Design of a Millimeter-Wave Radar Remote Monitoring System for the Elderly Living Alone Using WIFI Communication

Abstract

In response to the current demand for the remote monitoring of older people living alone, a non-contact human vital signs monitoring system based on millimeter wave radar has gradually become the object of research. This paper mainly carried out research regarding the detection method to obtain human breathing and heartbeat signals using a frequency modulated continuous wave system. We completed a portable millimeter-wave radar module for wireless communication. The radar module was a small size and had a WIFI communication interface, so we only needed to provide a power cord for the radar module. The breathing and heartbeat signals were detected and separated by FIR digital filter and the wavelet transform method. By building a cloud computing framework, we realized remote and senseless monitoring of the vital signs for older people living alone. Experiments were also carried out to compare the performance difference between the system and the common contact detection system. The experimental results showed that the life parameter detection system based on the millimeter wave sensor has strong real-time performance and accuracy.

Full text

sensors
Article
Design of a Millimeter-Wave Radar Remote Monitoring System
for the Elderly Living Alone Using WIFI Communication
Kai Guo 1,2 , Chang Liu 3, Shasha Zhao 2, Jingxin Lu 3, Senhao Zhang 1and Hongbo Yang 1,2,*


Citation: Guo, K.; Liu, C.; Zhao, S.;
Lu, J.; Zhang, S.; Yang, H. Design of a
Millimeter-Wave Radar Remote
Monitoring System for the Elderly
Living Alone Using WIFI
Communication. Sensors 2021,21,
7893. https://doi.org/10.3390/
s21237893
Academic Editor: Óscar Belmonte
Fernández
Received: 29 October 2021
Accepted: 24 November 2021
Published: 26 November 2021
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affil-
iations.
Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
1School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine,
University of Science and Technology of China, Hefei 230026, China; guok@sibet.ac.cn (K.G.);
zsh1996@mail.ustc.edu.cn (S.Z.)
2Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences,
Suzhou 215163, China; zhaoss@sibet.ac.cn
3School of Mechanical and Electrical Engineering, Changchun University of Science and Technology,
Changchun 130001, China; liuchang5224@163.com (C.L.); Q1246995415@163.com (J.L.)
*Correspondence: yanghb@sibet.ac.cn
Abstract:
In response to the current demand for the remote monitoring of older people living alone,
a non-contact human vital signs monitoring system based on millimeter wave radar has gradually
become the object of research. This paper mainly carried out research regarding the detection method
to obtain human breathing and heartbeat signals using a frequency modulated continuous wave
system. We completed a portable millimeter-wave radar module for wireless communication. The
radar module was a small size and had a WIFI communication interface, so we only needed to
provide a power cord for the radar module. The breathing and heartbeat signals were detected and
separated by FIR digital filter and the wavelet transform method. By building a cloud computing
framework, we realized remote and senseless monitoring of the vital signs for older people living
alone. Experiments were also carried out to compare the performance difference between the system
and the common contact detection system. The experimental results showed that the life parameter
detection system based on the millimeter wave sensor has strong real-time performance and accuracy.
Keywords: millimeter wave; phase unwrapping; FIR digital filter; health monitoring
1. Introduction
Most existing measuring instruments require physical contact; they need to be attached
to the patient for measurement and monitoring. Thus, these measuring instruments are
not convenient for patients who require continuous monitoring for a long time. Moreover,
in light of the current COVID-19 pandemic, non-contact vital signs monitoring equipment
could become more important. Non-contact monitoring could help minimize the spread of
the virus, and the use of non-contact monitoring methods could ensure the safety of health
care personnel. Therefore, remote, non-contact health monitoring instruments are urgently
needed [1].
As the elderly population continues to rise, the population model has gradually shown
an “inverted pyramid” pattern. In view of the situation where there is no one to take care
of the elderly living alone at home, monitoring the elderly and their daily routines will
become particularly important [
2
]. The elderly population needs care, and their living
conditions require real-time monitoring using professional equipment.
Therefore, there is a great social value to study indoor safety monitoring terminals
that can detect the daily activity routines of the elderly and ensure the daily safety of the
elderly living alone [3].
Millimeter wave is a new type of non-contact life signal detection method, which
can detect the relevant life parameter signals produced by the human body due to heart
and lung activities [
4
]. Compared to traditional ECG and pulse detectors, it has the
Sensors 2021,21, 7893. https://doi.org/10.3390/s21237893 https://www.mdpi.com/journal/sensors

