Contactless and Continuous Blood Pressure Detection With FMCW Millimeter-Wave Radar

Blood pressure (BP) is a vital sign that could provide crucial information for the prevention, diagnosis, and treatment of many diseases. Most of the existing BP detection solutions require physical contact between human skin and sensor, which may be frustrated due to uncomfortable experience and extra burden. Recently, millimeter-wave (mm-wave) radar-based BP detection has attracted more attention, but it is confronted with the challenges on noise interference, fine-grained arterial pulse signal recovery, and a shortage of annotated samples for training BP estimation model. In this article, we develop millimeter-wave radar-based blood pressure (mmRBP), a contactless and continuous BP detection system with a single frequency-modulated continuous wave (FMCW) mm-wave radar. mmRBP consists of three modules. The arterial pulse motion extraction module is designed to mitigate the noise interference, in which the range fast Fourier transform (range-FFT) and digital beamforming (DBF) are applied to scan the space, and the direction with the strongest arterial pulse motion is identified. Then, the arterial pulse signal translation module is devised to transform the mm-wave reflections to fine-grained arterial pulse signal by the convolutional neural network (CNN)-based translation filter. Finally, the BP estimation module is designed to address the issue of shortage of annotated radar training samples, where a deep transfer learning (DTL) model is employed to transfer the knowledge of the source network trained by the public BP dataset to the target network trained by the collected radar dataset. Results of extensive experiments show that the error of mmRBP is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.87~\pm ~6.12$ </tex-math></inline-formula> mmHg [mean error (ME) ± standard deviation (STD)] and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.59~\pm ~3.78$ </tex-math></inline-formula> mmHg for systolic BP (SBP) and diastolic BP (DBP), which satisfy both the British Hypertension Society (BHS) and the Association for the Advancement of Medical Instrumentation (AAMI) BP measurement standards.

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Publication Details

Journal
IEEE Sensors Journal
Published
2025-03-11
DOI
https://doi.org/10.1109/jsen.2025.3548259
Citations
1
Primary Topic
Non-Invasive Vital Sign Monitoring
Type
article
Field-Weighted Citation Impact
0.67

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Contactless and Continuous Blood Pressure Detection With FMCW Millimeter-Wave Radar

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article

Contactless and Continuous Blood Pressure Detection With FMCW Millimeter-Wave Radar

Wendong Xiao, Zhiqiang Zhang, Ziyang Feng, Sen Zhang, Luyao Liu, Jie Zhang
article en
1 citations

Abstract

Blood pressure (BP) is a vital sign that could provide crucial information for the prevention, diagnosis, and treatment of many diseases. Most of the existing BP detection solutions require physical contact between human skin and sensor, which may be frustrated due to uncomfortable experience and extra burden. Recently, millimeter-wave (mm-wave) radar-based BP detection has attracted more attention, but it is confronted with the challenges on noise interference, fine-grained arterial pulse signal recovery, and a shortage of annotated samples for training BP estimation model. In this article, we develop millimeter-wave radar-based blood pressure (mmRBP), a contactless and continuous BP detection system with a single frequency-modulated continuous wave (FMCW) mm-wave radar. mmRBP consists of three modules. The arterial pulse motion extraction module is designed to mitigate the noise interference, in which the range fast Fourier transform (range-FFT) and digital beamforming (DBF) are applied to scan the space, and the direction with the strongest arterial pulse motion is identified. Then, the arterial pulse signal translation module is devised to transform the mm-wave reflections to fine-grained arterial pulse signal by the convolutional neural network (CNN)-based translation filter. Finally, the BP estimation module is designed to address the issue of shortage of annotated radar training samples, where a deep transfer learning (DTL) model is employed to transfer the knowledge of the source network trained by the public BP dataset to the target network trained by the collected radar dataset. Results of extensive experiments show that the error of mmRBP is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.87~\pm ~6.12$ </tex-math></inline-formula> mmHg [mean error (ME) ± standard deviation (STD)] and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.59~\pm ~3.78$ </tex-math></inline-formula> mmHg for systolic BP (SBP) and diastolic BP (DBP), which satisfy both the British Hypertension Society (BHS) and the Association for the Advancement of Medical Instrumentation (AAMI) BP measurement standards.

IEEE Sensors JournalVol. 25(9)
Queen's University Belfast (GB), University of Leeds (GB), Shanghai Micro Electronics Equipment (China) (CN), University of Science and Technology Beijing (CN)
National Natural Science Foundation of China, Natural Science Foundation of Shandong Province, Fundamental Research Funds for the Central Universities
Openalex Percentile: Top 34%
Non-Invasive Vital Sign Monitoring
0.67
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