High-Accuracy Heart Rate Variability Monitoring Using Doppler Radar Based on Gaussian Pulse Train Modeling and FTPR Algorithm

This paper presents the theoretical and experimental study of a novel noncontact heart-beat signal modeling and estimation algorithm using a compact 2.4-GHz Doppler radar. The proposed technique is able to accurately reconstruct the heart-beat signal and generates heart rate variability indices at a distance of 1.5 m away from the human body. The feasibility of the proposed approach is validated by obtaining data from eight human subjects and comparing them with photoplethysmography (PPG) measurements. A Gaussian pulse train model is suggested for the heart-beat signal along with a modified-and-combined autocorrelation and frequency-time phase regression technique for high-accuracy detection of the human heart-beat rate. The proposed method is accurate, robust, and simple, and demonstrates an average heart-beat detection accuracy of more than 90% at a distance of 1.5 m away from the subjects. In addition, the average beat-to-beat time intervals extracted from the proposed model and signal reconstruction method show less than 2% error compared to PPG measurements. Bland-Altman analysis further validated the accuracy of the proposed approach in comparison with reference data.