Habitualisation: localisation without location data


McGrath, Rory and Coffey, Cathal and Pozdnoukhov, Alexei (2012) Habitualisation: localisation without location data. In: Nokia MDC challenge at PERVASIVE'2012, June 18-22 2012, Newcastle University, UK. (Unpublished)

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Abstract

This paper looks at identifying the locations of users from the Nokia MDC dataset throughout the day without taking into consideration location based data. By looking at a users habits and idiosyncrasies we determined the likelihood of a users location within known stay regions which we call habitats. The features used to determine location were extracted from a users interaction with the smart phone. None of the features contained a users locations or a users proximity to objects with known locations. Using a set of structured output support vector learning techniques we found that a users location with respect to the areas of typical activities is well predictable solely from daily routines and a smart phone usage habits.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Research presented in this paper was funded in part by Science Foundation Ireland Strategic Research Cluster grant 07/SRC/I1168 and 11/RFP.1/CMS/3247 award, and IBM PhD Fellowship program. The authors gratefully thank Aonghus Lawlor and Felix Kling for their support, fruitful discussions and help with software.
Keywords: machine learning; kernel methods; smart cities; pervasive computing;
Academic Unit: Faculty of Science and Engineering > Research Institutes > National Centre for Geocomputation, NCG
Item ID: 3928
Depositing User: Dr Alexei Pozdnoukhov
Date Deposited: 04 Oct 2012 08:50
Refereed: No
URI:

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