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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Veröffentlicht in:Sensors (Basel, Switzerland) Jg. 25; H. 17; S. 5607
Hauptverfasser: Cui, Guangyu, Wang, Yujie, Zhang, Xinyi, Li, Jiale, Liu, Xinfeng, Li, Bijie, Wang, Jiayi, Zhang, Quan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland MDPI AG 08.09.2025
Schlagworte:
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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Zusammenfassung: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.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s25175607