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}, |
| Keywords: | |
| Projects |
Idiap |
| Authors | |
| Added by: | [UNK] |
| Total mark: | 0 |
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