Mobile context inference using low-cost sensors

Authors:
Evan Welbourne;Jonathan Lester;Anthony LaMarca;Gaetano Borriello
Affiliations:
Department of Computer Science & Engineering, University of Washington, Seattle, WA;Department of Electrical Engineering, University of Washington, Seattle, WA;Intel Research Seattle, Seattle, WA;Department of Computer Science & Engineering, University of Washington, Seattle, WA
Venue:
LoCA'05 Proceedings of the First international conference on Location- and Context-Awareness
Year:
2005

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Abstract

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.