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Sleep Quality Monitoring with the Smart Bed

  • Daniel Waltisberg
  • Bert Arnrich
  • Gerhard Tröster
Chapter
Part of the Human–Computer Interaction Series book series (HCIS)

Abstract

Long-term sleep monitoring of patients is interesting for the diagnosis of sleep disorders and for the continuous monitoring of the health state. However, traditional polysomnography is not suited for long-term monitoring due to various reasons and new intelligent solutions are required for the continuous and unobtrusive monitoring of sleep parameters. In this chapter we present the Mobility Monitor, a portable sleep monitoring system which has been developed specifically for elderly care facilities. The system informs the nursing staff about the patient’s movement patterns during the night. This information can be used for the assessment of the risk of pressure ulcer, to monitor bed exits or to observe the influence of medication on sleep behavior. With application examples of a nursing home and the results of a recent observational study, we will demonstrate the use of the mobility analysis and show how the additional insights can improve the care quality.

Keywords

Nursing Home Sleep Quality Movement Pattern Nursing Staff Pressure Ulcer 
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.

References

  1. 1.
    Adami, A. M., Adami, A. G., Hayes, T. L., Pavel, M., & Beattie, Z. T. (2012). A Gaussian model for movement detection during sleep. In Annual International Conference of the IEEE Engineering in Medicine and Biology Society, (vol. 2012, pp. 2263–6). Jan 2012.Google Scholar
  2. 2.
    Adami, A. M., Pavel, M., Hayes, T. L., & Singer, C. M. (2010). Detection of movement in bed using unobtrusive load cell sensors. IEEE Transactions on Information Technology in Biomedicine, 14(2), 481–90.CrossRefGoogle Scholar
  3. 3.
    Alametsä, J., Viik, J., Alakare, J., Värri, A., & Palomäki, A. (2008). Ballistocardiography in sitting and horizontal positions. Physiological Measurement, 29(9), 1071–87.CrossRefGoogle Scholar
  4. 4.
    Alihanka, J., & Vaahtoranta, K. (1979). A static charge sensitive bed. A new method for recording body movements during sleep. Electroencephalography and Clinical Neurophysiology, 46, 731–4.CrossRefGoogle Scholar
  5. 5.
    American Academy of Sleep Medicine. (2005). International classification of sleep disorders (ICSD-2) (2nd ed.). Westchester: American Academy of Sleep Medicine.Google Scholar
  6. 6.
    Ancoli-Israel, S. (1997). Sleep problems in older adults: Putting myths to bed. Geriatrics, 52(1), 20–0.Google Scholar
  7. 7.
    Beattie, Z. T., Hagen, C. C., & Hayes, T. L. (2011) Classification of lying position using load cells under the bed. In Annual International Conference of the IEEE Engineering in Medicine and Biology Society, (vol. 2011, pp. 474–7). Jan 2011.Google Scholar
  8. 8.
    Beattie, Z. T., Hagen, C. C., Pavel, M., & Hayes, T. L. (2009). Classification of breathing events using load cells under the bed. In Annual International Conference of the IEEE Engineering in Medicine and Biology Society, (vol. 2009, pp. 3921–4). Jan 2009.Google Scholar
  9. 9.
    Belenky, G., Wesensten, N. J., Thorne, D. R., Thomas, M. L., Sing, H. C., Redmond, D. P., et al. (2003). Patterns of performance degradation and restoration during sleep restriction and subsequent recovery: A sleep dose-response study. Journal of sleep research, 12(1), 1–12.CrossRefGoogle Scholar
  10. 10.
    Bergstrom, N., Braden, B. J., Laguzza, A., & Holman, V. (1987). The braden scale for predicting pressure sore risk. Nursing Research, 36(4), 205–10.CrossRefGoogle Scholar
  11. 11.
    Bluestein, D., & Javaheri, A. (2008). Pressure ulcers: Prevention, evaluation, and management. American Family Physician, 78(10), 1186–94.Google Scholar
  12. 12.
    Brink, M., Müller, C. H., & Schierz, C. (2006). Contact-free measurement of heart rate, respiration rate, and body movements during sleep. Behavior Research Methods, 38(3), 511–1.CrossRefGoogle Scholar
  13. 13.
    Brink, M., Lercher, P., Eisenmann, A., & Schierz, C. H. (2008). Influence of slope of rise and event order of aircraft noise events on high resolution actimetry parameters. Somnologie-Schlafforschung und Schlafmedizin., 12(2), 118–8.CrossRefGoogle Scholar
  14. 14.
    Brüser, C., Stadlthanner, K., de Waele, S., & Leonhardt, S. (2011). Adaptive beat-to-beat heart rate estimation in ballistocardiograms. IEEE Transactions on Information Technology in Biomedicine, 15(5), 778–86.CrossRefGoogle Scholar