Sensors 2021,21, 7893 2 of 18
characteristics of non-contact, and at the same time has a certain penetration ability, so it
can be detected through obstacles such as clothing and quilts [
5
]. Compared to non-contact
sensors of other systems such as infrared and laser, millimeter wave has the characteristics
of low environmental interference, so it can be used in a domestic environment without
general electromagnetic interference [
6
]. Due to its high frequency and small antenna size,
millimeter wave sensors can construct a compact and cost-effective detection system [7].
With the above advantages, millimeter wave technology has huge application potential
in medical diagnosis, health monitoring, disaster rescue, and other fields, and therefore
has important research value and significance [8].
As early as the early 1970s, people had begun to study non-contact life detection
technology. In 1971, Caro and Bloice began to study asphyxia using microwave measure-
ments [
3
]. In 1975, Lin et al. developed an X-band detection device [
9
]. The device was
based on single-frequency continuous wave, in which the radar emits electromagnetic
waves and meets the object to form an echo. The receiver received the radar echo and
demodulated the echo signal to obtain phase and frequency related information. Then,
the signal processing algorithm was used to extract the relevant components of human
heart and lung signals, so as to test the target’s physical signs [
10
]. Since the 21st century,
with the development of microwave radio frequency and semiconductor technology, radar
detection technology has received great attention in the field of physiological detection. In
2004, the Droitcour research team in the United States designed a biologic radar that could
accurately detect cardiopulmonary information abnormalities within a 2m range [
11
]. Li’s
research team at the University of Florida developed a c-band portable vital signs detector
in 2007, with a transmission power of only 201xW and a detection accuracy of 80% within
a distance of 2.8 m [
12
]. In 2009, 5.8 ghz continuous wave radar was developed, which
can detect infant signs within 1.15 m [
13
]. In 2010, the company developed a 5.8 ghz radar
chip with a 0.13 micron CMOS process and an adjustable bandwidth of over 1GHz. In
2008, Zito’s research team from Italy carried out research on non-contact detection devices
based on ultra-wideband radar and successfully developed a set of cardiopulmonary signal
detection devices, which passed the test in 2011. The accuracy of data obtained was similar
to that of traditional physiological parameter detectors [
14
]. In 2009, Gupta’s team in Italy
successfully developed a non-contact physiological parameter detection device based on
a frequency modulation UWB radar, and in 2011, heart rate detection was successfully
implemented. In 2015, RenL et al., using the frequency step continuous wave (Stepped
Frequency Continuous Wave SFCW) radar, detected human body signs under different
angles of breathing heartbeat detection accuracy [
15
]. Due to the interference of respira-
tory harmonics, environmental noise and the jitter of the target itself, the accuracy of the
early non-contact sign detection is low. With the gradual development of research, some
new methods have been proposed. In 2010, Khan et al. proposed a method to suppress
respiratory harmonics using the MTD method. In 2017, the team also used Kalman filter
and n-iteration method to effectively improve the stability and accuracy of heartbeat fre-
quency detection. In 2011, Tariq et al. used wavelet transform to accurately extract breath
signals [
16
]. In 2014, Nguyen et al. extracted heartbeat signals by using the periodicity
of frequency distribution of respiratory harmonics without suppressing respiratory har-
monics [
17
]. In 2016, Ren et al. demodulated the received signal through complex signal
demodulation (CSD) and arctangent demodulation (AD). Then, they used the state-space
model to obtain vital signs information from the signal phase information [18].
In the early days of the People’s Republic of China, there were few reports on the
detection of vital signs by radar sensors, and application direction focused on earthquake
and mine search and rescue. The fourth Military Medical University was the first to study
biological radar technology, and the research team began the exploration of biological
radar vital signs detection in 1998. In 2004, UWB based biologic radar was developed, and
“Radar sign Detector” was developed for penetrating wall detection. In 2009, the Chinese
Academy of Sciences developed the vital signs detection system using UWB radar, which
can obtain relatively accurate signs in terms of detection algorithm. In 2014, Wang et al.

Sensors 2021,21, 7893 3 of 18
used differential and cross multiply (DACM) algorithm to effectively solve the problem
of arctangent demodulation phase interruption, thus improving the stability of physical
sign detection. In 2015, Wu et al. proposed a spectrum-weighted accumulative method to
improve the SNR of received signals. In 2018, Liang et al. proposed a vital sign monitoring
system based on UWB radar, which utilizes the short-time Fourier Transform (STFT),
Additionally, through the collection of empirical mode decomposition (EEMD Ensemble
Empirical Mode Decomposition) spectrum to detect vital signs parameters, the method to
solve the disadvantages of the EMD method, improved the detection accuracy [18].
This paper mainly studies the detection and separation methods of human breathing
and heartbeat signals under the FM continuous-wave system [19].
The software and hardware design of the millimeter wave sensor system was mainly
completed, and the FIR digital filter and the wavelet transform method were used to detect
and separate breathing and heartbeat signals. On this basis, a method of suppressing
respiratory harmonics based on adaptive filters was proposed for the interference of
respiratory harmonics in the heartbeat signal. Through the transplantation of algorithms
and the construction of software systems, we used cloud services to realize remote online
calculation of vital signs parameters. With the smaller size of the radar module, vital signs
monitoring can be realized without touch.
2. Materials and Methods
2.1. Theoretical Basis of Vital Parameter Detection Subsection
Breathing can cause chest wall displacement; the displacement was about 1~12 mm,
and the breathing frequency range was 0.1~0.5 Hz; the heartbeat can cause the chest wall
displacement to change 1~2 mm, and the heartbeat frequency range was 0.8~2.5 Hz [
20
]
(see Table 1).
Table 1. Model of human life parameters [20–22].
Vital Signs Parameters Amplitude Frequency
Breathing signal 1–12 mm 0.1–0.5 Hz
Heartbeat signal 1–2 mm 0.8–2.5 Hz
Breathing and heartbeat movements can cause tiny vibrations in the chest wall, and
millimeter wave radar is able to detect this tiny displacement through the phase change
of the signal [
21
]. So it can be used for predicting respiratory rate and heart rate [
20
] (see
Table 1).
Traditional medical testing methods used thoracic cavity palpation and observation
methods to detect breathing, but subjects may intentionally change their breathing rate and
pattern when they perceive the measurement. Therefore, the use of non-contact methods
to measure breathing frequency has great practical value [22].
2.2. Millimeter Wave Ranging Principle
Millimeter wave is a special class of radar technology that uses short-wavelength
electromagnetic waves. The electromagnetic wave signal emitted by the radar system
is reflected by other objects in the emission path, and by capturing the reflected signal,
the radar system can determine information such as the distance, speed, and angle of the
object. Since the target echo distance information of the LFMCW system radar can be
simply processed using the Fast Fourier Transform (FFT). This was the most widely used
modulation method, and has been used for the most in-depth research on chirp continuous
wave radar.
The LFMCW radar system emits linear FM pulse signals and captures the signals
reflected by objects in its emission path. Figure 1is a simplified block diagram of the
LFMCW radar system. The system working principle was as follows: first the signal source
generated a linear FM pulse which was transmitted by the transmitting antenna; the object

