A Quantitative Method for Revealing and Comparing Places in the Home

  • Ryan Aipperspach
  • Tye Rattenbury
  • Allison Woodruff
  • John Canny
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4206)

Abstract

Increasing availability of sensor-based location traces for individuals, combined with the goal of better understanding user context, has resulted in a recent emphasis on algorithms for automatically extracting users’ significant places from location data. Place-finding can be characterized by two sub-problems, (1) finding significant locations, and (2) assigning semantic labels to those locations (the problem of “moving from location to place”) [8]. Existing algorithms focus on the first sub-problem and on finding city-level locations. We use a principled approach in adapting Gaussian Mixture Models (GMMs) to provide a first solution for finding significant places within the home, based on the first set of long-term, precise location data collected from several homes. We also present a novel metric for quantifying the similarity between places, which has the potential to assign semantic labels to places by comparing them to a library of known places. We discuss several implications of these new techniques for the design of Ubicomp systems.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ryan Aipperspach
    • 1
    • 2
  • Tye Rattenbury
    • 1
  • Allison Woodruff
    • 2
  • John Canny
    • 1
  1. 1.Berkeley Institute of Design, Computer Science DivisionUniversity of CaliforniaBerkeleyUSA
  2. 2.Intel ResearchBerkeleyUSA

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