Unsupervised context detection using wireless signals
Pervasive and Mobile Computing
Discovering routines from large-scale human locations using probabilistic topic models
ACM Transactions on Intelligent Systems and Technology (TIST)
Discovering human places of interest from multimodal mobile phone data
Proceedings of the 9th International Conference on Mobile and Ubiquitous Multimedia
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We present a framework to automatically discover people's routines from information extracted by cell phones. The framework is built from a probabilistic topic model learned on novel bag type representations of activity-related cues (location, proximity and their temporal variations over a day) of peoples' daily routines. Using real-life data from the Reality Mining dataset, covering 68 000+ hours of human activities, we can successfully discover location-driven (from cell tower connections) and proximity-driven (from Bluetooth information) routines in an unsupervised manner. The resulting topics meaningfully characterize some of the underlying co-occurrence structure of the activities in the dataset, including “going to work early/late”, “being home all day”, “working constantly”, “working sporadically” and “meeting at lunch time”.