Contributo in atti di convegno, 2020, ENG, 10.1109/PerComWorkshops48775.2020.9156095

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

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

CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy

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.

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

Keywords

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

CNR authors

Barsocchi Paolo, Girolami Michele, Crivello Antonino, Mavilia Fabio

CNR institutes

ISTI – Istituto di scienza e tecnologie dell'informazione "Alessandro Faedo"

ID: 434524

Year: 2020

Type: Contributo in atti di convegno

Creation: 2020-10-22 15:58:01.000

Last update: 2021-12-17 17:21:29.000

External IDs

CNR OAI-PMH: oai:it.cnr:prodotti:434524

DOI: 10.1109/PerComWorkshops48775.2020.9156095

Scopus: 2-s2.0-85091995011