Research Article
RoutineSense: A Mobile Sensing Framework for the Reconstruction of User Routines
@ARTICLE{10.4108/eai.22-7-2015.2260055, author={Jean-Eudes Ranvier and Michele Catasta and Matteo Vasirani and Karl Aberer}, title={RoutineSense: A Mobile Sensing Framework for the Reconstruction of User Routines}, journal={EAI Endorsed Transactions on Ambient Systems}, volume={2}, number={5}, publisher={EAI}, journal_a={AMSYS}, year={2015}, month={8}, keywords={activity modeling, routine detection, mobile sensing}, doi={10.4108/eai.22-7-2015.2260055} }- Jean-Eudes Ranvier
Michele Catasta
Matteo Vasirani
Karl Aberer
Year: 2015
RoutineSense: A Mobile Sensing Framework for the Reconstruction of User Routines
AMSYS
EAI
DOI: 10.4108/eai.22-7-2015.2260055
Abstract
Modern smartphones are powerful platforms that have become part of the everyday life for most people. Thanks to their sensing and computing capabilities, smartphones can unobtrusively identify simple user states (e.g., location, performed activity, etc.), enabling a plethora of applications that provide insights on the lifestyle of the users. In this paper, we introduce routineSense: a system for the automatic reconstruction of complex daily routines from simple user states, implemented as an incremental processing framework. Such framework combines opportunistic sensing and user feedback to discover frequent and exceptional routines that can be used to segment and aggregate multiple user activities in a timeline. We use a comprehensive dataset containing rich geographic information to assess the feasibility and performance of routineSense, showing a near threefold improvement on the current state-of-the-art.
Copyright © 2015 J-E. Ranvier al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