  15. 15.
    Chen, W., Zhu, X., Nemoto, T., Kitamura, K.-I., Sugitani, K., & Wei, D. (2008). Unconstrained monitoring of long-term heart and breath rates during sleep. Physiological Measurement, 29(2), N1–10.CrossRefGoogle Scholar
  16. 16.
    Collop, N. A., Mc, W., Anderson, B., Boehlecke, D., Claman, R., Goldberg, D. J., et al. (2007). Clinical guidelines for the use of unattended portable monitors in the diagnosis of obstructive sleep apnea in adult patients. Journal of Clinical Sleep Medicine, 3(7), 737–7.Google Scholar
  17. 17.
    David, C., Mack, D., Patrie, J. P., Suratt, P. M., Felder, R. A., & Alwan, M. A. (2009). Development and preliminary validation of heart rate and breathing rate detection using a passive, ballistocardiography-based sleep monitoring system. IEEE Transactions on Information Technology in Biomedicine, 13(1), 111–20.CrossRefGoogle Scholar
  18. 18.
    Van Dongen, H. P., Maislin, G., Mullington, J. M., & Dinges, D. F. (2003). The cumulative cost of additional wakefulness: Dose-response effects on neurobehavioral functions and sleep physiology from chronic sleep restriction and total sleep deprivation. Sleep, 26(2), 117–26.Google Scholar
  19. 19.
    Eyers, I., Young, E., Luff, R., & Arber, S. (2012). Striking the balance: Night care versus the facilitation of good sleep. British Journal of Nursing, 21(5), 303–7.Google Scholar
  20. 20.
    Giovangrandi, L., Inan, O. T., Wiard, R. M., Etemadi, M., & Kovacs, G. T. (2011). Ballistocardiography: A method worth revisiting. Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011, 4279–82.Google Scholar
  21. 21.
    Iber, C., Ancoli-Israel, S., Chesson, A., & Quan, S. (2007). The AASM manual for the scoring of sleep and associated events: Rules, terminology and technical specifications. Darien: American Academy of Sleep Medicine.Google Scholar
  22. 22.
    Joon Chee, J., Jeong, D.-U., & Suk Park, K. (2010). Nonconstrained sleep monitoring system and algorithms using air-mattress with balancing tube method. IEEE Transactions on Information Technology in Biomedicine, 14(1), 147–56.CrossRefGoogle Scholar
  23. 23.
    Kärki, S. (2009) Film-type sensor materials in measurement of physiological force and pressure variables. Ph.D thesis, Tampere University of Technology, Finland.Google Scholar
  24. 24.
    De Koninck, J., Lorrain, D., & Gagnon, P. (1992). Sleep positions and position shifts in five age groups: An ontogenetic picture. Sleep, 15(2), 143–9.Google Scholar
  25. 25.
    Kortelainen, J. M., Mendez, M. O., Bianchi, A. M., Matteucci, M., & Cerutti, S. (2010). Sleep staging based on signals acquired through bed sensor. IEEE Transactions on Information Technology in Biomedicine, 14(3), 776–85.CrossRefGoogle Scholar
  26. 26.
    Kurihara, Y., Watanabe, K., & Tanaka, H. (2010). Sleep-states-transition model by body movement and estimation of sleep-stage-appearance probabilities by Kalman filter. IEEE Transactions on Information Technology in Biomedicine, 14(6), 1428–35.CrossRefGoogle Scholar
  27. 27.
    Kurihara, Y., & Watanabe, K. (2012). Sleep-stage decision algorithm by using heartbeat and body-movement signals. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 42(6), 1450–9.CrossRefGoogle Scholar
  28. 28.
    Kushida, C. A., Littner, M. R., Morgenthaler, T., Alessi, C. A., Bailey, D., Coleman, J., et al. (2005). Practice parameters for the indications for polysomnography and related procedures: An update for 2005. Sleep, 28(4), 499–521.Google Scholar
  29. 29.
    Martin, A., Julia, V. R., Khatami, R., & Landolt, H. P. (2006). Age-related changes in the time course of vigilant attention during 40 hours without sleep in men. Sleep, 29(1), 55–7.Google Scholar
  30. 30.
    Morgenthaler, T., Alessi, C., Friedman, L., Owens, J., Kapur, V., Boehlecke, B., et al. (2007). Practice parameters for the use of actigraphy in the assessment of sleep and sleep disorders: An update for 2007. Sleep, 30(4), 519–9.Google Scholar
  31. 31.
    Niizeki, K., Nishidate, I., Uchida, K., & Kuwahara, M. (2005). Unconstrained cardiorespiratory and body movement monitoring system for home care. Medical and Biological Engineering and Computing, 43(6), 716–24.CrossRefGoogle Scholar
  32. 32.
    Nukaya, S., Shino, T., Kurihara, Y., Watanabe, K., & Tanaka, H. (2012). Noninvasive bed sensing of human biosignals via piezoceramic devices sandwiched between the floor and bed. IEEE Sensors Journal, 12(3), 431–8.CrossRefGoogle Scholar
