Unobtrusive Analysis of Group Interactions without Cameras

Abstract

Group meetings are often inefficient, unorganized and poorly documented. Factors including "group-think," fear of speaking, unfocused discussion, and bias can affect the performance of a group meeting. In order to actively or passively facilitate group meetings, automatically analyzing group interaction patterns is critical. Existing research on group dynamics analysis still heavily depends on video cameras in the lines of sight of participants or wearable sensors, both of which could affect the natural behavior of participants. In this thesis, we present a smart meeting room that combines microphones and unobtrusive ceiling-mounted Time-of-Flight (ToF) sensors to understand group dynamics in team meetings. Since the ToF sensors are ceiling-mounted and out of the lines of sight of the participants, we posit that their presence would not disrupt the natural interaction patterns of individuals. We collect a new multi-modal dataset of group interactions where participants have to complete a task by reaching a group consensus, and then fill out a post-task questionnaire. We use this dataset for the development of our algorithms and analysis of group meetings. In this paper, we combine the ceiling-mounted ToF sensors and lapel microphones to: (1) estimate the seated body orientation of participants, (2) estimate the head pose and visual focus of attention (VFOA) of meeting participants, (3) estimate the arm pose and body posture of participants, and (4) analyze the multimodal data for passive understanding of group meetings, with a focus on perceived leadership and contribution.