Non-Contact Heart Rate Variability Monitoring with FMCW Radar via a Novel Signal Processing Algorithm

Heart rate variability (HRV), which quantitatively characterizes fluctuations in beat-to-beat intervals, serves as a critical indicator of cardiovascular and autonomic nervous system health. The inherent ability of non-contact methods to eliminate the need for subject contact effectively mitigates u...

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Vydané v:	Sensors (Basel, Switzerland) Ročník 25; číslo 17; s. 5607
Hlavní autori:	Cui, Guangyu, Wang, Yujie, Zhang, Xinyi, Li, Jiale, Liu, Xinfeng, Li, Bijie, Wang, Jiayi, Zhang, Quan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Switzerland MDPI AG 08.09.2025
Predmet:	Accuracy Algorithms Comparative analysis Deep learning Forecasts and trends Heart beat Heart rate Heart Rate - physiology heart rate variability (HRV) Humans Localization Machine learning Measurement Methods millimeter-wave (mmWave) radio Monitoring, Physiologic - methods Neural networks non-contact monitoring Optimization techniques Physiology Radar Radar systems Respiration Signal processing Signal Processing, Computer-Assisted spectral sparse separation algorithm Technology application China millimeter-wave (mmWave) radio spectral sparse separation algorithm heart rate variability (HRV) non-contact monitoring
ISSN:	1424-8220, 1424-8220
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Abstract	Heart rate variability (HRV), which quantitatively characterizes fluctuations in beat-to-beat intervals, serves as a critical indicator of cardiovascular and autonomic nervous system health. The inherent ability of non-contact methods to eliminate the need for subject contact effectively mitigates user burden and facilitates scalable long-term monitoring, thus attracting considerable research interest in non-contact HRV sensing. In this study, we propose a novel algorithm for HRV extraction utilizing FMCW millimeter-wave radar. First, we developed a calibration-free 3D target positioning module that captures subjects’ micro-motion signals through the integration of digital beamforming, moving target indication filtering, and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering techniques. Second, we established separate phase-based mathematical models for respiratory and cardiac vibrations to enable systematic signal separation. Third, we implemented the Second Order Spectral Sparse Separation Algorithm Using Lagrangian Multipliers, thereby achieving robust heartbeat extraction in the presence of respiratory movements and noise. Heartbeat events are identified via peak detection on the recovered cardiac signal, from which inter-beat intervals and HRV metrics are subsequently derived. Compared to state-of-the-art algorithms and traditional filter bank approaches, the proposed method demonstrated an over 50% reduction in average IBI (Inter-Beat Interval) estimation error, while maintaining consistent accuracy across all test scenarios. However, it should be noted that the method is currently applicable only to scenarios with limited subject movement and has been validated in offline mode, but a discussion addressing these two issues is provided at the end.
AbstractList	Heart rate variability (HRV), which quantitatively characterizes fluctuations in beat-to-beat intervals, serves as a critical indicator of cardiovascular and autonomic nervous system health. The inherent ability of non-contact methods to eliminate the need for subject contact effectively mitigates user burden and facilitates scalable long-term monitoring, thus attracting considerable research interest in non-contact HRV sensing. In this study, we propose a novel algorithm for HRV extraction utilizing FMCW millimeter-wave radar. First, we developed a calibration-free 3D target positioning module that captures subjects’ micro-motion signals through the integration of digital beamforming, moving target indication filtering, and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering techniques. Second, we established separate phase-based mathematical models for respiratory and cardiac vibrations to enable systematic signal separation. Third, we implemented the Second Order Spectral Sparse Separation Algorithm Using Lagrangian Multipliers, thereby achieving robust heartbeat extraction in the presence of respiratory movements and noise. Heartbeat events are identified via peak detection on the recovered cardiac signal, from which inter-beat intervals and HRV metrics are subsequently derived. Compared to state-of-the-art algorithms and traditional filter bank approaches, the proposed method demonstrated an over 50% reduction in average IBI (Inter-Beat Interval) estimation error, while maintaining consistent accuracy across all test scenarios. However, it should be noted that the method is currently applicable only to scenarios with limited subject movement and has been validated in offline mode, but a discussion addressing these two issues is provided at the end. Heart rate variability (HRV), which quantitatively characterizes fluctuations in beat-to-beat intervals, serves as a critical indicator of cardiovascular and autonomic nervous system health. The inherent ability of non-contact methods to eliminate the need for subject contact effectively mitigates user burden and facilitates scalable long-term monitoring, thus attracting considerable research interest in non-contact HRV sensing. In this study, we propose a novel algorithm for HRV extraction utilizing FMCW millimeter-wave radar. First, we developed a calibration-free 3D target positioning module that captures subjects' micro-motion signals through the integration of digital beamforming, moving target indication filtering, and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering techniques. Second, we established separate phase-based mathematical models for respiratory and cardiac vibrations to enable systematic signal separation. Third, we implemented the Second Order Spectral Sparse Separation Algorithm Using Lagrangian Multipliers, thereby achieving robust heartbeat extraction in the presence of respiratory movements and noise. Heartbeat events are identified via peak detection on the recovered cardiac signal, from which inter-beat intervals and HRV metrics are subsequently derived. Compared to state-of-the-art algorithms and traditional filter bank approaches, the proposed method demonstrated an over 50% reduction in average IBI (Inter-Beat Interval) estimation error, while maintaining consistent accuracy across all test scenarios. However, it should be noted that the method is currently applicable only to scenarios with limited subject movement and has been validated in offline mode, but a discussion addressing these two issues is provided at the end.Heart rate variability (HRV), which quantitatively characterizes fluctuations in beat-to-beat intervals, serves as a critical indicator of cardiovascular and autonomic nervous system health. The inherent ability of non-contact methods to eliminate the need for subject contact effectively mitigates user burden and facilitates scalable long-term monitoring, thus attracting considerable research interest in non-contact HRV sensing. In this study, we propose a novel algorithm for HRV extraction utilizing FMCW millimeter-wave radar. First, we developed a calibration-free 3D target positioning module that captures subjects' micro-motion signals through the integration of digital beamforming, moving target indication filtering, and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering techniques. Second, we established separate phase-based mathematical models for respiratory and cardiac vibrations to enable systematic signal separation. Third, we implemented the Second Order Spectral Sparse Separation Algorithm Using Lagrangian Multipliers, thereby achieving robust heartbeat extraction in the presence of respiratory movements and noise. Heartbeat events are identified via peak detection on the recovered cardiac signal, from which inter-beat intervals and HRV metrics are subsequently derived. Compared to state-of-the-art algorithms and traditional filter bank approaches, the proposed method demonstrated an over 50% reduction in average IBI (Inter-Beat Interval) estimation error, while maintaining consistent accuracy across all test scenarios. However, it should be noted that the method is currently applicable only to scenarios with limited subject movement and has been validated in offline mode, but a discussion addressing these two issues is provided at the end.
Audience	Academic
Author	Li, Bijie Wang, Yujie Zhang, Xinyi Wang, Jiayi Li, Jiale Cui, Guangyu Liu, Xinfeng Zhang, Quan
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Cites_doi	10.3390/s22093106 10.1186/s13634-022-00899-8 10.3390/s24072026 10.1152/ajpheart.00230.2014 10.3390/app14020921 10.1109/COMST.2022.3177305 10.1109/TMTT.2021.3076239 10.1109/TBME.2021.3066876 10.1109/JIOT.2021.3075167 10.1109/ACCESS.2019.2921240 10.1016/j.cpr.2016.09.003 10.1016/0141-5425(84)90056-6 10.1109/JIOT.2021.3051580 10.1109/TIM.2020.2978347 10.3390/s21113588 10.3390/s17112637 10.1007/BF02441473 10.1109/TRS.2023.3333628 10.3390/biomimetics9080481 10.1109/TBME.2013.2288319 10.3390/rs12142265 10.1016/j.apcbee.2013.08.016 10.3390/s20082351
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SubjectTerms	Accuracy Algorithms Comparative analysis Deep learning Forecasts and trends Heart beat Heart rate Heart Rate - physiology heart rate variability (HRV) Humans Localization Machine learning Measurement Methods millimeter-wave (mmWave) radio Monitoring, Physiologic - methods Neural networks non-contact monitoring Optimization techniques Physiology Radar Radar systems Respiration Signal processing Signal Processing, Computer-Assisted spectral sparse separation algorithm Technology application
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Title	Non-Contact Heart Rate Variability Monitoring with FMCW Radar via a Novel Signal Processing Algorithm
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