Sensors 2021,21, 7893 4 of 18
reflected and modulated a reflected linear FM pulse, captured by the receiving antenna;
the mixer combined the transmit signal and the received signal together to generate an
intermediate frequency (IF) signal [16].
Sensors 2021, 21, x FOR PEER REVIEW 4 of 18
LFMCW radar system. The system working principle was as follows: first the signal
source generated a linear FM pulse which was transmitted by the transmitting antenna;
the object reflected and modulated a reflected linear FM pulse, captured by the receiving
antenna; the mixer combined the transmit signal and the received signal together to gen-
erate an intermediate frequency (IF) signal [16].
Synt h
TX ant
RX ant
LP
Filter ADC FFT
Signal Processing
IF Signal
RF Analog Digital
Figure 1. A simplified block diagram of the FMCW radar system [5–9].
The “mixer” was used to mix the receiving end (RX) and transmitting end (TX) sig-
nals to generate an intermediate frequency (IF) signal. The output of the mixer contained
two kinds of signals, which were the sum and the difference between the Rx and Tx chirp
frequencies. There was also a low-pass filter used to limit the signal, allowing only signals
with a difference in frequency to pass.
The role of the mixer was to combine two input signals into a signal with a new fre-
quency for two sinusoidal input signals 𝑥 and 𝑥：
𝑥=sin(𝜔
𝑡+∅
) (1)
𝑥=sin(𝜔
𝑡+∅
) (2)
The instantaneous frequency of the output signal 𝑥 was the difference of the in-
stantaneous frequency of the two input signals, and the phase is equal to the difference of
the phase of the two input signals:
𝑥 =sin[(𝜔
−𝜔
)𝑡 + (∅−∅
)] (3)
In the FMCW radar system, the frequency of the emission signal increases linearly
over time, and this type of signal was also known as the linear FM pulse signal. Figure 2
was a function of the amplitude of the linear FM pulse varying over time.
Figure 1. A simplified block diagram of the FMCW radar system [5–9].
The “mixer” was used to mix the receiving end (RX) and transmitting end (TX) signals
to generate an intermediate frequency (IF) signal. The output of the mixer contained two
kinds of signals, which were the sum and the difference between the Rx and Tx chirp
frequencies. There was also a low-pass filter used to limit the signal, allowing only signals
with a difference in frequency to pass.
The role of the mixer was to combine two input signals into a signal with a new
frequency for two sinusoidal input signals x1and x2:
x1=sin(ω1t+∅1)(1)
x2=sin(ω2t+∅2)(2)
The instantaneous frequency of the output signal
xout
was the difference of the instan-
taneous frequency of the two input signals, and the phase is equal to the difference of the
phase of the two input signals:
xout =sin[(ω2−ω1)t+(∅2−∅1)] (3)
In the FMCW radar system, the frequency of the emission signal increases linearly
over time, and this type of signal was also known as the linear FM pulse signal. Figure 2
was a function of the amplitude of the linear FM pulse varying over time.
Figure 2showed the radar waveform used for life signal detection in this paper. The
radar waveform used was the sawtooth wave,
Tc
was the sawtooth wave period, and B was
the frequency modulation bandwidth. Within a sawtooth wave period, the radar signal
emitted could be expressed as:
STx (t)=expj2πfct+π γt2+ϕ (4)
In Formulas (2)–(4),
fc
was the center frequency of the radar transmitted signal,
γ
was
the slope of sawtooth wave, and
ϕ
was the initial phase of the transmitted signal. The
echo reflected after the radar signal irradiated on the chest wall of a human body can be
expressed as:
SRx (t)=σSTxt−2R1(τ)
C(5)

Sensors 2021,21, 7893 5 of 18
Sensors 2021, 21, x FOR PEER REVIEW 5 of 18
Time
Fo
Frequency
K
B
Time
Tc
02T
c
TX instantaneous frequency
RX instantaneous frequency
Sweep frequency
Figure 2. Detection of chest wall displacement [6–11].
Figure 2 showed the radar waveform used for life signal detection in this paper. The
radar waveform used was the sawtooth wave,T was the sawtooth wave period, and B
was the frequency modulation bandwidth. Within a sawtooth wave period, the radar sig-
nal emitted could be expressed as:
S(t) = exp(j(2π
f
t+πγt
+φ)) (4)
In Formulas (2)–(4), f was the center frequency of the radar transmitted signal,γ
was the slope of sawtooth wave, and φ was the initial phase of the transmitted signal.
The echo reflected after the radar signal irradiated on the chest wall of a human body can
be expressed as:
S(t) = σS (t − 2R(τ)
C) (5)
In Formulas (2)–(5), 𝐶 represented the speed of light and 𝜎 was the echo signal am-
plitude, a value related to the distance from the target to the radar and the radar reflection
cross-sectional area (RCS), which was inversely proportional to the distance from the tar-
get to the radar and inversely proportional to the RCS. The RCS value of radar was related
to the shape, size, material and other factors of the measured target.2𝑅(𝜏)/C is the delay
of radar echo signal, the mathematical expression of 𝑅(𝜏) is:
𝑅(𝜏) = 𝑑+𝑅(𝜏) (6)
In Formulas (2)–(6), 𝑑 was the distance from the radar antenna to the target pleural
motion center, and 𝜏 was the slow time increasing with the repetitions of the sawtooth
wave. Radar echo signal was mixed with local oscillator signal to obtain an intermediate
frequency signal:
𝑆(𝑡)=𝑆
(𝑡)×𝑆

