  In an organization, individuals prefer to form various formal and informal
groups for mutual interactions. Therefore, ubiquitous identification of such
groups and understanding their dynamics are important to monitor activities,
behaviours and well-being of the individuals. In this paper, we develop a
lightweight, yet near-accurate, methodology, called MeetSense, to identify
various interacting groups based on collective sensing through users'
smartphones. Group detection from sensor signals is not straightforward because
users in proximity may not always be under the same group. Therefore, we use
acoustic context extracted from audio signals to infer interaction pattern
among the subjects in proximity. We have developed an unsupervised and
lightweight mechanism for user group detection by taking cues from network
science and measuring the cohesivity of the detected groups in terms of
modularity. Taking modularity into consideration, MeetSense can efficiently
eliminate incorrect groups, as well as adapt the mechanism depending on the
role played by the proximity and the acoustic context in a specific scenario.
The proposed method has been implemented and tested under many real-life
scenarios in an academic institute environment, and we observe that MeetSense
can identify user groups with close to 90% accuracy even in a noisy
environment.
