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Publication . Article . 2020

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

Paolo Baronti; Paolo Barsocchi; Stefano Chessa; Antonino Crivello; Michele Girolami; Fabio Mavilia; Filippo Palumbo;
Closed Access
English
Published: 22 May 2020 Journal: Journal of Ambient Intelligence and Smart Environments, volume 12, issue 3, pages 203-217 (issn: 18761364, eissn: 18761372, Copyright policy )
Country: Italy
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%.
Subjects by Vocabulary

Microsoft Academic Graph classification: Computer science Beacon Real-time computing Bluetooth Low Energy computer.internet_protocol computer Remote detection

Subjects

Software, Proximity, Bluetooth low energy, Social interactions, Proximity, Bluetooth low energy, Social interactions

Funded by
EC| NESTORE
Project
NESTORE
Novel Empowering Solutions and Technologies for Older people to Retain Everyday life activities
  • Funder: European Commission (EC)
  • Project Code: 769643
  • Funding stream: H2020 | RIA
Validated by funder
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Article . 2020
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