Published in

2008 12th IEEE International Symposium on Wearable Computers

DOI: 10.1109/iswc.2008.4911580

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Discovering Human Routines from Cell Phone Data with Topic Models

Journal article published in 2008 by Katayoun Farrahi ORCID, Daniel Gatica-Perez
This paper is available in a repository.
This paper is available in a repository.

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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".