Discovering human routines from cell phone data with topic models

Farrahi, Katayoun and Gatica-Perez, Daniel (2008) Discovering human routines from cell phone data with topic models. In Proceedings - 12th IEEE International Symposium on Wearable Computers, ISWC 2008. IEEE. pp. 29-32 . (doi:10.1109/ISWC.2008.4911580).

Record type: Conference or Workshop Item (Paper)

Abstract

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".

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