A Dynamic Bayesian Approach to Computational Laban Shape Quality Analysis
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 throu...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2009-01-01
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| Series: | Advances in Human-Computer Interaction |
| Online Access: | http://dx.doi.org/10.1155/2009/362651 |
| Summary: | 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. |
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| ISSN: | 1687-5893 1687-5907 |