Human respiration rate estimation using ultra-wideband distributed cognitive radar system

Article

Abstract

It has been shown that remote monitoring of pulmonary activity can be achieved using ultra-wideband (UWB) systems, which shows promise in home healthcare, rescue, and security applications. In this paper, we first present a multi-ray propagation model for UWB signal, which is traveling through the human thorax and is reflected on the air/dry-skin/fat/muscle interfaces. A geometry-based statistical channel model is then developed for simulating the reception of UWB signals in the indoor propagation environment. This model enables replication of time-varying multipath profiles due to the displacement of a human chest. Subsequently, a UWB distributed cognitive radar system (UWB-DCRS) is developed for the robust detection of chest cavity motion and the accurate estimation of respiration rate. The analytical framework can serve as a basis in the planning and evaluation of future measurement programs. We also provide a case study on how the antenna beamwidth affects the estimation of respiration rate based on the proposed propagation models and system architecture.

Keywords

Medical and patient monitoring sensing technologies and signal processing vital sign ultra-wideband distributed cognitive radar respiration rate estimation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    R. N. Anderson. A Method for Constructing Complete Annual U.S. Life Tables. National Center for Health Statistics, Vital Health Statistics, vol. 2, no. 129, pp. 1–28, 1999.Google Scholar
  2. [2]
    A. N. Vgontzas, A. Kales. Sleep and its Disorders. Annual Review of Medicine, vol. 50, no. 1, pp. 387–400, 1999.CrossRefGoogle Scholar
  3. [3]
    F. Michahelles, R. Wicki, B. Shiele. Less Contact: Heartrate Detection without Even Touching the User. In Proceedings of the 8th International Symposium on Wearable Computers, IEEE Press, Washington DC, USA, pp. 4–7, 2004.CrossRefGoogle Scholar
  4. [4]
    E. M. Staderini. UWB Radars in Medicine. IEEE Aerospace and Electronic Systems Magazine, vol. 17, no. 1, pp. 13–18, 2002.CrossRefGoogle Scholar
  5. [5]
    G. Ossberger, T. Buchegger, E. Schimback, A. Stelzer, R. Weigel. Non-invasive Respiratory Movement Detection and Monitoring of Hidden Humans Using UltraWideband Pulse Radar. In Proceedings of International Workshop on Ultra Wideband Systems Joint with Conference on Ultrawideband Systems and Technologies, IEEE Press, Kyoto, Japan, pp. 395–399, 2004.CrossRefGoogle Scholar
  6. [6]
    I. Y. Immoreev, S. Samkov, T.H. Tao. Short-distance Ultra Wideband Radars. IEEE Aerospace and Electronic Systems Magazine, vol. 20, no. 6, pp. 9–14, 2005.CrossRefGoogle Scholar
  7. [7]
    S. Venkatesh, C. R. Anderson, N. V. Rivera, R.M. Buehrer. Implementation and Analysis of Respiration-rate Estimation Using Impulse-based UWB. In Proceedings of IEEE Military Communications Conference, IEEE Press, New Jersey, USA, pp. 3314–3320, 2005.Google Scholar
  8. [8]
    Y. Chen, E. Gunawan, K. S. Low, Y. Kim, C. B. Soh, A. R. Leyman, L. L. Thi. Non-invasive Respiration Rate Estimation Using Ultra-wideband Distributed Cognitive Radar System. In Proceedings of IEEE International Conference of the Engineering in Medicine and Biology Society, IEEE Press, New York, USA, pp. 920–923, 2006.Google Scholar
  9. [9]
    L. W. Chua. A New UWB Antenna With Excellent Time Domain Characteristics. In Proceedings of European Conference on Wireless Technology, Paris, France, pp. 531–534, 2005.Google Scholar
  10. [10]
    J. Y. Lee, S. Yoo. Large Error Performance of UWB Ranging in Multipath and Multiuser Environments. IEEE Transactions on Microwave Theory and Techniques, vol. 54, no. 4, pp. 1887–1895, 2006.CrossRefGoogle Scholar
  11. [11]
    S. Gabriel, R.W. Lau, C. Gabriel. The Dielectric Properties of Biological Tissues: III. Parametric Models for the Dielectric Spectrum of Tissues. Physics in Medicine and Biology, vol. 41, no. 11, pp. 2271–2293, 1996.CrossRefGoogle Scholar
  12. [12]
    Y. Chen, V. K. Dubey. Accuracy of Geometric Channelmodeling Methods. IEEE Transactions on Vehicular Technology, vol. 53, no. 1, pp. 82–93, 2004.CrossRefGoogle Scholar
  13. [13]
    F. Amoroso, W. W. Jones. Geometric Model for DSPN Satellite Reception in the Dense Scatterer Mobile Environment. IEEE Transactions on Communications, vol. 41, no. 3, pp. 450–453, 1993.CrossRefGoogle Scholar
  14. [14]
    B. Alavi, K. Pahlavan. Modeling of the Distance Error for Indoor Geolocation. In Proceedings of IEEE Wireless Communications and Networking Conference, IEEE Press, Louisiana, USA, pp. 668–672, 2003.Google Scholar
  15. [15]
    B. Alavi, K. Pahlavan. Bandwidth Effect on Distance Error Modeling for Indoor Geolocation. In Proceedings of International Symposium on Personal, Indoor and Mobile Radio Communications, IEEE Press, Beijing, PRC, pp. 2198–2202, 2003.Google Scholar
  16. [16]
    B. Denis, N. Daniele. NLOS Ranging Error Mitigation in a Distributed Positioning Algorithm for Indoor UWB Ad-hoc Networks. In Proceedings of IEEE International Workshop on Wireless Ad-hoc Networks, IEEE Press, Oulu, Finland, pp. 356–360, 2004.CrossRefGoogle Scholar
  17. [17]
    L. Yang, G. B. Giannakis. Ultra-wideband Communications: An Idea Whose Time Has Come. IEEE Signal Processing Magazine, vol. 21, no. 6, pp. 26–54, 2004.CrossRefGoogle Scholar
  18. [18]
    S. Haykin. Cognitive Radar: A Way of the Future. IEEE Signal Processing Magazine, vol. 23, no. 1, pp. 30–40, 2006.CrossRefGoogle Scholar
  19. [19]
    W. Suwansantisuk, M. Z. Win, L. A. Shepp. On the Performance of Wide-bandwidth Signal Acquisition in Dense Multipath Channels. IEEE Transactions on Vehicular Technology, vol. 54, no. 5, pp. 1584–1594, 2005.CrossRefGoogle Scholar
  20. [20]
    S. M. Kay. Fundamentals of Statistical Signal Processing: Detection Theory, PTR Prentice Hall, 1998.Google Scholar
  21. [21]
    S. M. Kay. Fundamentals of Statistical Signal Processing: Estimation Theory, PTR Prentice Hall, 1993.Google Scholar

Copyright information

© Institute of Automation, Chinese Academy of Sciences and Springer-Verlag GmbH 2008

Authors and Affiliations

  1. 1.School of EngineeringUniversity of GreenwichKentUK
  2. 2.School of Computer, Electronic and InformationGuangxi UniversityNanningPRC

Personalised recommendations