Computational Modeling of Synchronization and Leadership Dynamics in Small Group Interactions

This doctoral thesis introduces huSync, a computational framework developed to assess non-verbal communication dynamics in small groups quantitatively. It aspires to contribute to Interaction Design and Human-Computer Interaction (HCI), focused on bridging computer science techniques, embodied design and somatics. huSync employs pose estimation algorithms to interpret movement trajectories from video sequences, offering a non-intrusive way to study entrainment in small-group settings based on established conceptual frameworks. Joint actions in musical ensembles serve as the primary case study to explore how non-verbal body cues particularly influence interpersonal coordination and the directionality of information flow. These musical joint actions are exemplary instances of 'self-managed groups', illustrating the complex relationships between musical structure, entrainment dynamics, and mutual influence among ensemble members. These musical interactions are central to the research, serving as a universal language to understand nuanced human behaviours. Methods and results derived from three distinct studies on dyadic group dynamics are also presented. Recent studies are making improvements in computationally modelling human behavior, sharing interesting techniques and approaches, and continues to remain an open area for research. huSync provides a computational methodology and approach to model these behavioural mechanisms in small-group setups, and in this thesis, we present musical ensembles as a use case. By studying these subtle interactions in group settings, we begin to get a clearer perspective on how groups connect and interact. The potential of huSync lies in its capability to make these intangible elements tangible, offering a fresh perspective on the subtleties of small-group interactions. huSync is versatile, managing diverse data sources and identifying essential movement attributes characteristic of group interactions, combining multi-modal signals, feature extraction, entrainment measurement, and analysis validation. It extends its applications to healthcare projects, emphasizing music's universal role in non-verbal communication and interdisciplinary studies by integrating technology with economics, psychology, and the arts. It aims to bridge diverse disciplines, suggesting new paths for research in human movement sciences, especially regarding the use of markerless technologies in behaviorally driven computational research. The findings from huSync are reliable and provide an alternative means for analysing human body movements, aiding in deepening the understanding of small-group dynamics and the elements contributing to successful collaborations. huSync seeks to provide dependable insights into evolving human behaviours by centralising the human body in small-group interaction-related contexts.