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Discovering human routines from cell phone data with topic models

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2 Author(s)

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 ldquogoing to work early/laterdquo, ldquobeing home all dayrdquo, ldquoworking constantlyrdquo, ldquoworking sporadicallyrdquo and ldquomeeting at lunch timerdquo.

Published in:

Wearable Computers, 2008. ISWC 2008. 12th IEEE International Symposium on

Date of Conference:

Sept. 28 2008-Oct. 1 2008