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Daily routine classification from mobile phone data

Daily routine classification from mobile phone data
Daily routine classification from mobile phone data

The automatic analysis of real-life, long-term behavior and dynamics of individuals and groups from mobile sensor data constitutes an emerging and challenging domain. We present a framework to classify people's daily routines (defined by day type, and by group affiliation type) from real-life data collected with mobile phones, which include physical location information (derived from cell tower connectivity), and social context (given by person proximity information derived from Bluetooth). We propose and compare single- and multi-modal routine representations at multiple time scales, each capable of highlighting different features from the data, to determine which best characterized the underlying structure of the daily routines. Using a massive data set of 87000+ hours spanning four months of the life of 30 university students, we show that the integration of location and social context and the use of multiple time-scales used in our method is effective, producing accuracies of over 80% for the two daily routine classification tasks investigated, with significant performance differences with respect to the single-modal cues.

0302-9743
173-184
Springer-Verlag Berlin Heidelberg
Farrahi, Katayoun
bc848b9c-fc32-475c-b241-f6ade8babacb
Gatica-Perez, Daniel
583e99b0-abef-4d2a-b54f-70ab7b498975
Popescu-Belis, Andrei
Stiefelhagen, Rainer
Farrahi, Katayoun
bc848b9c-fc32-475c-b241-f6ade8babacb
Gatica-Perez, Daniel
583e99b0-abef-4d2a-b54f-70ab7b498975
Popescu-Belis, Andrei
Stiefelhagen, Rainer

Farrahi, Katayoun and Gatica-Perez, Daniel (2008) Daily routine classification from mobile phone data. Popescu-Belis, Andrei and Stiefelhagen, Rainer (eds.) In Machine Learning for Multimodal Interaction - 5th International Workshop, MLMI 2008, Proceedings. vol. 5237 LNCS, Springer-Verlag Berlin Heidelberg. pp. 173-184 . (doi:10.1007/978-3-540-85853-9-16).

Record type: Conference or Workshop Item (Paper)

Abstract

The automatic analysis of real-life, long-term behavior and dynamics of individuals and groups from mobile sensor data constitutes an emerging and challenging domain. We present a framework to classify people's daily routines (defined by day type, and by group affiliation type) from real-life data collected with mobile phones, which include physical location information (derived from cell tower connectivity), and social context (given by person proximity information derived from Bluetooth). We propose and compare single- and multi-modal routine representations at multiple time scales, each capable of highlighting different features from the data, to determine which best characterized the underlying structure of the daily routines. Using a massive data set of 87000+ hours spanning four months of the life of 30 university students, we show that the integration of location and social context and the use of multiple time-scales used in our method is effective, producing accuracies of over 80% for the two daily routine classification tasks investigated, with significant performance differences with respect to the single-modal cues.

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More information

Published date: 25 December 2008
Venue - Dates: 5th International Workshop on Machine Learning for Multimodal Interaction, MLMI 2008, Netherlands, 2008-09-07 - 2008-09-09

Identifiers

Local EPrints ID: 430376
URI: http://eprints.soton.ac.uk/id/eprint/430376
ISSN: 0302-9743
PURE UUID: 318f41fa-dcbc-4190-b6e4-176577cc9586

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Date deposited: 26 Apr 2019 16:30
Last modified: 07 Oct 2020 00:37

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Contributors

Author: Katayoun Farrahi
Author: Daniel Gatica-Perez
Editor: Andrei Popescu-Belis
Editor: Rainer Stiefelhagen

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