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In this paper, we investigate the use of proxemics and dynamics forautomatically identifying conversing groups, or so-called F-formations. Moreformally we aim to automatically identify whether wearable sensor data comingfrom 2 people is indicative of F-formation membership. We also explore theproblem of jointly detecting membership and more descriptive information aboutthe pair relating to the role they take in the conversation (i.e. speaker orlistener). We jointly model the concepts of proxemics and dynamics using binaryproximity and acceleration obtained through a single wearable sensor perperson. We test our approaches on the publicly available MatchNMingle datasetwhich was collected during real-life mingling events. We find out that fusionof these two modalities performs significantly better than them independently,providing an AUC of 0.975 when data from 30-second windows are used.Furthermore, our investigation into roles detection shows that each role pairrequires a different time resolution for accurate detection.

Detecting F-formations & Roles in Crowded Social Scenes with Wearables: Combining Proxemics & Dynamics using LSTMs