Contextual Grouping: Discovering Real-Life Interaction Types from Longitudinal Bluetooth Data

Trinh Minh Tri Do, Daniel Gatica-Perez
2011 2011 IEEE 12th International Conference on Mobile Data Management  
By exploiting built-in sensors, mobile smartphone have become attractive options for large-scale sensing of human behavior as well as social interaction. In this paper, we present a new probabilistic model to analyze longitudinal dynamic social networks created by the physical proximity of people sensed continuously by the phone Bluetooth sensors. A new probabilistic model is proposed in order to jointly infer emergent grouping modes of the community together with their temporal context. We
more » ... ral context. We present experimental results on a Bluetooth proximity network sensed with mobile smart-phones over 9 months of continuous real-life, and show the effectiveness of our method.
doi:10.1109/mdm.2011.18 dblp:conf/mdm/DoG11 fatcat:fzbs5hperbg3tljvhjg3tsekxa