A Dynamic Bayesian Approach to Computational Laban Shape Quality Analysis. (24th February 2009)

• Record Type: Journal Article
• Authors: Swaminathan, Dilip; Thornburg, Harvey; Mumford, Jessica; Rajko, Stjepan; James, Jodi; Ingalls, Todd; Campana, Ellen; Qian, Gang; Sampath, Pavithra; Peng, Bo
• Other Names: Ashbrook Daniel Academic Editor.
• Is Part Of: Advances in human-computer interaction. Volume 2009(2009)
• Publication Date: 2009-02-24
• Subjects: Human-computer interaction -- Periodicals; Human-computer interaction; Periodicals; Electronic journals; 004.019
• DOI: 10.1155/2009/362651
• Languages: English
• ISSNs: 1687-5893

Abstract

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.