Mobility Detection Using Everyday GSM Traces

  • Timothy Sohn
  • Alex Varshavsky
  • Anthony LaMarca
  • Mike Y. Chen
  • Tanzeem Choudhury
  • Ian Smith
  • Sunny Consolvo
  • Jeffrey Hightower
  • William G. Griswold
  • Eyal de Lara
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4206)


Recognition of everyday physical activities is difficult due to the challenges of building informative, yet unobtrusive sensors. The most widely deployed and used mobile computing device today is the mobile phone, which presents an obvious candidate for recognizing activities. This paper explores how coarse-grained GSM data from mobile phones can be used to recognize high-level properties of user mobility, and daily step count. We demonstrate that even without knowledge of observed cell tower locations, we can recognize mobility modes that are useful for several application domains. Our mobility detection system was evaluated with GSM traces from the everyday lives of three data collectors over a period of one month, yielding an overall average accuracy of 85%, and a daily step count number that reasonably approximates the numbers determined by several commercial pedometers.


Mobile Phone Signal Strength Data Collector Activity Recognition Step Count 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Timothy Sohn
    • 1
  • Alex Varshavsky
    • 2
  • Anthony LaMarca
    • 3
  • Mike Y. Chen
    • 3
  • Tanzeem Choudhury
    • 3
  • Ian Smith
    • 3
  • Sunny Consolvo
    • 3
  • Jeffrey Hightower
    • 3
  • William G. Griswold
    • 1
  • Eyal de Lara
    • 2
  1. 1.Computer Science and EngineeringUniversity of CaliforniaSan Diego, La JollaUSA
  2. 2.Computer ScienceUniversity of TorontoTorontoCanada
  3. 3.Intel ResearchSeattleUSA

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