∗(𝑡)
=𝜎 𝑒𝑥𝑝(
𝑗
(4𝜋𝛾𝑅(𝜏)𝑡
𝐶+4𝜋
𝑓
𝑅(𝜏)
𝐶−4𝜋𝛾𝑅
(𝜏)
𝐶))
(7)
𝑓
=()

(8)
Figure 2. Detection of chest wall displacement [6–11].
In Formulas (2)–(5),
C
represented the speed of light and
σ
was the echo signal
amplitude, a value related to the distance from the target to the radar and the radar
reflection cross-sectional area (RCS), which was inversely proportional to the distance from
the target to the radar and inversely proportional to the RCS. The RCS value of radar was
related to the shape, size, material and other factors of the measured target. 2
R1(τ)
/Cis
the delay of radar echo signal, the mathematical expression of R1(τ)is:
R1(τ)=d0+R(τ)(6)
In Formulas (2)–(6),
d0
was the distance from the radar antenna to the target pleural
motion center, and
τ
was the slow time increasing with the repetitions of the sawtooth
wave. Radar echo signal was mixed with local oscillator signal to obtain an intermediate
frequency signal:
SIF (t)=ST x (t)×S∗
Rx (t)
=σex p(j(4πγ R1(τ)t
C+4πfcR1(τ)
C−4πγR2
1(τ)
C2)) (7)
fb=2γR1(τ)
C(8)
ϕb=4πfcR1(τ)
C−4πγR2
1(τ)
C2(9)
According to Formulas (2)–(8), the frequency of the intermediate frequency signal
was
fb
, and the phase was
ϕb
. Since the value of 4
πγR2
1(τ)C2
, 4
πγR2
1(τ)/C2
, was very
small and can be ignored. Therefore, the phase
ϕb
of the intermediate frequency signal
could be further simplified as
ϕb=4πfcR1(τ)
C(10)
According to Equations (2)–(8) and (2)–(10), it could be seen that both frequency
fb
and
phase
ϕb
of radar intermediate frequency signal
SIF (t)
contain displacement information
R1(τ)
of detecting target chest cavity. IF frequency
fb
could be obtained by FFT of radar
IF signal, but the maximum frequency resolution that FFT can achieve at this time was
1
/Tc
. Put into the formula, it can be shown that the maximum range resolution that FFT of

Sensors 2021,21, 7893 6 of 18
radar IF signal can obtain was C/2B, because the chest displacement caused by respiration
and heartbeat was very small. Therefore, it was difficult to obtain the chest displacement
change of the measured target directly through the radar intermediate frequency signal
frequency fb.
Use radar intermediate-frequency signal measured target to get the chest cavity dis-
placement change law of each of the sawtooth sampling points as a row of the matrix, you
can get a matrix (m for sawtooth wave number, n sampling points of each tooth), Vital
signals related to breathing and heartbeat rates can then be obtained.
2.3. Software and Hardware Design for the Millimeter-Wave System
2.3.1. System Framework
The overall framework of the millimeter wave radar hardware is shown in Figure 3.
The system constructed a complete modular circuit based on STM32f103 chip. The circuit
mainly includes:
(1)
Power supply module;
(2)
TI C3220 WIFI communication module;
(3)
IWR6843 mm-wave sensor;
(4)
UART serial port interface for communication with the millimeter wave sensor mod-
ule command system; TI CC3220 WIFI communicates with the millimeter wave sensor
module data transmission interface to obtain raw data of the millimeter wave radar
for back end data processing.
Sensors 2021, 21, x FOR PEER REVIEW 6 of 18
𝜑=4𝜋
𝑓
𝑅(𝜏)
𝐶
−4𝜋𝛾𝑅
(𝜏)
𝐶
 (9)
According to Formulas (2)–(8), the frequency of the intermediate frequency signal
was f , and the phase was φ . Since the value of 4πγR
(τ) ≪ C,4πγR
(τ)/C, was
very small and can be ignored. Therefore, the phase φ of the intermediate frequency
signal could be further simplified as
𝜑=
()
 (10)
According to Equations (2)–(8) and (2)–(10), it could be seen that both frequency f
and phase φ of radar intermediate frequency signal S(t) contain displacement infor-
mation R(τ) of detecting target chest cavity. IF frequency f could be obtained by FFT
of radar IF signal, but the maximum frequency resolution that FFT can achieve at this time
was 1/T. Put into the formula, it can be shown that the maximum range resolution that
FFT of radar IF signal can obtain was C/2B, because the chest displacement caused by
respiration and heartbeat was very small. Therefore, it was difficult to obtain the chest
displacement change of the measured target directly through the radar intermediate fre-
quency signal frequency f.
Use radar intermediate-frequency signal measured target to get the chest cavity dis-
placement change law of each of the sawtooth sampling points as a row of the matrix, you
can get a matrix (m for sawtooth wave number, n sampling points of each tooth), Vital
signals related to breathing and heartbeat rates can then be obtained.
2.3. Software and Hardware Design for the Millimeter-Wave System
2.3.1. System Framework
The overall framework of the millimeter wave radar hardware is shown in Figure 3.
The system constructed a complete modular circuit based on STM32f103 chip. The circuit
mainly includes:
(1) Power supply module;
(2) TI C3220 WIFI communication module;
(3) IWR6843 mm-wave sensor;
(4) UART serial port interface for communication with the millimeter wave sensor mod-
ule command system; TI CC3220 WIFI communicates with the millimeter wave sen-
sor module data transmission interface to obtain raw data of the millimeter wave
radar for back end data processing.
STM32f103
Power suppl y
TI CC3220 WIFI
Command port
IWR6843
Type-c USB
Button
Data port
Debug Interface
Rada r power
supply chip
Radar antenn a
UART
Transmission and power
supply int erface
Figure 3. Hardware overall structure diagram.
2.3.2. Sending and Receiving Circuit
The transceiver circuit was built based on the transceiver integrated chip IWR6843 of
TI Company. The IWR6843 was an integrated single-chip mmwave sensor based on
FMCW radar technology capable of operation in the 60-GHz to 64-GHz band. It was built
Figure 3. Hardware overall structure diagram.
2.3.2. Sending and Receiving Circuit
The transceiver circuit was built based on the transceiver integrated chip IWR6843 of
TI Company. The IWR6843 was an integrated single-chip mmwave sensor based on FMCW
radar technology capable of operation in the 60-GHz to 64-GHz band. It was built with TI
45 nm RFCMOS process and enabled unprecedented levels of integration in an extremely
small form factor. The IWR6843 was an ideal solution for low power, self-monitored,
ultra-accurate radar systems in the industrial space. It had three transmitting channels and
four receiving channels. The antenna design is shown in Figure 4.
Sensors 2021, 21, x FOR PEER REVIEW 7 of 18
with TI 45 nm RFCMOS process and enabled unprecedented levels of integration in an
extremely small form factor. The IWR6843 was an ideal solution for low power, self-mon-
itored, ultra-accurate radar systems in the industrial space. It had three transmitting chan-
nels and four receiving channels. The antenna design is shown in Figure 4.
receiving antenna transmitting antenna
Figure 4. Antenna design [6–11].
The emission system consists of three parallel emission links, each with an independ-
ent binary phase as well as amplitude control (see Figure 5). The maximum output power
for each emission link was 12 dBm, amplitude noise up to −145 dBc/Hz.
Quadrature mixing
BB_IP
BB_IN
BB_QP
BB_Q
NLO1
18
0°
9
0
°
Bandpass fil ter
IF amplificatio n
Mixe r
LO2 Bandpas s fil ter
Power amplification
RFOUT_P
RFOUT_N
Figure 5. Schematic block diagram of the emission pathway [5–9,21].
The receiving system consists of four parallel channels, each including a low noise
amplifier (LNA), mixer, intermediate frequency filter, and ADC (see Figure 6). All four
receiving channels can be run simultaneously. Compared with the traditional mixer, the
orthogonal mixing can effectively inhibit the mirror interference, while dividing the IF
signals into real and virtual parts, thus reducing the ADC sampling rate. The receiver
coefficient of noise was 15 dB, IF gain range of 24~48 dB, step 2dB, IF bandwidth of 5 MHz,
the maximum sampling rate of 12.5 MHz.
Quadrature mixing
VOUT_IP
VOUT_IN
VOUT_QP
VOUT_QN
LO1
18
0°
9
0
°
Bandpa ss filter
IF amplificatio n
Mixer
LO2 LNA
Baseband amplifica tion
RFIN
Figure 6. Block diagram of the receiving pathway principle [5–10,21].
Figure 4. Antenna design [6–11].

