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Discovering human routines from cell phone data with topic models

Discovering human routines from cell phone data with topic models
Discovering human routines from cell phone data with topic models

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".

29-32
IEEE
Farrahi, Katayoun
bc848b9c-fc32-475c-b241-f6ade8babacb
Gatica-Perez, Daniel
583e99b0-abef-4d2a-b54f-70ab7b498975
Farrahi, Katayoun
bc848b9c-fc32-475c-b241-f6ade8babacb
Gatica-Perez, Daniel
583e99b0-abef-4d2a-b54f-70ab7b498975

Farrahi, Katayoun and Gatica-Perez, Daniel (2008) Discovering human routines from cell phone data with topic models. In Proceedings - 12th IEEE International Symposium on Wearable Computers, ISWC 2008. IEEE. pp. 29-32 . (doi:10.1109/ISWC.2008.4911580).

Record type: Conference or Workshop Item (Paper)

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".

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More information

Published date: 1 December 2008
Venue - Dates: 12th IEEE International Symposium on Wearable Computers, ISWC 2008, United States, 2008-09-27 - 2008-09-30

Identifiers

Local EPrints ID: 430375
URI: http://eprints.soton.ac.uk/id/eprint/430375
PURE UUID: d3394cd5-fdfb-43af-a57d-f2359e00ac17

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Date deposited: 26 Apr 2019 16:30
Last modified: 07 Oct 2020 00:30

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