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Biomedizinische Technik. Biomedical engineering
Dec 15, 2023;68 (6): 617-632.

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

Shuai Fu, Ming Ling, Zhenhua Li, Long Pan
Author Information
  1. Shuai Fu: School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, ShangHai, China.
  2. Ming Ling: School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, ShangHai, China.
  3. Zhenhua Li: Shanghai Baolong Automotive Corporation, ShangHai, China.
  4. Long Pan: Shanghai Baolong Automotive Corporation, ShangHai, China.
PMID: 37289651 DOI: 10.1515/bmt-2023-0068

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

Gaussian interpolation empirical mode decomposition random body motion trend filtering vital signs

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MeSH Term

Male
Female
Humans
Signal Processing, Computer-Assisted
Radar
Vital Signs
Heart Rate
Respiratory Rate
Algorithms
Journal Article

Word Cloud

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