Investigating Respiration-Heartbeat Separation Through a Multipoint Scattering Chest Wall Motion Model: 60-GHz FMCW Radar Assessment

This article presents a novel multipoint radar scatter model for accurately simulating human chest wall motion. Based on this model, a comprehensive radar-based simulation system for the measurement of respiration signal (RS) and heartbeat signal (HS) is developed and validated against actual radar experiments, highlighting the challenges in capturing RS and HS and proposing effective measurement techniques. Initially, a chest wall tracking correction algorithm is introduced to precisely determine the chest wall’s radial position relative to the radar, significantly minimizing tracking errors due to random bodily movements, and then extracts phase signals related to chest wall motion. Subsequently, a novel phase difference technique, enhanced by outlier filtering, is proposed to mitigate the impact of low-frequency noise on respiratory measurements and the effect of respiratory harmonics on HS. On this basis, an adaptive Gaussian-weighted filter is proposed, which achieves high-fidelity RS extraction by finding the optimal width of the Gaussian filter. The HS with minimal heart rate (HR) distortion is obtained from the phase signal using a lower order finite impulse response (FIR) filter. The efficacy of these methods was assessed through a 60-GHz frequency-modulated continuous wave (FMCW) radar system in experiments with two subjects, demonstrating minimal distortion in respiratory and heartbeat movement measurements. The correlation between measured and reference values for respiratory rate (RR) and HR exceeded 0.99 and 0.94, respectively. This article also concludes with a validation of the algorithm’s effectiveness across various positions. A quantitative analysis of both simulation and experimental results confirms that the proposed method offers high precision, robustness, and applicability in noninvasive monitoring human RS and HS.