Discovering human routines from cell phone data with topic models

Katayoun Farrahi, Daniel Gatica-Perez
2008 2008 12th IEEE International Symposium on Wearable Computers  
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)
more » ... proximity-driven (from Bluetooth information) routines in an unsupervised manner. The resulting topics meaningfully characterize some of the underlying cooccurrence structure of the activities in the dataset, including "going to
doi:10.1109/iswc.2008.4911580 dblp:conf/iswc/FarrahiG08 fatcat:2mgwnt4rfvds5p5dpfi743fm6u