A Longitudinal Study of Pressure Sensing to Infer Real-World Water Usage Events in the Home

  • Jon Froehlich
  • Eric Larson
  • Elliot Saba
  • Tim Campbell
  • Les Atlas
  • James Fogarty
  • Shwetak Patel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6696)


We present the first longitudinal study of pressure sensing to infer real-world water usage events in the home (e.g., dishwasher, upstairs bathroom sink, downstairs toilet). In order to study the pressure-based approach out in the wild, we deployed a ground truth sensor network for five weeks in three homes and two apartments that directly monitored valve-level water usage by fixtures and appliances. We use this data to, first, demonstrate the practical challenges in constructing water usage activity inference algorithms and, second, to inform the design of a new probabilistic-based classification approach. Inspired by algorithms in speech recognition, our novel Bayesian approach incorporates template matching, a language model, grammar, and prior probabilities. We show that with a single pressure sensor, our probabilistic algorithm can classify real-world water usage at the fixture level with 90% accuracy and at the fixturecategory level with 96% accuracy. With two pressure sensors, these accuracies increase to 94% and 98%. Finally, we show how our new approach can be trained with fewer examples than a strict template-matching approach alone.


Water sensing activity inference sustainability field deployments 


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  1. 1.
    Bowman, A.W., Azzalini, A.: Applied Smoothing Techniques for Data Analysis. Oxford University Press, New York (1997)MATHGoogle Scholar
  2. 2.
    Chow, Y., Schwartz, R.: The N-Best algorithm: an efficient procedure for finding top N sentence hypotheses. In: Proc. of the Workshop on Speech and Natural Language, Cape Cod, Massachusetts, October 15-18. Association for Computational Linguistics, Morristown (1989)Google Scholar
  3. 3.
    DeOreo, W.B., Heaney, J.P., Mayer, P.W.: Flow Trace Analysis to Assess Water Use. Journal of the American Water Works Association 88(1) (January 1996)Google Scholar
  4. 4.
    DeOreo, W.B., Mayer, P.W.: The End Uses of Hot Water in Single Family Homes from Flow Trace Analysis. Aquacraft, Inc., (2002)Google Scholar
  5. 5.
    Dziegielewski, B., Opitz, E., Kiefer, J., Baumann, D.: Evaluation of Urban Water Conservation Programs: A Procedures Manual. Prepared for California Urban Water Agencies by Planning and Management Consultants, Ltd., Carbondale, Illinois (February 1992)Google Scholar
  6. 6.
    Fogarty, J., Au, C., Hudson, S.E.: Sensing from the Basement: A Feasibility Study of Unobtrusive and Low-Cost Home Activity Recognition. In: Proc. of UIST 2006, pp. 91–100 (2006) Google Scholar
  7. 7.
    Froehlich, J., Findlater, L., Landay, J.: The Design of Eco-Feedback Technology. In: Proceedings of CHI 2010, Atlanta, GA, pp. 1999–2008 (2010)Google Scholar
  8. 8.
    Froehlich, J.E., Larson, E., et al.: HydroSense: infrastructure-mediated single-point sensing of whole-home water activity. In: Proc. of UbiComp 2009, Orlando, Florida, USA, pp. 235–244 (2009)Google Scholar
  9. 9.
    Kim, Y., Schmid, T., Charbiwala, Z.M., Friedman, J., Srivastava, M.B.: NAWMS: Non-Intrusive Autonomous Water Monitoring System. In: Proceedings of SenSys 2008, pp. 309–322 (2008)Google Scholar
  10. 10.
    Larson, E., et al.: Disaggregated water sensing from a single, pressure-based sensor: An extended analysis of HydroSense using staged experiments. Pervasive and Mobile Computing (in press) Google Scholar
  11. 11.
    Mayer, P.W., DeOreo, W.B., Kiefer, J., Opitz, E., Dziegieliewski, B., Nelson, J.O.: Residential End Uses of Water. American Water Works Association, Denver (1999)Google Scholar
  12. 12.
    Mayer, P.W., et al.: Great Expectations—Actual Water Savings with the Latest High-Efficiency Residential Fixtures and Appliances. In: Proc. of the Water Sources Conference, Las Vegas, NV (2002)Google Scholar
  13. 13.
    Mayer, P., DeOreo, W. B., Towler, E., Lewis, D. M.: Residential Indoor Water Conservation Study: Evaluation of High Efficiency Indoor Plumbing Fixture Retrofits in Single-Family Homes in the East Bay Municipal Utility District Service Area, Prepared for EBMUD and the US EPA (July 2003) Google Scholar
  14. 14.
    Mead, N., Aravinthan, V.: Investigation of Household Water Consumption Using A Smart Metering System. Desalination and Water Treatment 11, 1–9 (2009)CrossRefGoogle Scholar
  15. 15.
    Navigant Consulting. Water & Heating Working Group Meeting. Residential & Multifamily: Background, Outcomes & Next Steps. ACEEE Hot Water Forum, Downey, CA (March 10, 2010)Google Scholar
  16. 16.
    North, D.O.: An analysis of the factors which determine signal/noise discrimination in pulsed carrier systems. RCA Labs, Princeton (1943)Google Scholar
  17. 17.
    US Department of Energy. US Household Electricity Report, Energy Information Administration, US DoE (2001), http://www.eia.doe.gov/emeu/reps/enduse/er01_us_tab1.html (last accessed October 10, 2010)
  18. 18.
    Chen, S.F., Rosenfeld, R.: A Survey of Smoothing Techniques for Maximum Entropy Models. IEEE Transactions on Speech and Audio Processing 8(1), 37–50 (2000)CrossRefGoogle Scholar
  19. 19.
    Tapia, E., Intille, S.S., Larson, K.: Activity Recognition in the Home Using Simple and Ubiquitous Sensors. In: Ferscha, A., Mattern, F. (eds.) PERVASIVE 2004. LNCS, vol. 3001, pp. 158–175. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  20. 20.
    Wilkes, C., Mason, A., Niang, L., Jensen, K., Hern, S.: Evaluation of the Meter-Master Data Logger and the Trace Wizard Analysis Software. Special Appendix to Quantification of Exposure-Related Water Uses for Various U.S. Subpopulations. Prepared for US EPA (December 2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jon Froehlich
    • 1
  • Eric Larson
    • 2
  • Elliot Saba
    • 2
  • Tim Campbell
    • 3
  • Les Atlas
    • 2
  • James Fogarty
    • 1
  • Shwetak Patel
    • 1
    • 2
  1. 1.Computer Science and EngineeringUniversity of WashingtonSeattleUSA
  2. 2.Electrical EngineeringUniversity of WashingtonSeattleUSA
  3. 3.Mechnical EngineeringUniversity of WashingtonSeattleUSA

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