Portable Wireless Sensors for Object Usage Sensing in the Home: Challenges and Practicalities

  • Emmanuel Munguia Tapia
  • Stephen S. Intille
  • Kent Larson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4794)


A low-cost kit of stick-on wireless sensors that transmit data indicating whenever various objects are being touched or used might aid ubiquitous computing research efforts on rapid prototyping, context-aware computing,and ultra-dense object sensing, among others. Ideally, the sensors would besmall, easy-to-install, and affordable. The sensors would reliably recognize when specific objects are manipulated, despite vibrations produced by the usage of nearby objects and environmental noise. Finally, the sensors would operate continuously for several months, or longer. In this paper, we discuss the challenges and practical aspects associated with creating such "object usage" sensors. We describe the existing technologies used to recognize object usage and then present the design and evaluation of a new stick-on, wireless object usage sensor. The device uses (1) a simple classification rule tuned to differentiate real object usage from adjacent vibrations and noise in real-time based on data collected from a real home, and (2) two complimentary sensors to obtain good battery performance. Results of testing 168 of the sensors in an instrumented home for one month of normal usage are reported as well as results from a 4-hour session of a person busily cooking and cleaning in the home, where every object usage interaction was annotated and analyzed.


Sensor Node Ubiquitous Computing Battery Life Object Usage Piezoelectric Film 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Munguia Tapia, E., Intille, S.S., Larson, K.: Activity Recognition in the Home Setting Using Simple and Ubiquitous Sensors. In: Ferscha, A., Mattern, F. (eds.) PERVASIVE 2004. LNCS, vol. 3001, pp. 158–175. Springer, Heidelberg (2004)Google Scholar
  2. 2.
    Wilson, D.: Simultaneous Tracking and Activity Recognition (STAR) Using Many Anonymous, Binary Sensors. In: Gellersen, H.-W., Want, R., Schmidt, A. (eds.) PERVASIVE 2005. LNCS, vol. 3468, pp. 62–83. Springer, Heidelberg (2005)Google Scholar
  3. 3.
    Barger, T., Brown, D., Alwan, M.: Health Status Monitoring Through Analysis of Behavioral Patterns. Proceedings of IEEE Transactions on Systems, Man and Cybernetics Part A 35, 22–27 (2005)CrossRefGoogle Scholar
  4. 4.
    Philipose, M., Fishkin, K.P., Perkowitz, M., Patterson, D.J., Hahnel, D., Fox, D., Kautz, H.: Inferring Activities from Interactions with Objects. IEEE Pervasive Computing Magazine 3, 50–57 (2004)CrossRefGoogle Scholar
  5. 5.
    Beaudin, J.S.: From Personal Experience to Design: Externalizing the Homeowner’s Needs and Assessment Process, M.S. Thesis, The Media Laboratory. MIT, Cambridge, MA (2003)Google Scholar
  6. 6.
    Philipose, M., Fishkin, K., Fox, D., Kautz, H., Patterson, D., Perkowitz, M.: Guide: Towards Understanding Daily Life Via Auto-Identification and Statistical Analysis. In: UbiHealth 2003 (2003)Google Scholar
  7. 7.
    Fishkin, K., Philipose, M., Rea, A.: Hands-On RFID: Wireless Wearables for Detecting Use of Objects. In: ISWC 2005, pp. 38–41. IEEE Press, Los Alamitos (2005)Google Scholar
  8. 8.
    Munguia Tapia, E., Intille, S.S.: Environmental Sensors Hardware and Software Resources, [cited August 28th, 2007], available from
  9. 9.
    Feldmeier, M., Paradiso, J.A.: Giveaway Wireless Sensors for Large-Group Interaction. In: CHI 2004. Proceedings of The ACM Conference on Human Factors and Computing Systems, pp. 1291–1292. ACM Press, New York (2004)CrossRefGoogle Scholar
  10. 10.
    Munguia Tapia, E., Intille, S.S., Lopez, L., Larson, K.: The Design of a Portable Kit of Wireless Sensors for Naturalistic Data Collection. In: Fishkin, K.P., Schiele, B., Nixon, P., Quigley, A. (eds.) PERVASIVE 2006. LNCS, vol. 3968, pp. 117–134. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  11. 11.
    Philipose, M.: Large-Scale Human Activity Recognition Using Ultra-Dense Sensing. The Bridge, National Academy of Engineering 35(4) (2005)Google Scholar
  12. 12.
    Philipose, M., Smith, J.R., Jiang, B., Mamishev, A., Roy, S., Sundara-Rajan, K.: Battery-Free Wireless Identification and Sensing. IEEE Pervasive Computing 4, 37–45 (2005)CrossRefGoogle Scholar
  13. 13.
    Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)zbMATHGoogle Scholar
  14. 14.
    Intille, S.S., Larson, K., Munguia Tapia, E., Beaudin, J.S., Kaushik, P., Nawyn, J., Rockinson, R.: Using a Live-In Laboratory for Ubiquitous Computing Research. In: Fishkin, K.P., Schiele, B., Nixon, P., Quigley, A. (eds.) PERVASIVE 2006. LNCS, vol. 3968, pp. 349–365. Springer, Heidelberg (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Emmanuel Munguia Tapia
    • 1
  • Stephen S. Intille
    • 1
  • Kent Larson
    • 1
  1. 1.House_n, Massachusetts Institute of Technology, 1 Cambridge Center, 4FL, Cambridge, MA, 02142USA

Personalised recommendations