skip to main content
10.1145/1166253.1166269acmconferencesArticle/Chapter ViewAbstractPublication PagesuistConference Proceedingsconference-collections
Article

Sensing from the basement: a feasibility study of unobtrusive and low-cost home activity recognition

Published:15 October 2006Publication History

ABSTRACT

The home deployment of sensor-based systems offers many opportunities, particularly in the area of using sensor-based systems to support aging in place by monitoring an elder's activities of daily living. But existing approaches to home activity recognition are typically expensive, difficult to install, or intrude into the living space. This paper considers the feasibility of a new approach that "reaches into the home" via the existing infrastructure. Specifically, we deploy a small number of low-cost sensors at critical locations in a home's water distribution infrastructure. Based on water usage patterns, we can then infer activities in the home. To examine the feasibility of this approach, we deployed real sensors into a real home for six weeks. Among other findings, we show that a model built on microphone-based sensors that are placed away from systematic noise sources can identify 100% of clothes washer usage, 95% of dishwasher usage, 94% of showers, 88% of toilet flushes, 73% of bathroom sink activity lasting ten seconds or longer, and 81% of kitchen sink activity lasting ten seconds or longer. While there are clear limits to what activities can be detected when analyzing water usage, our new approach represents a sweet spot in the tradeoff between what information is collected at what cost.

Skip Supplemental Material Section

Supplemental Material

1166269.mp4

mp4

148.9 MB

References

  1. Abowd, G. and Mynatt, E.D. (2000) Charting Past, Present, and Future Research in Ubiquitous Computing. ACM Transactions on Computer-Human Interaction (TOCHI), 7(1). 29--58. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Beckmann, C., Consolvo, S. and LaMarca, A. (2004) Some Assembly Required: Supporting End-User Sensor Installation in Domestic Ubiquitous Computing Environments. Proceedings of the International Conference on Ubiquitous Computing (UbiComp 2004), 107--124.Google ScholarGoogle Scholar
  3. Begole, J. B., Tang, J. C. and Hill, R. (2003) Rhythm Modeling, Visualizations, and Applications. Proceedings of the ACM Symposium on User Interface Software and Technology (UIST 2003), 11--20. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Begole, J. B., Tang, J. C., Smith, R. B. and Yankelovich, N.(2002) Work Rhythms: Analyzing Visualizations of Awareness Histories of Distributed Groups. Proceedings of the ACM Conference on Computer Supported Cooperative Work (CSCW 2002), 334--343. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Chen, J., Kam, A. H., Zhang, J., Liu, N. and Shue, L. (2005) Bathroom Activity Monitoring Based on Sound. Proceedings of the International Conference on Pervasive Computing (Pervasive 2005), 47--61. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Consolvo, S., Roessler, P. and Shelton, B. E. (2004) The CareNet Display: Lessons Learned from an In Home Evaluation of an Ambient Display. Proceedings of the International Conference on Ubiquitous Computing (UbiComp 2004), 1--17.Google ScholarGoogle ScholarCross RefCross Ref
  7. Crossbow Technology. http://www.xbow.comGoogle ScholarGoogle Scholar
  8. Culler, D. E., Hill, J., Buonadonna, R. and Woo, A. (2001) A Network-Centric Approach to Embedded Software for Tiny Devices. Proceedings of the International Workshop on Embedded Software (EMSOFT 2001). Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Hirsch, T., Forlizzi, J., Hyder, E., Goetz, J., Kurtz, C. and Stroback, J. (2000) The ELDer Project: Social, Emotional, and Environmental Factors in the Design of Eldercare Technologies. Proceedings of the ACM Conference on Universal Usability, 72--79. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Munguia Tapia, E., Intille, S. S. and Larson, K. (2004) Activity Recognition in the Home Using Simple and Ubiquitous Sensors. Proceedings of the International Conference on Pervasive Computing (Pervasive 2004), 158--175.Google ScholarGoogle Scholar
  11. Mynatt, E. D., Rowan, J., Jacobs, A. and Craighill, S. (2001) Digital Family Portraits: Supporting Peace of Mind for Extended Family Members. Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI2001), 333--340. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Philipose, M., Fishkin, K. P., Perkowitz, M., Patterson, D. J., Fox, D., Kautz, H. and Hahnel, D. (2004) Inferring Activities from Interactions with Objects. IEEE Pervasive Computing, 3(4). 50--57. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Platt, J. C. Fast Training of Support Vector Machines using Sequential Minimal Optimization. In Schölkopf, B., Burges, C. and Smola, A. eds. Advances in Kernel Methods: Support Vector Learning, MIT Press, 1999, 185--208. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Rowan, J. and Mynatt, E. D. (2005) Digital Family Portrait Field Trial: Support for Aging in Place. Proceedings of the ACM Conference on Human Factors in Computing Systems(CHI 2005), 521--530. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Wilson, D. H. and Atkeson, C. G. (2005) Simultaneous Tracking and Activity Recognition (STAR) Using Many Anonymous, Binary Sensors. Proceedings of the International Conference on Pervasive Computing (Pervasive 2005), 62--79. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Witten, I. H. and Frank, E. (1999) Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Sensing from the basement: a feasibility study of unobtrusive and low-cost home activity recognition

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Conferences
      UIST '06: Proceedings of the 19th annual ACM symposium on User interface software and technology
      October 2006
      354 pages
      ISBN:1595933131
      DOI:10.1145/1166253

      Copyright © 2006 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 15 October 2006

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • Article

      Acceptance Rates

      Overall Acceptance Rate842of3,967submissions,21%

      Upcoming Conference

      UIST '24

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader