Nothing Special   »   [go: up one dir, main page]

Skip to main content

Recognizing Handheld Electrical Device Usage with Hand-Worn Coil of Wire

  • Conference paper
Pervasive Computing (Pervasive 2012)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7319))

Included in the following conference series:

Abstract

This paper describes the development of a new finger-ring shaped sensor device with a coil of wire for recognizing the use of handheld electrical devices such as digital cameras, cellphones, electric toothbrushes, and hair dryers by sensing time-varying magnetic fields emitted by the devices. Recently, sensing the usage of home electrical devices has emerged as a promising area for activity recognition studies because we can estimate high-level daily activities by recognizing the use of electrical devices that exist ubiquitously in our daily lives. A feature of our approach is that we can recognize the use of electrical devices that are not connected to the home infrastructure without the need to equip them with sensors. We evaluated the performance of our approach by using sensor data obtained from real houses. We also investigated the portability of training data between different users.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Mynatt, E., Rowan, J., Craighill, S., Jacobs, A.: Digital family portraits: Supporting peace of mind for extended family members. In: CHI 2001, pp. 333–340 (2001)

    Google Scholar 

  2. Maekawa, T., Yanagisawa, Y., Kishino, Y., Kamei, K., Sakurai, Y., Okadome, T.: Object-blog system for environment-generated content. IEEE Pervasive Computing 7(4), 20–27 (2008)

    Article  Google Scholar 

  3. van Kasteren, T., Noulas, A., Englebienne, G., Kröse, B.: Accurate activity recognition in a home setting. In: Ubicomp 2008, pp. 1–9 (2008)

    Google Scholar 

  4. Philipose, M., Fishkin, K., Perkowitz, M.: Inferring activities from interactions with objects. IEEE Pervasive Computing 3(4), 50–57 (2004)

    Article  Google Scholar 

  5. Tapia, E.M., 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)

    Chapter  Google Scholar 

  6. Bao, L., Intille, S.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)

    Chapter  Google Scholar 

  7. Lester, J., Choudhury, T., Borriello, G.: A Practical Approach to Recognizing Physical Activities. In: Fishkin, K.P., Schiele, B., Nixon, P., Quigley, A. (eds.) PERVASIVE 2006. LNCS, vol. 3968, pp. 1–16. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  8. Maekawa, T., Yanagisawa, Y., Kishino, Y., Ishiguro, K., Kamei, K., Sakurai, Y., Okadome, T.: Object-Based Activity Recognition with Heterogeneous Sensors on Wrist. In: Floréen, P., Krüger, A., Spasojevic, M. (eds.) Pervasive 2010. LNCS, vol. 6030, pp. 246–264. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  9. Maekawa, T., Watanabe, S.: Unsupervised activity recognition with user’s physical characteristics data. In: Int’l Symp. on Wearable Computers, pp. 89–96 (2011)

    Google Scholar 

  10. Kim, Y., Schmid, T., Charbiwala, Z., Srivastava, M.: ViridiScope: design and implementation of a fine grained power monitoring system for homes. In: Ubicomp 2009, pp. 245–254 (2009)

    Google Scholar 

  11. Hart, G.: Nonintrusive appliance load monitoring. Proceedings of the IEEE 80(12), 1870–1891 (1992)

    Article  Google Scholar 

  12. Patel, S.N., Robertson, T., Kientz, J.A., Reynolds, M.S., Abowd, G.D.: At the Flick of a Switch: Detecting and Classifying Unique Electrical Events on the Residential Power Line (Nominated for the Best Paper Award). In: Krumm, J., Abowd, G.D., Seneviratne, A., Strang, T. (eds.) UbiComp 2007. LNCS, vol. 4717, pp. 271–288. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  13. Gupta, S., Reynolds, M., Patel, S.: ElectriSense: Single-point sensing using EMI for electrical event detection and classification in the home. In: Ubicomp 2010, pp. 139–148 (2010)

    Google Scholar 

  14. Maekawa, T., Kishino, Y., Sakurai, Y., Suyama, T.: Recognizing the Use of Portable Electrical Devices with Hand-Worn Magnetic Sensors. In: Lyons, K., Hightower, J., Huang, E.M. (eds.) Pervasive 2011. LNCS, vol. 6696, pp. 276–293. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  15. Lenz, J.: A review of magnetic sensors. Proceedings of the IEEE 78(6), 973–989 (1990)

    Article  Google Scholar 

  16. Lifton, J., Feldmeier, M., Ono, Y., Lewis, C., Paradiso, J.: A platform for ubiquitous sensor deployment in occupational and domestic environments. In: IPSN 2007, pp. 119–127 (2007)

    Google Scholar 

  17. Jiang, X., Dawson-Haggerty, S., Dutta, P., Culler, D.: Design and implementation of a high-fidelity ac metering network. In: IPSN 2009, pp. 253–264 (2009)

    Google Scholar 

  18. Cohn, G., Morris, D., Patel, S.N., Tan, D.S.: Your noise is my command: sensing gestures using the body as an antenna. In: CHI 2011, pp. 791–800 (2011)

    Google Scholar 

  19. Rabiner, L.: A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE 77(2), 257–286 (1989)

    Article  Google Scholar 

  20. Leggetter, C., Woodland, P.: Maximum likelihood linear regression for speaker adaptation of continuous density hidden Markov models. Computer Speech & Language 9(2), 171–185 (1995)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Maekawa, T., Kishino, Y., Yanagisawa, Y., Sakurai, Y. (2012). Recognizing Handheld Electrical Device Usage with Hand-Worn Coil of Wire. In: Kay, J., Lukowicz, P., Tokuda, H., Olivier, P., Krüger, A. (eds) Pervasive Computing. Pervasive 2012. Lecture Notes in Computer Science, vol 7319. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31205-2_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-31205-2_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31204-5

  • Online ISBN: 978-3-642-31205-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics