Spectrum-averaged Harmonic Path (SHAPA) algorithm for non-contact vital sign monitoring with ultra-wideband (UWB) radar

We introduce the Spectrum-averaged Harmonic Path (SHAPA) algorithm for estimation of heart rate (HR) and respiration rate (RR) with Impulse Radio Ultrawideband (IR-UWB) radar. Periodic movement of human torso caused by respiration and heart beat induces fundamental frequencies and their harmonics at the respiration and heart rates. IR-UWB enables capture of these spectral components and frequency domain processing enables a low cost implementation. Most existing methods of identifying the fundamental component either in frequency or time domain to estimate the HR and/or RR lead to significant error if the fundamental is distorted or cancelled by interference. The SHAPA algorithm (1) takes advantage of the HR harmonics, where there is less interference, and (2) exploits the information in previous spectra to achieve more reliable and robust estimation of the fundamental frequency in the spectrum under consideration. Example experimental results for HR estimation demonstrate how our algorithm eliminates errors caused by interference and produces 16% to 60% more valid estimates.

[1]  F. Harris.  On the use of windows for harmonic analysis with the discrete Fourier transform , 1978, Proceedings of the IEEE.

[2]  E. M. Staderini,et al.  UWB radars in medicine , 2002 .

[3]  Laura Anitori,et al.  FMCW radar for life-sign detection , 2009, 2009 IEEE Radar Conference.

[4]  Marco Baldi,et al.  Analysis and simulation of algorithms for vital signs detection using UWB radars , 2011, 2011 IEEE International Conference on Ultra-Wideband (ICUWB).

[5]  Cheong Boon Soh,et al.  Wireless Sensing of Human Respiratory Parameters by Low-Power Ultrawideband Impulse Radio Radar , 2011, IEEE Transactions on Instrumentation and Measurement.

[6]  Teh-Ho Tao,et al.  UWB radar for patient monitoring , 2008, IEEE Aerospace and Electronic Systems Magazine.

[7]  森利·富.  Ultra wideband monitoring systems and antennas , 2007 .

[8]  Bernd Schleicher,et al.  Vital signs monitoring with a UWB radar based on a correlation receiver , 2010, Proceedings of the Fourth European Conference on Antennas and Propagation.

[9]  Frédo Durand,et al.  Eulerian video magnification for revealing subtle changes in the world , 2012, ACM Trans. Graph..

[10]  Enrico Staderini,et al.  On the UWB medical radars working principles , 2011, Int. J. Ultra Wideband Commun. Syst..

[11]  S. Venkatesh,et al.  Implementation and analysis of respiration-rate estimation using impulse-based UWB , 2005, MILCOM 2005 - 2005 IEEE Military Communications Conference.

[12]  Mary Ann Weitnauer,et al.  Harmonic Path (HAPA) algorithm for non-contact vital signs monitoring with IR-UWB radar , 2013, 2013 IEEE Biomedical Circuits and Systems Conference (BioCAS).

[13]  David Girbau,et al.  ANALYSIS OF VITAL SIGNS MONITORING USING AN IR-UWB RADAR , 2010 .

[14]  Richard O. Claus,et al.  A 'smart' bed for non-intrusive monitoring of patient physiological factors , 2004 .

[15]  Alireza Ahmadian,et al.  An accurate and robust algorithm for detection of heart and respiration rates using an impulse based UWB signal , 2009, 2009 International Conference on Biomedical and Pharmaceutical Engineering.

[16]  Mohammad Eshghi,et al.  A New Algorithm for Detection Motion Rate Based on Energy in Frequency Domain Using UWB Signals , 2010, 2010 4th International Conference on Bioinformatics and Biomedical Engineering.

[17]  Olga Boric-Lubecke,et al.  A new algorithm for detection of heart and respiration rate with UWB signals , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[18]  O. Postolache,et al.  Vital Signs Monitoring System Based on EMFi Sensors and Wavelet Analysis , 2007, 2007 IEEE Instrumentation & Measurement Technology Conference IMTC 2007.