A Dynamic Bayesian Approach to Computational Laban Shape Quality Analysis
Autor: | Dilip Swaminathan, Harvey Thornburg, Jessica Mumford, Stjepan Rajko, Jodi James, Todd Ingalls, Ellen Campana, Gang Qian, Pavithra Sampath, Bo Peng |
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Jazyk: | angličtina |
Rok vydání: | 2009 |
Předmět: | |
Zdroj: | Advances in Human-Computer Interaction, Vol 2009 (2009) |
Druh dokumentu: | article |
ISSN: | 1687-5893 1687-5907 |
DOI: | 10.1155/2009/362651 |
Popis: | 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. |
Databáze: | Directory of Open Access Journals |
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