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

2020

Journal article Open Access

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

Baronti P., Barsocchi P., Chessa S., Crivello A., Girolami M., Mavilia F., Palumbo F.

Bluetooth low energy Proximity Social interactions Software

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%.

Source: Journal of ambient intelligence and smart environments (Print) 12 (2020): 203–217. doi:10.3233/AIS-200560

Publisher: IOS Press, Amsterdam , Paesi Bassi

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@article{oai:it.cnr:prodotti:424549,
	title = {Remote detection of social interactions in indoor environments through bluetooth low energy beacons},
	author = {Baronti P. and Barsocchi P. and Chessa S. and Crivello A. and Girolami M. and Mavilia F. and Palumbo F.},
	publisher = {IOS Press, Amsterdam , Paesi Bassi},
	doi = {10.3233/ais-200560},
	journal = {Journal of ambient intelligence and smart environments (Print)},
	volume = {12},
	pages = {203–217},
	year = {2020}
}