amsys 15(5): e5

Research Article

RoutineSense: A Mobile Sensing Framework for the Reconstruction of User Routines

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  • @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
Jean-Eudes Ranvier1,*, Michele Catasta1, Matteo Vasirani1, Karl Aberer1
  • 1: EPFL
*Contact email: jean-eudes.ranvier@epfl.ch

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.