Contributo in atti di convegno, 2020, ENG, 10.1109/PerComWorkshops48775.2020.9156095
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
Bluetooth, indoor environment, learning (artificial intelligence), pattern classification, social sciences, Social Interactions, Bluetooth Low Energy, Proximity
Barsocchi Paolo, Girolami Michele, Crivello Antonino, Mavilia Fabio
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 links
OAI-PMH: Dublin Core
OAI-PMH: Mods
OAI-PMH: RDF
External IDs
CNR OAI-PMH: oai:it.cnr:prodotti:434524
DOI: 10.1109/PerComWorkshops48775.2020.9156095
Scopus: 2-s2.0-85091995011