  33. 33.
    Paalasmaa, J., Waris, M., Toivonen, H., Leppäkorpi, L., & Partinen, M. (2012). Unobtrusive online monitoring of sleep at home. Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012, 3784–8.Google Scholar
  34. 34.
    Pancorbo-Hidalgo, P. L., Garcia-Fernandez, F. P., Lopez-Medina, I. M., & Alvarez-Nieto, C. (2006). Risk assessment scales for pressure ulcer prevention: A systematic review. Journal of Advanced Nursing, 54(1), 94–110.CrossRefGoogle Scholar
  35. 35.
    Panfil, E.-M., Gattinger, H., Flnkiger, R., & Manger, S. (2013). Bewegungen objektiver messen: Dekubitusgefährdung und -therapie. Krankenpflege SBK, 106, 22–3.Google Scholar
  36. 36.
    Pilcher, J. J., & Huffcutt, A. J. (1996). Effects of sleep deprivation on performance: A meta-analysis. Sleep, 19(4), 318–26.Google Scholar
  37. 37.
    Rajala, S., & Lekkala, J. (2012). Film-type sensor materials PVDF and EMFi in measurement of cardiorespiratory signals–a review. IEEE Sensors Journal, 12(3), 439–46.CrossRefGoogle Scholar
  38. 38.
    Roehrs, T., Burduvali, E., Bonahoom, A., Drake, C., & Roth, T. (2003). Ethanol and sleep loss: A “dose” comparison of impairing effects. Sleep, 26(8), 981–5.Google Scholar
  39. 39.
    Shin, J. H., Chee, Y., & Suk Park, K. (2006). Long-term sleep monitoring system and long-term sleep parameters using unconstrained method. In International Special Topic Conference on Information Technology in BME 2006.Google Scholar
  40. 40.
    Shin, J. H., Choi, B. H., Lim, Y. G., Jeong, D. U., & Park, K. S. (2008) Automatic ballistocardiogram (BCG) beat detection using a template matching approach. In Annual International Conference of the IEEE Engineering in Medicine and Biology Society, (vol. 2008, pp. 1144–6), Jan 2008.Google Scholar
  41. 41.
    Shino, T., & Watanabe, K. (2010). Noninvasive biosignal measurement of a subject in bed using ceramic sensors. In SICE Annual Conference, (pp. 1559–1562).Google Scholar
  42. 42.
    Stone, K. L., & Ancoli-israel, S. (2008) Chapter 147: Actigraphy. In Principles and practice of sleep medicine (5th ed.). (pp. 1668–1675) Elsevier Inc.Google Scholar
  43. 43.
    Sung Chung, G., Hoon Choi, B., Jeong, D-N., & Park, K. S. (2007) Noninvasive heart rate variability analysis using loadcell-installed bed during sleep. In Annual International Conference of the IEEE Engineering in Medicine and Biology Society, (pp. 2357–2360).Google Scholar
  44. 44.
    Sung Chung, G., Lee, J.-S., Jeong, D.-N., & Suk Park, K. (2009). Slow-wave sleep estimation on a load-cell-installed bed: A non-constrained method. Physiological Measurement, 30(11), 1163–70.CrossRefGoogle Scholar
  45. 45.
    Watanabe, T., & Watanabe, K. (2004). Noncontact method for sleep stage estimation. IEEE Transactions on Biomedical Engineering, 51(10), 1735–48.CrossRefGoogle Scholar
  46. 46.
    Watanabe, K., Watanabe, T., Watanabe, H., Ando, H., Ishikawa, T., & Kobayashi, K. (2005). Noninvasive measurement of heartbeat, respiration, snoring and body movements of a subject in bed via a pneumatic method. IEEE Transactions on Biomedical Engineering, 52(12), 2100–7.CrossRefGoogle Scholar
  47. 47.
    Williamson, A. M., & Feyer, A.-M. (2000). Moderate sleep deprivation produces impairments in cognitive and motor performance equivalent to legally prescribed levels of alcohol intoxication. Occupational and Environmental Medicine, 57(10), 649–55.CrossRefGoogle Scholar
  48. 48.
    Woo Seo, J., Min Choi, J., Bum Shin, H., Lee, J. Y., Jeong, D. U., & Suk Park, K. (2007). Non-constraining sleep/wake monitoring system using bed actigraphy. Medical Biological Engineering Computing, 45(1), 107–14.Google Scholar
  49. 49.
    Zhu, X., Chen, W., Nemoto, T., Kanemitsu, Y., Kitamura, K.-I., Yamakoshi, K.-I., et al. (2006). Real-time monitoring of respiration rhythm and pulse rate during sleep. IEEE Transactions on Biomedical Engineering, 53(12), 2553–3.CrossRefGoogle Scholar
  50. 50.
    Zhu, X., Chen, W., Nemoto, T., Kitamura, K.-I., & Wei, D. (2010). Long-term monitoring of heart rate, respiration rhythm, and body movement during sleep based upon a network. Telemedicine Journal and E-health: The Official Journal of the American Telemedicine Association, 16(2), 244–53.CrossRefGoogle Scholar
  51. 51.
    Ziefle, M., Röcker, C., & Holzinger, A. (2014). Current trends and challenges for pervasive health technologies: From technical innovation to user integration. In Pervasive health: State-of-the-art & beyond (pp. 1–18). London: Springer.Google Scholar

