Mobile Context Inference Using Low-Cost Sensors

  • Evan Welbourne
  • Jonathan Lester
  • Anthony LaMarca
  • Gaetano Borriello
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3479)


In this paper, we introduce a compact system for fusing location data with data from simple, low-cost, non-location sensors to infer a user’s place and situational context. Specifically, the system senses location with a GSM cell phone and a WiFi-enabled mobile device (each running Place Lab), and collects additional sensor data using a 2” x 1” sensor board that contains a set of common sensors (e.g. accelerometers, barometric pressure sensors) and is attached to the mobile device. Our chief contribution is a multi-sensor system design that provides indoor-outdoor location information, and which models the capabilities and form factor of future cell phones. With two basic examples, we demonstrate that even using fairly primitive sensor processing and fusion algorithms we can leverage the synergy between our location and non-location sensors to unlock new possibilities for mobile context inference. We conclude by discussing directions for future work.


Mobile Device Cell Phone Activity Recognition Ubiquitous Computing Situational Context 
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 2005

Authors and Affiliations

  • Evan Welbourne
    • 1
  • Jonathan Lester
    • 2
  • Anthony LaMarca
    • 3
  • Gaetano Borriello
    • 1
    • 3
  1. 1.Department of Computer Science & EngineeringUniversity of WashingtonSeattleUSA
  2. 2.Department of Electrical EngineeringUniversity of WashingtonSeattleUSA
  3. 3.Intel Research SeattleSeattleUSA

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