文章基本信息

标题：A Dynamic Bayesian Approach to Computational Laban Shape Quality Analysis
本地全文：下载
作者：Dilip Swaminathan ; Harvey Thornburg ; Jessica Mumford 等
期刊名称：Advances in Human-Computer Interaction
印刷版ISSN：1687-5893
电子版ISSN：1687-5907
出版年度：2009
卷号：2009
DOI：10.1155/2009/362651
出版社：Hindawi Publishing Corporation
摘要：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.