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...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2009-01-01
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| Series: | Advances in Human-Computer Interaction |
| Online Access: | http://dx.doi.org/10.1155/2009/362651 |
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| _version_ | 1832554264911675392 |
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| author | Dilip Swaminathan Harvey Thornburg Jessica Mumford Stjepan Rajko Jodi James Todd Ingalls Ellen Campana Gang Qian Pavithra Sampath Bo Peng |
| author_facet | Dilip Swaminathan Harvey Thornburg Jessica Mumford Stjepan Rajko Jodi James Todd Ingalls Ellen Campana Gang Qian Pavithra Sampath Bo Peng |
| author_sort | Dilip Swaminathan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-018e305dd1b044cfb737b2298aee9f14 |
| institution | Kabale University |
| issn | 1687-5893 1687-5907 |
| language | English |
| publishDate | 2009-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Human-Computer Interaction |
| spelling | doaj-art-018e305dd1b044cfb737b2298aee9f142025-02-03T05:51:59ZengWileyAdvances in Human-Computer Interaction1687-58931687-59072009-01-01200910.1155/2009/362651362651A Dynamic Bayesian Approach to Computational Laban Shape Quality AnalysisDilip Swaminathan0Harvey Thornburg1Jessica Mumford2Stjepan Rajko3Jodi James4Todd Ingalls5Ellen Campana6Gang Qian7Pavithra Sampath8Bo Peng9Arts, Media, and Engineering Program, Department of Electrical Engineering, Arizona State University, Tempe, AZ 85287, USAArts, Media, and Engineering Program, Department of Electrical Engineering, Arizona State University, Tempe, AZ 85287, USAArts, Media, and Engineering Program, Department of Electrical Engineering, Arizona State University, Tempe, AZ 85287, USAArts, Media, and Engineering Program, Department of Electrical Engineering, Arizona State University, Tempe, AZ 85287, USAArts, Media, and Engineering Program, Department of Electrical Engineering, Arizona State University, Tempe, AZ 85287, USAArts, Media, and Engineering Program, Department of Electrical Engineering, Arizona State University, Tempe, AZ 85287, USAArts, Media, and Engineering Program, Department of Electrical Engineering, Arizona State University, Tempe, AZ 85287, USAArts, Media, and Engineering Program, Department of Electrical Engineering, Arizona State University, Tempe, AZ 85287, USAArts, Media, and Engineering Program, Department of Electrical Engineering, Arizona State University, Tempe, AZ 85287, USAArts, Media, and Engineering Program, Department of Electrical Engineering, Arizona State University, Tempe, AZ 85287, USALaban 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.http://dx.doi.org/10.1155/2009/362651 |
| spellingShingle | Dilip Swaminathan Harvey Thornburg Jessica Mumford Stjepan Rajko Jodi James Todd Ingalls Ellen Campana Gang Qian Pavithra Sampath Bo Peng A Dynamic Bayesian Approach to Computational Laban Shape Quality Analysis Advances in Human-Computer Interaction |
| title | A Dynamic Bayesian Approach to Computational Laban Shape Quality Analysis |
| title_full | A Dynamic Bayesian Approach to Computational Laban Shape Quality Analysis |
| title_fullStr | A Dynamic Bayesian Approach to Computational Laban Shape Quality Analysis |
| title_full_unstemmed | A Dynamic Bayesian Approach to Computational Laban Shape Quality Analysis |
| title_short | A Dynamic Bayesian Approach to Computational Laban Shape Quality Analysis |
| title_sort | dynamic bayesian approach to computational laban shape quality analysis |
| url | http://dx.doi.org/10.1155/2009/362651 |
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