Sensors 2021,21, 7893 7 of 18
The emission system consists of three parallel emission links, each with an indepen-
dent binary phase as well as amplitude control (see Figure 5). The maximum output power
for each emission link was 12 dBm, amplitude noise up to −145 dBc/Hz.
Sensors 2021, 21, x FOR PEER REVIEW 7 of 18
with TI 45 nm RFCMOS process and enabled unprecedented levels of integration in an
extremely small form factor. The IWR6843 was an ideal solution for low power, self-mon-
itored, ultra-accurate radar systems in the industrial space. It had three transmitting chan-
nels and four receiving channels. The antenna design is shown in Figure 4.
receiving antenna transmitting antenna
Figure 4. Antenna design [6–11].
The emission system consists of three parallel emission links, each with an independ-
ent binary phase as well as amplitude control (see Figure 5). The maximum output power
for each emission link was 12 dBm, amplitude noise up to −145 dBc/Hz.
Quadrature mixing
BB_IP
BB_IN
BB_QP
BB_Q
NLO1
18
0°
9
0
°
Bandpa ss fil ter
IF amplificatio n
Mixe r
LO2 Bandpass fil ter
Power amplification
RFOUT_P
RFOUT_N
Figure 5. Schematic block diagram of the emission pathway [5–9,21].
The receiving system consists of four parallel channels, each including a low noise
amplifier (LNA), mixer, intermediate frequency filter, and ADC (see Figure 6). All four
receiving channels can be run simultaneously. Compared with the traditional mixer, the
orthogonal mixing can effectively inhibit the mirror interference, while dividing the IF
signals into real and virtual parts, thus reducing the ADC sampling rate. The receiver
coefficient of noise was 15 dB, IF gain range of 24~48 dB, step 2dB, IF bandwidth of 5 MHz,
the maximum sampling rate of 12.5 MHz.
Quadrature mixing
VOUT_IP
VOUT_IN
VOUT_QP
VOUT_QN
LO1
18
0°
9
0
°
Bandpa ss fil ter
IF amplific ation
Mixer
LO2 LNA
Baseband amplification
RFIN
Figure 6. Block diagram of the receiving pathway principle [5–10,21].
Figure 5. Schematic block diagram of the emission pathway [5–9,21].
The receiving system consists of four parallel channels, each including a low noise
amplifier (LNA), mixer, intermediate frequency filter, and ADC (see Figure 6). All four
receiving channels can be run simultaneously. Compared with the traditional mixer, the
orthogonal mixing can effectively inhibit the mirror interference, while dividing the IF
signals into real and virtual parts, thus reducing the ADC sampling rate. The receiver
coefficient of noise was 15 dB, IF gain range of 24~48 dB, step 2dB, IF bandwidth of 5 MHz,
the maximum sampling rate of 12.5 MHz.
Sensors 2021, 21, x FOR PEER REVIEW 7 of 18
with TI 45 nm RFCMOS process and enabled unprecedented levels of integration in an
extremely small form factor. The IWR6843 was an ideal solution for low power, self-mon-
itored, ultra-accurate radar systems in the industrial space. It had three transmitting chan-
nels and four receiving channels. The antenna design is shown in Figure 4.
receiving antenna transmitting antenna
Figure 4. Antenna design [6–11].
The emission system consists of three parallel emission links, each with an independ-
ent binary phase as well as amplitude control (see Figure 5). The maximum output power
for each emission link was 12 dBm, amplitude noise up to −145 dBc/Hz.
Quadrature mixing
BB_IP
BB_IN
BB_QP
BB_Q
NLO1
18
0°
9
0
°
Bandpass fil ter
IF amplificatio n
Mixe r
LO2 Bandpas s fil ter
Power amplification
RFOUT_P
RFOUT_N
Figure 5. Schematic block diagram of the emission pathway [5–9,21].
The receiving system consists of four parallel channels, each including a low noise
amplifier (LNA), mixer, intermediate frequency filter, and ADC (see Figure 6). All four
receiving channels can be run simultaneously. Compared with the traditional mixer, the
orthogonal mixing can effectively inhibit the mirror interference, while dividing the IF
signals into real and virtual parts, thus reducing the ADC sampling rate. The receiver
coefficient of noise was 15 dB, IF gain range of 24~48 dB, step 2dB, IF bandwidth of 5 MHz,
the maximum sampling rate of 12.5 MHz.
Quadrature mixing
VOUT_IP
VOUT_IN
VOUT_QP
VOUT_QN
LO1
18
0°
9
0
°
Bandpa ss filter
IF amplificatio n
Mixer
LO2 LNA
Baseband amplifica tion
RFIN
Figure 6. Block diagram of the receiving pathway principle [5–10,21].
Figure 6. Block diagram of the receiving pathway principle [5–10,21].
The peripheral circuit included power management, external FLASH, high-speed
interface, and other peripheral modules. The power management scheme was used to
divide the input voltage into multiple channels by switching power supply, and further
reduce the voltage by cascade low-voltage differential linear regulator (LDO). At the same
time,
π
type filter was added between different levels to reduce noise. Finally, four outputs
were formed, corresponding to the IO voltage of the main chip, the analog power supply
voltage, the RF transceiver voltage and the core and SRAM voltage.
2.3.3. Manufacturing the Hardware of the Radar System
We customized the radar core board from Changsha Ruigan Company, which provided
the radar core board with customized interface based on our needs. We designed and
welded the WIFI data board of the radar by ourselves, and there is no similar sample on
the market. The final data transmission circuit board based on WIFI communication is
shown in Figures 7and 8.

