Understanding human behavior through nonverbal-based features, is interesting in several applications such as surveillance, ambient assisted living and human-robot interaction. In this article in order to analyze human behaviors in social context, we propose a new approach which explores interrelations between body part motions in scenarios with people doing a conversation. The novelty of this method is that we analyze body motion-based features in frequency domain to estimate different human social patterns: Interpersonal Behaviors (IBs) and a Social Role (SR). To analyze the dynamics and interrelations of people's body motions, a human movement descriptor is used to extract discriminative features, and a multi-layer Dynamic Bayesian Network (DBN) technique is proposed to model the existent dependencies. Laban Movement Analysis (LMA) is a well-known human movement descriptor, which provides efficient mid-level information of human body motions. The mid-level information is useful to extract the complex interdependencies. The DBN technique is tested in different scenarios to model the mentioned complex dependencies. The study is applied for obtaining four IBs (Interest, Indicator, Empathy and Emphasis) to estimate one SR (Leading).The obtained results give a good indication of the capabilities of the proposed approach for people interaction analysis with potential applications in human-robot interaction.