enhance their bodily‐kinesthetic awareness through immersive sound and light feedback, with applications to kinesiology training, Parkinson′s patient rehabilitation, interactive dance, and many other areas.