CONF farrahi:iswc:2008/IDIAP Discovering Human Routines from Cell Phone Data with Topic Models Farrahi, Katayoun Gatica-Perez, Daniel EXTERNAL http://publications.idiap.ch/attachments/papers/2008/farrahi-iswc-2008.pdf PUBLIC http://publications.idiap.ch/index.php/publications/showcite/farrahi:rr08-32 Related documents IEEE International Symposium on Wearable Computers (ISWC) 2008 IDIAP-RR 08-32 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". REPORT farrahi:rr08-32/IDIAP Discovering Human Routines from Cell Phone Data with Topic Models Farrahi, Katayoun Gatica-Perez, Daniel EXTERNAL http://publications.idiap.ch/attachments/reports/2008/farrahi-idiap-rr-08-32.pdf PUBLIC Idiap-RR-32-2008 2008 IDIAP 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".