Affiliation:
1. Arts, Media, and Engineering Program, Department of Electrical Engineering, Arizona State University, Tempe, AZ 85287, USA
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 becomeembodiedin 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 inResponse, 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.
Funder
National Science Foundation
Subject
Human-Computer Interaction
Cited by
15 articles.
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1. Students’ Mathematical Thinking in Movement;International Journal of Research in Undergraduate Mathematics Education;2024-01-27
2. Human walking analysis in a random walk framework;Measurement;2023-10
3. Laban Movement Analysis applied to Human-Computer Interaction;2020 22nd Symposium on Virtual and Augmented Reality (SVR);2020-11
4. The role of respiration audio in multimodal analysis of movement qualities;Journal on Multimodal User Interfaces;2019-04-11
5. Embodied Learning: Somatically Informed Instructional Design;Perspectives on Wearable Enhanced Learning (WELL);2019