A Dynamic Bayesian Approach to Computational Laban Shape Quality Analysis

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.

Laban movement analysis and hidden Markov models for dynamic 3D gesture recognition

“…Such methods require the use of machine learning techniques to infer expressive representations from low/mid-level features. In [21], a Bayesian fusion approach is used that fuses body motion features for identifying the shape movement quality from dancer improvisations. In [22], four neural networks are exploited.…”

Machine Learning of Personal Gesture Variation in Music Conducting

“…Movement theory, such as Laban Movement Analysis (LMA), provides a high-level representation [14] which can be used to train gesture recognition systems for discrete control [15]. In other work, heuristics from experts in the field, such as choreographers, are used to specify movement qualities to train learning algorithms to track gestures in real time [1].…”

Motion Modeling for Expressive Interaction

“…Swaminathan et al [14] propose a dynamic bayesian network for recognizing qualities and ambiguity between qualities. Visell et al [15] propose a particle filtering based method for inferring non-linear motion dynamics.…”