Interrelation Analysis for Interpersonal Behaviour Understanding in Social Context

In this paper we study a probabilistic approach to characterize Interpersonal Behaviours (IBs) in a social concept by exploring the existent interrelation between body motion features. Human activities were explored in different level of complexities, such as social-based human activity. To bridge the existent big gap between human body motions and the IBs analysis, a set of proper dependencies definition between the features is vital. Inspired in the works of Alex Pentland and Rudolph Laban, we proposed a couple of layers of analysis. In the first layer, we analyse human body parts motions based on a known body motion descriptor, Laban Movement analysis (LMA). LMA composes a set of components which provides different types of human movement features. We investigated the interrelation between those LMA features of a couple of persons to provide a proper model to estimate the IBs in the second layer. To reach the goal, LMA components are used as body motion features. To computerize the model, Dynamic Bayesian Network (DBN) approach is used, because of its flexibility in development and implementation of the dependencies and interrelations. The results show the importance of the interrelations to have more accurate results of the IBs estimations.