Remote Monitoring of Human Vital Signs Based on 77-GHz mm-Wave FMCW Radar

Abstract

In recent years, non-contact radar detection technology has been able to achieve long-term and long-range detection for the breathing and heartbeat signals. Compared with contact-based detection methods, it brings a more comfortable and a faster experience to the human body, and it has gradually received attention in the field of radar sensing. Therefore, this paper extends the application of millimeter-wave radar to the field of health care. The millimeter-wave radar first transmits the frequency-modulated continuous wave (FMCW) and collects the echo signals of the human body. Then, the phase information of the intermediate frequency (IF) signals including the breathing and heartbeat signals are extracted, and the Direct Current (DC) offset of the phase information is corrected using the circle center dynamic tracking algorithm. The extended differential and cross-multiply (DACM) is further applied for phase unwrapping. We propose two algorithms, namely the compressive sensing based on orthogonal matching pursuit (CS-OMP) algorithm and rigrsure adaptive soft threshold noise reduction based on discrete wavelet transform (RA-DWT) algorithm, to separate and reconstruct the breathing and heartbeat signals. Then, a frequency-domain fast Fourier transform and a time-domain autocorrelation estimation algorithm are proposed to calculate the respiratory and heartbeat rates. The proposed algorithms are compared with the contact-based detection ones. The results demonstrate that the proposed algorithms effectively suppress the noise and harmonic interference, and the accuracies of the proposed algorithms for both respiratory rate and heartbeat rate reach about 93%.

An Inclusive Survey of Contactless Wireless Sensing: A Technology Used for Remotely Monitoring Vital Signs Has the Potential to Combating COVID-19

“…FMCW is used in radar systems for sensing, localizing, and tracking an object in front of it by measuring the range, velocity, and angle of arrival of the reflected wave. In healthcare, this technique sends a special sinusoid signal to a subject then the system analyzes the reflected signal based on its frequency and amplitude to extract information about the observed person [11] [12]. Here, we present the principle of this technique by answering the following four questions:…”

“…In [25], the vertical axis uses a single chir time, and N = 1. However, we experimentally found that using multiple chirps achieves a FIGURE 22: Human breathing monitoring using 77 GhHz mm-wave FMCW radar [11].…”

“…This means that the corresponding training sample xn does not contribute to the classification process, giving rise to the sparseness of SVM. A detailed description of SVM can be found in standard textbooks like [11].…”

“…This procedure is repeated N times, each time with a different sample being held out. c) k-Nearest Neighbours: kNN is considered to be a nonparametric method and despite its simplicity, it often achieves good performance in practice [11], [13]. One advantage is that kNN does not require a training phase.…”

Continuous In-Bed Monitoring of Vital Signs Using a Multi Radar Setup for Freely Moving Patients

“…For example, continuous-wave (CW) Doppler radars at [ 13 , 14 ], [ 15 ] and 24 [ 16 , 17 ] were built and used to measure respiration, heartbeat, or motion activity. Furthermore, frequency-modulated CW systems at 77 [ 18 , 19 ] and 122 [ 20 ] are used to measure vital parameters of several people simultaneously by separating the targets. In addition, ultra-wideband radars with different frequencies and bandwidths are used to measure vital parameters [ 21 , 22 , 23 ].…”