Remote detection of social interactions in indoor environments through bluetooth low energy beacons

Issue title: Impact of Sensor Data in Intelligent Environments

Article type: Research Article

Authors: Baronti, Paolo^a | Barsocchi, Paolo^a | Chessa, Stefano^b | Crivello, Antonino^a | Girolami, Michele^{a; *} | Mavilia, Fabio^a | Palumbo, Filippo^a

Affiliations: [a] Italian National Council of Research, ISTI-CNR, Pisa, Italy. E-mails: Paolo.Baronti@isti.cnr.it, Paolo.Barsocchi@isti.cnr.it, Antonino.Crivello@isti.cnr.it, Michele.Girolami@isti.cnr.it, Fabio.Mavilia@isti.cnr.it, Filippo.Palumbo@isti.cnr.it | [b] Department of Computer Science, University of Pisa, Pisa, Italy. E-mail: stefano.chessa@unipi.it

Correspondence: [*] Corresponding author. E-mail: Michele.Girolami@isti.cnr.it.

Abstract: The way people interact in daily life is a challenging phenomenon to be captured and studied without altering the natural rhythm of the interactions. We investigate the development of automated tools that may provide information to the researchers that analyse interactions among humans. One important requirement of these tools is that should not interfere with the subjects under observation, in order to avoid any alteration in the subject’s normal behaviour. Our approach is based on the detection of proximity among groups of people that is obtained using commercial wearable wireless tags based on Bluetooth Low Energy (BLE) and a novel algorithm called Remote Detection of Human Proximity (ReD-HuP) that analyses the wireless signal of tags and produce the proximity information. The algorithm, which has been validated against the ground truth of an experimental dataset, achieves an accuracy of 95.91% and an F-Score of 95.79%.

Keywords: Proximity, bluetooth low energy, social interactions

DOI: 10.3233/AIS-200560

Journal: Journal of Ambient Intelligence and Smart Environments, vol. 12, no. 3, pp. 203-217, 2020