Sensors 2021,21, 7893 8 of 18
Sensors 2021, 21, x FOR PEER REVIEW 8 of 18
The peripheral circuit included power management, external FLASH, high-speed in-
terface, and other peripheral modules. The power management scheme was used to di-
vide the input voltage into multiple channels by switching power supply, and further re-
duce the voltage by cascade low-voltage differential linear regulator (LDO). At the same
time, π type filter was added between different levels to reduce noise. Finally, four out-
puts were formed, corresponding to the IO voltage of the main chip, the analog power
supply voltage, the RF transceiver voltage and the core and SRAM voltage.
2.3.3. Manufacturing the Hardware of the Radar System
We customized the radar core board from Changsha Ruigan Company, which pro-
vided the radar core board with customized interface based on our needs. We designed
and welded the WIFI data board of the radar by ourselves, and there is no similar sample
on the market. The final data transmission circuit board based on WIFI communication is
shown in Figures 7 and 8.
Figure 7. The schematic diagram of the WIFI communication circuit.
Figure 7 is the schematic diagram of the circuit, the WIFI circuit board and the radar
board were connected by connecting pins, on which power supply and UART serial port
transmission were integrated.
Transmission and power
supply interface
Type-c interface USR C322 WIFI module
Figure 8. WiFi radar interface circuit.
After we connect the core board and the Wi-Fi communication board we designed,
the overall WIFI communication-based millimeter wave radar vital signs monitoring sys-
tem is shown in Figure 9. The size of the whole hardware system was 34.0 × 32.0 × 10 mm.
Figure 7. The schematic diagram of the WIFI communication circuit.
Sensors 2021, 21, x FOR PEER REVIEW 8 of 18
The peripheral circuit included power management, external FLASH, high-speed in-
terface, and other peripheral modules. The power management scheme was used to di-
vide the input voltage into multiple channels by switching power supply, and further re-
duce the voltage by cascade low-voltage differential linear regulator (LDO). At the same
time, π type filter was added between different levels to reduce noise. Finally, four out-
puts were formed, corresponding to the IO voltage of the main chip, the analog power
supply voltage, the RF transceiver voltage and the core and SRAM voltage.
2.3.3. Manufacturing the Hardware of the Radar System
We customized the radar core board from Changsha Ruigan Company, which pro-
vided the radar core board with customized interface based on our needs. We designed
and welded the WIFI data board of the radar by ourselves, and there is no similar sample
on the market. The final data transmission circuit board based on WIFI communication is
shown in Figures 7 and 8.
Figure 7. The schematic diagram of the WIFI communication circuit.
Figure 7 is the schematic diagram of the circuit, the WIFI circuit board and the radar
board were connected by connecting pins, on which power supply and UART serial port
transmission were integrated.
Transmission and power
supply interface
Type-c interface USR C322 WIFI module
Figure 8. WiFi radar interface circuit.
After we connect the core board and the Wi-Fi communication board we designed,
the overall WIFI communication-based millimeter wave radar vital signs monitoring sys-
tem is shown in Figure 9. The size of the whole hardware system was 34.0 × 32.0 × 10 mm.
Figure 8. WiFi radar interface circuit.
Figure 7is the schematic diagram of the circuit, the WIFI circuit board and the radar
board were connected by connecting pins, on which power supply and UART serial port
transmission were integrated.
After we connect the core board and the Wi-Fi communication board we designed, the
overall WIFI communication-based millimeter wave radar vital signs monitoring system is
shown in Figure 9. The size of the whole hardware system was 34.0 ×32.0 ×10 mm.
Sensors 2021, 21, x FOR PEER REVIEW 9 of 18
Radar
board
WIFI board
Transmission
and power
supply interface
Figure 9. The Overall physical diagram of the hardware.
The hardware mainly includes the core board and the WIFI data communication
board. Firmware is an embedded program running on a circuit board, not software for
algorithm calculations, so we generally treat firmware as part of the hardware. Two dif-
ferent hardware will have two different firmwares, the main contributions we made in
the design and production are shown in Table 2.
Table 2. The main contributions we made in the hardware and firmware.
System Composition Hardware Design and Manufactur-
ing Firmware of Board
The core board Customized board from other com-
pany Compile, debug, burn
The WIFI communication
board Design and manufacture Debug, carry through
From the above table, we mainly designed and produced the WIFI communication
board, and the firmware of the two boards in the system was debugged, modified, and
finalized by us.
2.3.4. RF and Digital Front-End Configuration
In the LFMCW system, the frequency of the emission signal varies linearly over time.
This periodic frequency scan was commonly referred to as chirp [23–27], as a linear FM
pulse.
The frame period was set to 50ms, and each frame contained two chirp structures per
frame. During the data processing, the chirp of each frame was sampled, and then the FFT
calculation of the distance dimension extracts the phase information of the distance unit
where the object was being measured, which contains the chest wall vibration data, so
reciprocated. Thus, for the original chest wall displacement data, the sampling rate was
associated with the frame period, and the sampling rate of the original phase data was 20
Hz. In addition, according to the chip data manual, the gain range of the receiver was 24–
48 dB, step 2 dB. Since this paper needs to detect minor shifts and has a low SNR, the
intermediate frequency reception gain was set to a maximum of 48 dB. A schematic dia-
gram of the emission waveform after the configuration is shown in Figure 10.
Figure 9. The Overall physical diagram of the hardware.

