Extended Kalman Filter for Doppler radar cardiopulmonary monitoring system

Remote monitoring of cardiopulmonary activities using quadrature direct conversion Doppler radar shows promise in medical and security applications. One major challenge for such monitoring is demodulation of the IQ signals and heart and respiration rate determination. This paper presents a rate finding algorithm based on extended Kalman filter (EKF) and principal component analysis. We present a state space model of the quadrature IQ signals and use it with the EKF to simultaneously estimate and track heart and respiration rate by a unified statistical approach. We evaluate the performance of the algorithm for practical data obtained from the implemented hardware and present the results to illustrate the feasibility and accuracy of the algorithm. Results are compared with reference ECG results which clearly demonstrate that the proposed algorithm can be successfully applied for heart and respiration rate detection.