Skip to main content

Towards Non-intrusive Sleep Pattern Recognition in Elder Assistive Environment

  • Conference paper
Ubiquitous Intelligence and Computing (UIC 2010)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 6406))

Included in the following conference series:

  • 2133 Accesses

Abstract

Quality of sleep is an important attribute of an elder’s health state and its assessment is still a challenge. The sleep pattern is a significant aspect to evaluate the quality of sleep, and how to recognize elder’s sleep pattern is an important issue for elder-care community. With the pressure sensor matrix to monitor the elder’s sleep behavior in bed, this paper presents an unobtrusive sleep postures detection and pattern recognition approaches. Based on the proposed sleep monitoring system, the processing methods of experimental data and the classification algorithms for sleep pattern recognition are also discussed.

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

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. Foley, D.J., Monjan, A.A., Brown, S.L., et al.: Sleep complaints among elderly persons: an epidemiologic study of three communities. Sleep 18(6), 425–432 (1995)

    Article  Google Scholar 

  2. Foley, D., Ancoli-Israel, S., Britz, P., Walsh, J.: Sleep disturbances and chronic disease in older adults: results of the 2003 National Sleep Foundation Sleep in America Survey. J. Psychosom. Res. 56, 497–502 (2004)

    Article  Google Scholar 

  3. Aaronson, S.T., Rashed, S., Biber, M.P., Hobson, J.A.: Brain state and body posture: a time-lapse video study of sleep. Arch. Gen. Psychiatry 39, 330–335 (1982)

    Article  Google Scholar 

  4. Muzet, A.: Dynamics of Body Movements in Normal Sleep. Presented at Eighth European Congress on Sleep Research, Szeged, Hungary (1986)

    Google Scholar 

  5. Aaronson, S.T., Rashed, S., Biber, M.P., Hobson, A.: Brain State and Body Posture. Archives of General Psychiatry 39, 330–335 (1982)

    Article  Google Scholar 

  6. Wilde-Frenz, J., Schulz, H.: Rate and Distribution of Body Movements during Sleep in Humans. Perceptual and Motor Skills 56, 275–283 (1983)

    Article  Google Scholar 

  7. Cheng, C.M., Hsu, Y.L., Young, C.M., Wu, C.H.: Development of a portable device for tele-monitoring of snoring and OSAS symptoms. In: Telemed e-Health 2008, vol. 14, pp. 55–68 (2008)

    Google Scholar 

  8. Wilde-Frenz, J., Schulz, H.: Rate and Distribution of Body Movements during Sleep in Humans. Perceptual and Motor Skills 56, 275–283 (1983)

    Article  Google Scholar 

  9. Tuisku, K., Holi, M.M., Wahlbeck, K., Ahlgren, A.J., Lauerma, H.: Quantitative Rest Activity in Ambulatory Monitoring as a Physiological Marker of Restless Legs Syndrome: A Controlled Study. Movement Disorders 18, 442–448 (2002)

    Article  Google Scholar 

  10. Shneerson, J.M., Smith, I.E.: The Validation of a New Actigraphy System for the Measurement of Period Leg Movements in Sleep. Sleep Medicine 6, 507–513 (2005)

    Article  Google Scholar 

  11. Tryon, W.W.: Issues of Validity in Actigraphic Sleep Assessment. Sleep 27, 158–165 (2004)

    Article  Google Scholar 

  12. Prill, T., Fahrenberg, J.: Simultaneous Assessment of Posture and Limb Movements (e.g., periodic leg movements) with Calibrated Multiple Accelerometry. Physiological Measurement, 47–53 (2007)

    Google Scholar 

  13. Occhiuzzi, C., Marrocco, G.: RFID Technology for the Neuroscience: Feasibility of Sleep Disease Monitoring. In: Proceedings of EuCAP 2009, Berlin (2009)

    Google Scholar 

  14. Tamura, T., Zhou, J., Mizukami, H., Togawa, T.: A System for Monitoring Temperature Distribution in Bed and Its Application to the Assessment of Body Movement. Physiological Measurements 14, 33–41 (1993)

    Article  Google Scholar 

  15. Kaartinen, J., Kuhlman, I., Peura, P.: Long-term Monitoring of Movements in Bed and Their Relation to Subjective Sleep Quality. Sleep and Hypnosis 5(3), 145–153 (2003)

    Google Scholar 

  16. Rauhala, E., Erkinjuntti, M., Polo, O.: Detection of Periodic Leg Movements with a Static-Charge-Sensitive Bed. Journal of Sleep Research 5, 246–250 (1996)

    Article  Google Scholar 

  17. Van der Loos, H.F.M., Ullrich, N., Kobayashi, H.E.R.: Development of sensate and robotic bed technologies for vital signs monitoring and sleep quality improvement. Autonomous Robots 15(1), 67–79 (2003)

    Article  Google Scholar 

  18. Tamura, T., Nishigaichi, A., Nomura, T.: Monitoring of body movement during sleep in bed. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1483–1484 (1992)

    Google Scholar 

  19. Kimura, H., Kobayashi, H., Kawabata, K., Van der Loos, H.F.: Development of an unobtrusive vital signs detection system using conductive fiber sensors. In: Proceedings 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 307–312 (2004)

    Google Scholar 

  20. Adami, A.M., Hayes, T.L., Pavel, M., Singer, C.M.: Detection and classification of movements in bed using load cells. In: IEEE 27th Annual International Conference of the Engineering in Medicine and Biology Society, pp. 589–592 (2005)

    Google Scholar 

  21. The website of Tekscan Int., http://www.tekscan.com/flexiforce/flexiforce.html

  22. Simonceli, L., Bittar, R.S.M., Greters, M.E.: Posture restrictions do not interfere in the results of canalith repostureing maneuver. Braz. J. Otorhinolaryngol. 71, 55–59 (2005)

    Article  Google Scholar 

  23. http://maisonbisson.com/blog/post/10182/claim-sleep-position-personality/

  24. John, G.H., Langley, P.: Estimating Continuous Distributions in Bayesian Classifiers. In: Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, San Mateo, pp. 338–345 (1995)

    Google Scholar 

  25. Breiman, L.: Random Forests. Machine Learning 45(1), 5–32 (2001)

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Ni, H. et al. (2010). Towards Non-intrusive Sleep Pattern Recognition in Elder Assistive Environment. In: Yu, Z., Liscano, R., Chen, G., Zhang, D., Zhou, X. (eds) Ubiquitous Intelligence and Computing. UIC 2010. Lecture Notes in Computer Science, vol 6406. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16355-5_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-16355-5_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-16354-8

  • Online ISBN: 978-3-642-16355-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics