Non-contact seismocardiogram measurement and HRV analysis using cardiac beamforming with FMCW radar

TY - JOUR

T1 - Non-contact seismocardiogram measurement and HRV analysis using cardiac beamforming with FMCW radar

AU - Yu, Guang

AU - Yang, Chenxi

AU - Li, Haobo

AU - Wang, Chaochao

AU - Zhang, Xianchao

AU - Li, Jianqing

AU - Liu, Chengyu

N1 - Publisher Copyright: Copyright © 2026 Yu, Yang, Li, Wang, Zhang, Li and Liu.

PY - 2026/1/23

Y1 - 2026/1/23

N2 - Introduction: Heart rate variability (HRV) is a vital metric for assessing cardiovascular health, psychological stress, and sleep quality. Non-contact HRV monitoring offers advantages in safety, comfort, and hygiene, making it an increasingly attractive solution. Methods: In this study, we propose a high-precision, non-contact HRV analysis method using a 77 GHz multiple-input multiple-output (MIMO) frequency-modulated continuous wave (FMCW) radar system. The proposed method first employs an optimized Capon beamforming algorithm to accurately localize the heart and enhance intermediate frequency (IF) signals from the heart’s direction. A modified differentiate and cross-multiply (MDACM) algorithm is then used to demodulate the phase sequence, yielding a raw vital sign signal that includes both respiratory and cardiac components. This signal is further processed using a six-level wavelet packet transform (WPT), from which specific wavelet coefficients (6th to 12th bands at level six) are selected to reconstruct the seismocardiogram (SCG) signal. To extract precise inter-beat interval (IBI) sequences, a robust aortic valve opening (AO) point detection algorithm is developed. Time-domain HRV indices—including the standard deviation of normal-to-normal intervals (SDNN), the root mean square of successive differences (RMSSD), and the percentage of successive normal-to-normal intervals differing by more than 50 milliseconds (ms) (pNN50)—are then computed from the IBI sequence. To validate the approach, we developed a synchronized data acquisition system combining radar and electrocardiogram (ECG) sensors and collected data from 13 participants—each person collected data for 10 min. Results: Experimental results demonstrate the effectiveness of our method, achieving average errors of 4.11 ms in SDNN, 8.05 ms in RMSSD, and 2.15% in pNN50 compared to ECG-derived ground truth. Discussion: These results outperform existing non-contact HRV monitoring techniques and highlight the method’s potential for practical, continuous, and unobtrusive cardiovascular monitoring.

AB - Introduction: Heart rate variability (HRV) is a vital metric for assessing cardiovascular health, psychological stress, and sleep quality. Non-contact HRV monitoring offers advantages in safety, comfort, and hygiene, making it an increasingly attractive solution. Methods: In this study, we propose a high-precision, non-contact HRV analysis method using a 77 GHz multiple-input multiple-output (MIMO) frequency-modulated continuous wave (FMCW) radar system. The proposed method first employs an optimized Capon beamforming algorithm to accurately localize the heart and enhance intermediate frequency (IF) signals from the heart’s direction. A modified differentiate and cross-multiply (MDACM) algorithm is then used to demodulate the phase sequence, yielding a raw vital sign signal that includes both respiratory and cardiac components. This signal is further processed using a six-level wavelet packet transform (WPT), from which specific wavelet coefficients (6th to 12th bands at level six) are selected to reconstruct the seismocardiogram (SCG) signal. To extract precise inter-beat interval (IBI) sequences, a robust aortic valve opening (AO) point detection algorithm is developed. Time-domain HRV indices—including the standard deviation of normal-to-normal intervals (SDNN), the root mean square of successive differences (RMSSD), and the percentage of successive normal-to-normal intervals differing by more than 50 milliseconds (ms) (pNN50)—are then computed from the IBI sequence. To validate the approach, we developed a synchronized data acquisition system combining radar and electrocardiogram (ECG) sensors and collected data from 13 participants—each person collected data for 10 min. Results: Experimental results demonstrate the effectiveness of our method, achieving average errors of 4.11 ms in SDNN, 8.05 ms in RMSSD, and 2.15% in pNN50 compared to ECG-derived ground truth. Discussion: These results outperform existing non-contact HRV monitoring techniques and highlight the method’s potential for practical, continuous, and unobtrusive cardiovascular monitoring.

KW - aortic opening (AO) detection

KW - beamforming

KW - frequency-modulated continuous-wave (FMCW) radar

KW - heart rate variability (HRV)

KW - interbeat interval (IBI)

KW - seismocardiogram (SCG)

KW - wavelet packet transform (WPT)

UR - https://www.scopus.com/pages/publications/105029488479

U2 - 10.3389/fphys.2025.1733573

DO - 10.3389/fphys.2025.1733573

M3 - Article

C2 - 41659925

AN - SCOPUS:105029488479

SN - 1664-042X

VL - 16

SP - 1

EP - 19

JO - Frontiers in Physiology

JF - Frontiers in Physiology

M1 - 1733573

ER -