%Aigaion2 BibTeX export from Idiap Publications
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         author = {Farrahi, Katayoun and Gatica-Perez, Daniel},
       projects = {Idiap},
          title = {Discovering Human Routines from Cell Phone Data with Topic Models},
           type = {Idiap-RR},
         number = {Idiap-RR-32-2008},
           year = {2008},
    institution = {IDIAP},
       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".},
            pdf = {https://publications.idiap.ch/attachments/reports/2008/farrahi-idiap-rr-08-32.pdf},
     postscript = {ftp://ftp.idiap.ch/pub/reports/2008/farrahi-idiap-rr-08-32.ps.gz},