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Journal of Sensors
Volume 2016 (2016), Article ID 6156914, 15 pages
Research Article

Energy Optimization for Outdoor Activity Recognition

1LIARA Laboratory, University of Quebec at Chicoutimi, Chicoutimi, QC, Canada G7H 2B1
2LICEF Research Center, Tele-Universite of Quebec, Quebec, QC, Canada G1K 9H6
3DOMUS Laboratory, University of Sherbrooke, Sherbrooke, QC, Canada J1K 2R1

Received 20 December 2015; Accepted 30 March 2016

Academic Editor: Chehri Abdellah

Copyright © 2016 Mehdi Boukhechba et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The mobile phone is no longer only a communication device, but also a powerful environmental sensing unit that can monitor a user’s ambient context. Mobile users take their devices with them everywhere which increases the availability of persons’ traces. Extracting and analyzing knowledge from these traces represent a strong support for several applications domains, ranging from traffic management to advertisement and social studies. However, the limited battery capacity of mobile devices represents a big hurdle for context detection, no matter how useful the service may be. We present a novel approach to online recognizing users’ outdoor activities without depleting the mobile resources. We associate the places visited by individuals during their movements with meaningful human activities using a novel algorithm that clusters incrementally user’s moves into different types of activities. To optimize the battery consumption, the algorithm behaves variably on the basis of users’ behaviors and the remaining battery level. Studies using real GPS records from two big datasets demonstrate that the proposal is effective and is capable of inferring human activities without draining the phone resources.