Further Readings

  1. 52.
    Kelly, J., Strecker, R., & Bianchi, M. (2012). Recent developments in home sleep-monitoring devices. ISRN Neurology. 768794, 10 (This paper provides a review of portable monitoring devices which are developed for sleep quality and quantity estimation in the home environment).Google Scholar
  2. 53.
    Kryger, M., Roth, T., & Dement, W. (2011). Principles and practive of sleep medicine (5th ed.). Elsevier, ISBN:978-1-4160-6645-3. (This book gives an excellent overview on many different aspects of sleep medicine).Google Scholar
  3. 54.
    Paalasmaa, J., Waris, M., Toivonen, H., Leppakorpi, L., & Partinen, M. (2012). Unobtrusive online monitoring of sleep at home. In Annual International Conference of the IEEE Engineering in Medicine and Biology Society, (Vol. 2012, pp. 3784–3788) (This paper describes an online sleep monitoring service, based on unobtrusive ballistocardiography (BCG) measurement in an ordinary bed. In combination with environmental information and user-logged tags, this approach is well suited for long-term monitoring at home).Google Scholar

Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  • Daniel Waltisberg
    • 1
  • Bert Arnrich
    • 2
  • Gerhard Tröster
    • 1
  1. 1.Electronics LaboratoryETH ZürichZürichSwitzerland
  2. 2.Department of Computer EngineeringBogazici UniversityIstanbulTurkey

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