Parametric extraction of cardiac and respiratory rates from radar measurements of the human body

Cardiac and respiratory motion of the chest cavity, crucial to vital sign detection, has not been considered in the study of electromagnetic (EM) wave interaction with tissues using phantom models commonly found in commercial EM software packages. The volume and physical composition of the tissue layers near the surface change with physiological motion, thereby imparting time dependence to the constitutive parameters. In this paper, we consider a human body phantom model that includes physiological motion and develop a new algorithm based on the state-space method to extract cardiac and respiration rates. The method is applied to simulated data as well as 24 GHz radar measurements on a sedentary subject. It is shown that the state-space method accurately estimates vital signs without producing harmonics and inter-modulation products that plague signal resolution in commonly used auto-correlated FFT spectrograms relying on peak detection.