Mobile Context Inference Using Low-Cost Sensors

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.

[1]  Mike Horton,et al.  The platforms enabling wireless sensor networks , 2004, CACM.

[2]  Gaetano Borriello,et al.  Extracting places from traces of locations , 2004, MOCO.

[3]  Mika Raento,et al.  Adaptive On-Device Location Recognition , 2004, Pervasive.

[4]  David A. Winter,et al.  Biomechanics and Motor Control of Human Movement , 1990 .

[5]  G. Goertzel.  An Algorithm for the Evaluation of Finite Trigonometric Series , 1958 .

[6]  Lin Liao,et al.  Inferring high-level behavior from , 2003 .

[7]  Bill N. Schilit,et al.  Place Lab: Device Positioning Using Radio Beacons in the Wild , 2005, Pervasive.

[8]  Chris Schmandt,et al.  A User-Centered Location Model , 2002, Personal and Ubiquitous Computing.

[9]  Ling Bao,et al.  Activity Recognition from User-Annotated Acceleration Data , 2004, Pervasive.

[10]  Hans Mulder,et al.  Smart sensors to network the world. , 2004, Scientific American.

[11]  Henry A. Kautz,et al.  Inferring High-Level Behavior from Low-Level Sensors , 2003, UbiComp.

[12]  Albrecht Schmidt,et al.  Advanced Interaction in Context , 1999, HUC.

[13]  Nigel Davies,et al.  UbiComp 2004: Ubiquitous Computing , 2004, Lecture Notes in Computer Science.

[14]  Andreas Krause,et al.  SenSay: a context-aware mobile phone , 2003, Seventh IEEE International Symposium on Wearable Computers, 2003. Proceedings..

[15]  Uwe Hansmann,et al.  Pervasive Computing , 2003 .

[16]  Chris Schmandt,et al.  WatchMe: Communication and Awareness Between Members of a Closely-Knit Group , 2004, UbiComp.

[17]  Thad Starner,et al.  Using GPS to learn significant locations and predict movement across multiple users , 2003, Personal and Ubiquitous Computing.

[18]  Anind K. Dey,et al.  UbiComp 2003: Ubiquitous Computing , 2003, Lecture Notes in Computer Science.

[19]  Gaetano Borriello,et al.  Some sensor network elements for ubiquitous computing , 2005, IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks, 2005..

[20]  Henry A. Kautz,et al.  Learning and inferring transportation routines , 2004, Artif. Intell..

[21]  Krzysztof Z. Gajos,et al.  Opportunity Knocks: A System to Provide Cognitive Assistance with Transportation Services , 2004, UbiComp.

[22]  George Buchanan,et al.  An Evaluation of WebTwig - A Site Outliner for Handheld Web Access , 1999, HUC.

[23]  Paul Lukowicz,et al.  SoundButton: design of a low power wearable audio classification system , 2003, Seventh IEEE International Symposium on Wearable Computers, 2003. Proceedings..