This paper describes a data collection campaign and a dataset of BLE beacons for detecting and analysing human social interactions. The dataset has been collected by involving 15 volunteers that interacted in indoor environments for a total of 11 hours of activity. The dataset is released as a collection of CSV files with a timestamp, RSSI (Received Signal Strength Indicator) and a unique identifier of the emitting and of the receiving devices. Volunteers wear a wristband equipped with BLE tags emitting beacons at a fixed rate, and a mobile application able to collect and to store beacons. We organized 6 interaction sessions, designed to reproduce the three common stages of an interaction (Non Interaction, Approaching and Interaction). Moreover, we reproduced interactions by varying the volunteer's posture as well as the position of the receiving device. The dataset is released with a ground truth annotation reporting the exact time intervals during which volunteers actually interacted. The combination of such factors, provides a rich dataset useful to experiment algorithms for detecting interactions and for analyzing dynamics of interactions in a real-world setting. We present in detail the dataset and its evaluation in "Sensing Social Interactions through BLE Beacons and Commercial Mobile Devices", in which we focus on two orthogonal analysis: quality of the dataset and RSSI symmetry of the channel during the interaction stage between pairs of users.
Keywords: Bluetooth low energy; Co-location; Social interactions; Social sensing.
© 2020 The Authors.