Sensors 2021,21, 7893 9 of 18
The hardware mainly includes the core board and the WIFI data communication
board. Firmware is an embedded program running on a circuit board, not software for
algorithm calculations, so we generally treat firmware as part of the hardware. Two
different hardware will have two different firmwares, the main contributions we made in
the design and production are shown in Table 2.
Table 2. The main contributions we made in the hardware and firmware.
System Composition Hardware Design and
Manufacturing Firmware of Board
The core board Customized board from other
company Compile, debug, burn
The WIFI communication
board Design and manufacture Debug, carry through
From the above table, we mainly designed and produced the WIFI communication
board, and the firmware of the two boards in the system was debugged, modified, and
finalized by us.
2.3.4. RF and Digital Front-End Configuration
In the LFMCW system, the frequency of the emission signal varies linearly over time.
This periodic frequency scan was commonly referred to as chirp [
23
–
27
], as a linear FM
pulse.
The frame period was set to 50ms, and each frame contained two chirp structures per
frame. During the data processing, the chirp of each frame was sampled, and then the
FFT calculation of the distance dimension extracts the phase information of the distance
unit where the object was being measured, which contains the chest wall vibration data,
so reciprocated. Thus, for the original chest wall displacement data, the sampling rate
was associated with the frame period, and the sampling rate of the original phase data
was 20 Hz. In addition, according to the chip data manual, the gain range of the receiver
was 24–48 dB, step 2 dB. Since this paper needs to detect minor shifts and has a low SNR,
the intermediate frequency reception gain was set to a maximum of 48 dB. A schematic
diagram of the emission waveform after the configuration is shown in Figure 10.
Sensors 2021, 21, x FOR PEER REVIEW 10 of 18
Frame 1 Fram e 2 Frame 3 Frame N
Range-Bins
Frame periodicity=50ms
Range-
FFT
Range-
FFT
Range-
FFT
Range-
FFT
Time
Object Range Bin
Extract phase and
unwrap for the
object range bin
Further
processing
Figure 10. Emission waveform diagram [9–11].
Sampling according to each frame cycle in the figure above, first ADC samples the
median frequency signal according to the overall algorithm processing process, then dis-
tance dimension FFT processing, the target distance is calculated, and then a series of sub-
sequent algorithm processing detection and separation of human respiratory and heart-
beat signals [24–27].
2.3.5. The Software Architecture and Design
The overall software workflow is shown in Figure 11, and the software system archi-
tecture based on cloud services is shown in Figure 12.
Initialize chip
clock,
peripherals
and interfaces
Initialize the
RFFE (RF
Front End)
Config ure the
transceiver
channel and
internal ADC
Config ure the
pulse parameters
of the chirp
Config ure
the frame
period
structure
Emit
electroma
gnetic
waves
Collect intermediate
frequency da ta and
perform signal
processing
Transmit data to
the server through
serial port and
wifi mod ule
Figure 11. Software flow chart of millimeter wave radar module.
Our system used a cloud service software architecture. The device did not need to be
directly connected to the computer via a data cable. The data was sent directly to the
server via Wi-Fi, and the server’s program calculated the monitored data in real time by
unpacking and computing the socket data. The data was saved on the server, and the
software obtained and displayed the parameters in real time through POST and GET
methods. The software system architecture diagram based on cloud service is shown in
Figure 12. Our cloud server used Alibaba Cloud’s ECS server, and the server uses Ubuntu
18.04 system. The algorithm was transplanted and packaged into a dynamic link library
of the Linux system (.so file). The netty framework was used on the server side to realize
real-time data unpacking and algorithm calculation and store it in the database. The server
used the RESTFUL framework of node.js, and the computer software obtained the calcu-
lated data through GET and post methods.
Figure 10. Emission waveform diagram [9–11].

