  In this paper, we investigate the use of proxemics and dynamics for
automatically identifying conversing groups, or so-called F-formations. More
formally we aim to automatically identify whether wearable sensor data coming
from 2 people is indicative of F-formation membership. We also explore the
problem of jointly detecting membership and more descriptive information about
the pair relating to the role they take in the conversation (i.e. speaker or
listener). We jointly model the concepts of proxemics and dynamics using binary
proximity and acceleration obtained through a single wearable sensor per
person. We test our approaches on the publicly available MatchNMingle dataset
which was collected during real-life mingling events. We find out that fusion
of 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 pair
requires a different time resolution for accurate detection.
