Human Machine Interaction based on Bayesian Analysis of Human Movements

We present as a contribution to the field of human-machine interaction a system that analyzes human movements online, 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 gesture recognition. Nowadays technology offers an incredible number of applications to be used in human-machine interaction. Still, it is difficult to find implemented cognitive processes that benefit from those possibilities. Future approaches must offer to the user an effortless and intuitive way of interaction. 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. It also provides the possibility to anticipate the performed action given the observed features. We present results of our system through its embodiment in the social robot ’Nicole’ in the context of a person performing gestures and ’Nicole’ reacting by means of audio output and robot movement.