Laban Movement Analysis for multi-ocular systems

We present as a contribution to the field of human-machine interaction a system that analyzes human movements online through multiple observers, based on the concept of Laban Movement Analysis (LMA). The implementation uses a Bayesian model for learning and classification, while the results are presented for the application to analyze expressive movements. In sports like Karate four judges are placed in the corners to observe the fight to ensure that the overall judgment is correct. In this paper we propose a multi-ocular system where each sub-system observes a movement from a different monocular perspective. The sub-systems send continuously guesses in form of probability distributions to the central system. The central system fuses the evidences and presents the final result. We present the Laban Movement Analysis as a concept to identify useful features of human movements to classify human actions. The movements are extracted using both, vision and magnetic tracker. The descriptor opens possibilities towards expressiveness and emotional content. To solve the problem of classification we use the Bayesian framework as it offers an intuitive approach to learning and classification. The presented work targets applications like social robots, smart houses and surveillance.