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 [BibTeX] [Marc21]
Discovering Human Routines from Cell Phone Data with Topic Models
Type of publication: Conference paper
Citation: farrahi:iswc:2008
Booktitle: IEEE International Symposium on Wearable Computers (ISWC)
Year: 2008
Note: IDIAP-RR 08-32
Crossref: farrahi:rr08-32:
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".
Userfields: ipdmembership={vision},
Projects Idiap
Authors Farrahi, Katayoun
Gatica-Perez, Daniel
Added by: [UNK]
Total mark: 0
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