Advertisement

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)

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.

Keywords

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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Patterson, D., Liao, L., Fox, D., Kautz, H.: Inferring high-level behavior from low-level sensors. In: Dey, A.K., Schmidt, A., McCarthy, J.F. (eds.) UbiComp 2003. LNCS, vol. 2864, pp. 73–89. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  2. 2.
    Ashbrook, D., Starner, T.: Using GPS to Learn Significant Locations and Predict Movement Across Multiple Users. Personal and Ubiquitous Computing 7(5), 275–286 (2003)CrossRefGoogle Scholar
  3. 3.
    Marmasse, N., Schmandt, C.: A User-Centered Location Model. Personal and Ubiquitous Computing, 318–321 (2002)Google Scholar
  4. 4.
    Marmasse, N., Schmandt, C., Spectre, D.: WatchMe: Communication and awareness between members of a closely-knit group. In: Davies, N., Mynatt, E.D., Siio, I. (eds.) UbiComp 2004. LNCS, vol. 3205, pp. 214–231. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  5. 5.
    Schmidt, A., Aidoo, K.A., Takaluoma, A., Tuomela, U., Van Laerhoven, K., Van de Velde, W.: Advanced interaction in context. In: Gellersen, H.-W. (ed.) HUC 1999. LNCS, vol. 1707, pp. 89–101. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  6. 6.
    Bao, L., Intille, S.: Activity recognition from user-annotated acceleration data. In: Ferscha, A., Mattern, F. (eds.) PERVASIVE 2004. LNCS, vol. 3001, pp. 1–17. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  7. 7.
    Patterson, D., Liao, L., Gajos, K., Collier, M., Livic, N., Olson, K., Wang, S., Fox, D., Kautz, H.: Opportunity knocks: A system to provide cognitive assistance with transportation services. In: Davies, N., Mynatt, E.D., Siio, I. (eds.) UbiComp 2004. LNCS, vol. 3205, pp. 433–450. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  8. 8.
    Liao, L., Fox, D., Kautz, H.: Learning and Inferring Transportation Routines. In: Proc. of the National Conference on Artificial Intelligence (2004)Google Scholar
  9. 9.
    Stäger, M., Lukowicz, P., Perera, N., Büren, T., Tröster, G., Starner, T.: SoundButton: Design of a Low Power Wearable Audio Classification System. In: Seventh IEEE International Symposium on Wearable Computers, pp. 12–17 (2003)Google Scholar
  10. 10.
    Kang, J., Welbourne, W., Stewart, B., Borriello, G.: Extracting places from traces of locations. In: Proceedings of the 2nd ACM international workshop on Wireless mobile applications and services on WLAN hotspots, pp. 110–118 (2004)Google Scholar
  11. 11.
    Hill, J., et al.: The platforms enabling wireless sensor networks. Communications of the ACM 47(6), 41–46 (2004)CrossRefGoogle Scholar
  12. 12.
    Culler, D., Mulder, H.: Smart Sensors to Network the World. Scientific American, 84–91 (2004)Google Scholar
  13. 13.
    Winter, D.: Biomechanics and Motor Control of Human Movement, 2nd edn. Wiley, New York (1990)Google Scholar
  14. 14.
    Goertzel, G.: An Algorithm for the Evaluation of Finite Trigonometric Series. Amer. Math. Month. 65, 34–35 (1958)zbMATHCrossRefMathSciNetGoogle Scholar
  15. 15.
    Bahl, P., Padmanabhan, V.N.: RADAR: An RF-Based In-Building User Location and Tracking System. In: Proc. IEEE Infocom (March 2000)Google Scholar
  16. 16.
    LaMarca, A., Chawathe, Y., Consolvo, S., Hightower, J., Smith, I., Scott, J., Sohn, T., Howard, J., Hughes, J., Potter, F., Tabert, J., Powledge, P., Borriello, G., Schilit, B.: Place Lab: Device Positioning Using Radio Beacons in the Wild., Intel Research Technical Report: IRS-TR-04-016Google Scholar
  17. 17.
    Laasonen, K., Raento, M., Toivonen, H.: Adaptive On-Device Location Recognition. In: Proceedings of the 2nd International Conference on Pervasive Computting (April 2004)Google Scholar
  18. 18.
    LaMarca, A., et al.: Place lab: Device positioning using radio beacons in the wild. In: Gellersen, H.-W., Want, R., Schmidt, A. (eds.) PERVASIVE 2005. LNCS, vol. 3468, pp. 116–133. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  19. 19.
    Siewiorek, D., et al.: SenSay: A Context-Aware Mobile Phone. In: IEEE International Symposium on Wearable Computers (ISWC 2003), New York (2003)Google Scholar
  20. 20.
    Brunette, W., et al.: Some Sensor Network Elements for Ubiquitous Computing. In: The Fourth International Conference on Information Processing in Sensor Networks (IPSN 2005), Los Angeles, CA (2005) (to appear)Google Scholar

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

Personalised recommendations