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A new method for vital sign detection using FMCW radar based on random body motion cancellation

  • Shuai Fu , Ming Ling EMAIL logo , Zhenhua Li and Long Pan
Published/Copyright: June 9, 2023
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Biomedical Engineering / Biomedizinische Technik
From the journal Biomedical Engineering / Biomedizinische Technik Volume 68 Issue 6

Abstract

In this study, we present a new method for acquiring human vital signs using a Range-Doppler matrix (RDM) of FMCW radar data and a Gaussian interpolation algorithm (GIA). First, the RDM is derived by applying a two-dimensional fast Fourier transform (2D-FFT) to the radar data, and the GIA is applied in the Doppler dimension to estimate the target velocity signal. Subsequently, a robust enhanced trend filtering (RETF) algorithm is used to eliminate the large-scale body motion from the vital signs. Finally, the time-varying filter-based empirical mode decomposition (TVF-EMD) algorithm is employed to extract the respiratory and heartbeat intrinsic mode functions (IMFs), which are filtered according to their respective spectral power to obtain the respiratory and heartbeat frequencies. The proposed method was evaluated using vital signs data collected from seven volunteers (4 males and 3 females) with Texas Instrument’s AWR1642, and the results were compared with data from a reference monitor. The experiments showed that the method had an accuracy of 93 % for respiration and 95 % for heart rate in the presence of random body movements. Unlike traditional radar-based vital signs detection methods, this approach does not rely on range bin selection of the range profile matrix (RPM), thereby avoiding phase wrap problems and producing more accurate results. Currently, research in this field is limited.

Keywords: empirical mode decomposition; Gaussian interpolation; random body motion; trend filtering; vital signs
Corresponding author: Ming Ling, School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, ShangHai, China, Phone: +86 13918372637, E-mail:

Funding source: Shanghai Technology Innovation Project

Award Identifier / Grant number: 21DZ2204300

Acknowledgments

Sincere thanks to Wenzhen Zhang and other volunteers who assisted in data collection.

  1. Research funding: This research was funded by Shanghai Technology Innovation Project, grant number 21DZ2204300.

  2. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Competing interests: Authors state no conflict of interest.

  4. Informed consent: Informed consent was obtained from all individuals included in this study.

  5. Ethical approval: The local Institutional Review Board deemed the study exempt from review.

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Received: 2022-11-17
Accepted: 2023-05-15
Published Online: 2023-06-09
Published in Print: 2023-12-15

© 2023 Walter de Gruyter GmbH, Berlin/Boston

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https://doi.org/10.1515/bmt-2023-0068
Keywords for this article
empirical mode decomposition; Gaussian interpolation; random body motion; trend filtering; vital signs

Articles in the same Issue

  1. Frontmatter
  2. Review
  3. Biomechanical testing of osteosynthetic locking plates for proximal humeral shaft fractures – a systematic literature review
  4. Research Articles
  5. Instrumented treadmill for run biomechanics analysis: a comparative study
  6. Computational modelling of the graft-tunnel interaction in single-bundle ACL reconstructed knee
  7. Biomechanical effects of inclined implant shoulder design in all-on-four treatment concept: a three-dimensional finite element analysis
  8. Extension of the working time of dental composites due to a new type of white operating lamp
  9. Modeling the compliance of the human eye with elastic membranes based on a bionic approach
  10. Region-wise severity analysis of diabetic plantar foot thermograms
  11. A new method for vital sign detection using FMCW radar based on random body motion cancellation
  12. Atherosclerosis plaque tissue classification using self-attention-based conditional variational auto-encoder generative adversarial network using OCT plaque image
  13. A combined impedance compensation strategy applied to external automatic defibrillators
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