Sensing social interactions through BLE beacons and commercial mobile devices

Michele Girolami; Fabio Mavilia; Franca Delmastro

doi:10.1016/j.pmcj.2020.101198

Sensing social interactions through BLE beacons and commercial mobile devices

Pervasive Mob Comput. 2020 Sep:67:101198. doi: 10.1016/j.pmcj.2020.101198. Epub 2020 Jun 20.

Authors

Michele Girolami¹, Fabio Mavilia¹, Franca Delmastro²

Affiliations

¹ Institute of Information Science and Technologies, National Research Council (ISTI-CNR), Pisa, Italy.
² Institute of Informatics and Telematics, National Research Council (IIT-CNR), Pisa, Italy.

Abstract

Wearable sensing devices can provide high-resolution data useful to characterise and identify complex human behaviours. Sensing human social interactions through wearable devices represents one of the emerging field in mobile social sensing, considering their impact on different user categories and on different social contexts. However, it is important to limit the collection and use of sensitive information characterising individual users and their social interactions in order to maintain the user compliance. For this reason, we decided to focus mainly on physical proximity and, specifically, on the analysis of BLE wireless signals commonly used by commercial mobile devices. In this work, we present the SocializeME framework designed to collect proximity information and to detect social interactions through heterogeneous personal mobile devices. We also present the results of an experimental data collection campaign conducted with real users, highlighting technical limitations and performances in terms of quality of RSS, packet loss, and channel symmetry, and how they are influenced by different configurations of the user's body and the position of the personal device. Specifically, we obtained a dataset with more than 820.000 Bluetooth signals (BLE beacons) collected, with a total monitoring of over 11 h. The dataset collected reproduces 4 different configurations by mixing two user posture's layouts (standing and sitting) and different positions of the receiver device (in hand, in the front pocket and in the back pocket). The large number of experiments in those different configurations, well cover the common way of holding a mobile device, and the layout of a dyad involved in a social interaction. We also present the results obtained by SME-D algorithm, designed to automatically detect social interactions based on the collected wireless signals, which obtained an overall accuracy of 81.56% and F-score 84.7%. The collected and labelled dataset is also released to the mobile social sensing community in order to evaluate and compare new algorithms.

Keywords: Bluetooth Low Energy; Human proximity; Social interactions; Wearable sensors.