A Dynamic Bayesian Approach to Computational Laban Shape Quality Analysis

• Published in: Advances in human-computer interaction Vol. 2009; no. 2009; pp. 1 - 17
• Main Authors: Swaminathan, Dilip, Thornburg, Harvey, Mumford, Jessica, Rajko, Stjepan, James, Jodi, Ingalls, Todd, Campana, Ellen, Qian, Gang, Sampath, Pavithra, Peng, Bo
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Puplishing Corporation 2009
• ISSN: 1687-5893; 1687-5907

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.