Circadian Rhythm Evaluation Using Fuzzy Logic

  • Martin CernyEmail author
  • Miroslav Pokorny
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 457)


Useful information about person’s behavior and its changes provides the measurement of the physical activity of the monitored person in flat. The identified changes are cyclic with a period of approximately 24 hours – this is Circadian Rhythm of Activity, CAR. In the event that we correlate CAR with information about the type of room and activities envisaged in this room, we can define the circadian rhythm (CR). The CR evaluation is made by different mathematical and statistical procedures now. Such systems do not mostly have predictive character.

This work uses for classification and prediction of significant circadian rhythms deviations diagnostic method - fuzzy expert system. This methodology allows quick and effective decision-making and it shows predictive ability to detect deviations of circadian rhythm.


Circadian rhythm Fuzzy logic Remote home care 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Chan, M., Campo, E., et al.: Smart homes - Current features and future perspectives Maturitas, vol. 64, pp. 90–97 (2009) ISSN: 0378-5122Google Scholar
  2. 2.
    Virone, G., Noury, N., Demongeot, J.: A System for Automatic Measurement of Circadian Activity Deviations in Telemedicine. IEEE TBME 49(12) (2002)Google Scholar
  3. 3.
    Chan, M., Campo, E., Lavaland, E., Esteve, D.: Validation of a remote monitoring system for the elderly: Application to mobility measurements. Technology and Health Care 10, 391–399 (2000)Google Scholar
  4. 4.
    Project MonAmi – Mainstreaming on Ambient Intelligence,
  5. 5.
    Cerny, M., Penhaker, M.: Wireless Body Sensor Network in Health Maintenance system. Journal Electronics and Electrical Engineering 11(9), ISSN 1392-1215Google Scholar
  6. 6.
    Poujaud, J., Noury, N.: Identification of inactivity behavior in Smart Homes. In: Proceedings 30th Annual International IEEE EMBS Conference, pp. 2075–2078. IEEE (2008) ISBN 978-1-4244-1815-2Google Scholar
  7. 7.
    Shin, J.H., Lee, B., Park, K.: Park Detection of Abnormal Living Patterns for Elderly Living Alone Using Support Vector Data Description. IEEE Trans. on Information Technology in Biomedicine 14(3), 438–448 (2011), doi:10.1109/TITB.2011.2113352CrossRefGoogle Scholar
  8. 8.
    Cerny, M.: Circadian Rhythm Evaluation in Remote Home Care. Ph.D thesis, VSB – Technical University of Ostrava, Ostrava, Czech Republic (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.VSB – Technical University of OstravaOstravaCzech Republic

Personalised recommendations