Contextual grouping: discovering real-life interaction types from longitudinal Bluetooth data

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 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.


Presented at:
12th International Conference on Mobile Data Management
Year:
2011
Laboratories:




 Record created 2011-05-19, last modified 2018-09-13

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