Document - Detecting Social Interactions in Indoor Environments with the Red-HuP Algorithm

2020

Conference article Closed Access

Detecting Social Interactions in Indoor Environments with the Red-HuP Algorithm

Barsocchi P., Crivello A., Girolami M., Mavilia F.

pattern classification Bluetooth Proximity social sciences Social Interactions indoor environment learning (artificial intelligence) Bluetooth Low Energy

Detecting social interactions among people represents a challenging task. In this study we evaluate the performance of the ReD-HuP algorithm. We study a real-world and useful experimental dataset and we provide a comparison with some classification methods. Interactions are inferred from co-location of people by exploiting Bluetooth Low Energy (BLE) beacons. Our analysis investigates how the different transmission powers affect the overall performance, we also analyze the results by varying the width of the time window used to analyze BLE beacons. Results obtained with the ReD-HuP algorithm have been compared against two well known and wide adopted machine learning classification methods.

Source: 2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), Austin, TX, USA, USA, 23-27 March 2020

Publisher: IEEE Communications Society, Piscataway, USA

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@inproceedings{oai:it.cnr:prodotti:434524,
	title = {Detecting Social Interactions in Indoor Environments with the Red-HuP Algorithm},
	author = {Barsocchi P. and Crivello A. and Girolami M. and Mavilia F.},
	publisher = {IEEE Communications Society, Piscataway, USA},
	doi = {10.1109/percomworkshops48775.2020.9156095},
	booktitle = {2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), Austin, TX, USA, USA, 23-27 March 2020},
	year = {2020}
}