MeetSense: A Lightweight Framework for Group Identification using Smartphones

TL;DR

MeetSense is a lightweight, smartphone-based framework that accurately identifies social groups in real-world settings by analyzing acoustic context and proximity, achieving nearly 90% accuracy.

Contribution

This paper introduces MeetSense, a novel unsupervised method leveraging acoustic signals and network science for efficient group detection on smartphones.

Findings

Achieves close to 90% accuracy in real-life scenarios

Effectively filters incorrect groups using modularity measures

Operates efficiently in noisy environments

Abstract

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.