Recognizing conversational context in group interaction using privacy-sensitive mobile sensors

The availability of mobile sociometric sensors allows Computer-Supported Cooperative Work (CSCW) designers the possibility to enhance online meeting support through automatic recognition of conversational context. This paper addresses the task of discriminating one conversational context against another, specifically brainstorming from decision-making interactions using easily computable nonverbal behavioral cues. We hypothesize that the difference in the dynamics between brainstorming and decision-making discussions is significant and measurable using speech activity based nonverbal cues. We employ a set of nonverbal cues to characterize the entire group by the aggregation (both temporal and person-wise) of their nonverbal behavior. Our results on a dataset collected using privacy-sensitive sociometric badges shows that the floor-occupation patterns in a brain-storming interaction is different from a decision-making interaction and we can obtain an accuracy as high as 87.5%.