Sensors 2021,21, 7893 10 of 18
Sampling according to each frame cycle in the figure above, first ADC samples the
median frequency signal according to the overall algorithm processing process, then
distance dimension FFT processing, the target distance is calculated, and then a series
of subsequent algorithm processing detection and separation of human respiratory and
heartbeat signals [24–27].
2.3.5. The Software Architecture and Design
The overall software workflow is shown in Figure 11, and the software system archi-
tecture based on cloud services is shown in Figure 12.
Sensors 2021, 21, x FOR PEER REVIEW 10 of 18
Frame 1 Fram e 2 Frame 3 Frame N
Range-Bins
Frame periodicity=50ms
Range-
FFT
Range-
FFT
Range-
FFT
Range-
FFT
Time
Object Range Bin
Extract phase and
unwrap for the
object range bin
Further
processing
Figure 10. Emission waveform diagram [9–11].
Sampling according to each frame cycle in the figure above, first ADC samples the
median frequency signal according to the overall algorithm processing process, then dis-
tance dimension FFT processing, the target distance is calculated, and then a series of sub-
sequent algorithm processing detection and separation of human respiratory and heart-
beat signals [24–27].
2.3.5. The Software Architecture and Design
The overall software workflow is shown in Figure 11, and the software system archi-
tecture based on cloud services is shown in Figure 12.
Initialize chip
cloc k,
peripherals
and interfaces
Initialize the
RFFE (RF
Front End)
Config ure the
transceiver
channel and
internal ADC
Config ure the
pulse parameters
of the chirp
Config ure
the frame
period
structure
Emit
electroma
gnetic
waves
Collect in termedi ate
frequ ency da ta and
perform signal
processing
Transmit data to
the server through
serial port and
wifi m odule
Figure 11. Software flow chart of millimeter wave radar module.
Our system used a cloud service software architecture. The device did not need to be
directly connected to the computer via a data cable. The data was sent directly to the
server via Wi-Fi, and the server’s program calculated the monitored data in real time by
unpacking and computing the socket data. The data was saved on the server, and the
software obtained and displayed the parameters in real time through POST and GET
methods. The software system architecture diagram based on cloud service is shown in
Figure 12. Our cloud server used Alibaba Cloud’s ECS server, and the server uses Ubuntu
18.04 system. The algorithm was transplanted and packaged into a dynamic link library
of the Linux system (.so file). The netty framework was used on the server side to realize
real-time data unpacking and algorithm calculation and store it in the database. The server
used the RESTFUL framework of node.js, and the computer software obtained the calcu-
lated data through GET and post methods.
Figure 11. Software flow chart of millimeter wave radar module.
Sensors 2021, 21, x FOR PEER REVIEW 11 of 18
Algorithm program
Algorithm, remove
platform
dependency, add
interface function,
cross compile
DLL library for
debugging
Millimeter wave radar
module equipment
Conta ine rized c luste r
Data verification/
unpacking
Alg orithm Li nux
library .so
Respiration and
heartbeat data streams
transferred to different
algorithm libraries
Data storage using
SQL+ noSQL
RESTful API
Identity verification/
authorization
deadline
Millimeter wave
radar windows
display interface
TCP
X86
Linux
Win
GET
POST
Hetero geneo us al go rithm Cloud computing and storage
Figure 12. Software system architecture diagram based on cloud service.
As shown in Figure 12, the algorithm must be run on a remote server for the system
to implement cloud computing. Since the server system was Linux without an operation
interface, we needed to transplant the algorithm. Considering the data was transmitted to
the server through a socket, the socket framework used by the server was called netty,
which was in java environment, so we need to package the algorithm of the C++ language.
The C++ language algorithm needed to replace and rewrite the MFP-specific functions,
and then define all functions as “extern ‘C’ int __declspec(dllexport)” so that the pack-
aged. So, the file can be called externally. We needed to transfer the transplanted algo-
rithm file into the computing server environment used and package the C++ language
algorithm into the Linux platform dynamic link library by running the “g++ linuxData-
Calculate.cpp -fPIC -shared -o libDataCal_1.so” command under bash.
After the algorithm was transplanted, it combined the netty framework to realize
real-time unpacking and calculation of the data, and the result was stored in the server.
The display software could obtain real-time data through the RestFUL API.
2.3.6. The PC-Terminal Display Interface Design
The software functions designed based on this algorithm are roughly introduced as
follows:
(1) Control the start, pause, and end of data collection and transmission functions;
(2) Real-time display of time-domain waveforms of breathing and heartbeat signals;
(3) Spectral analysis of breathing and heartbeat signals
(4) Estimate and display the respiratory rate and heart rate numerically.
The PC-terminal display interface design was shown in Figure 13.
Figure 13. The PC-terminal display interface design.
Figure 12. Software system architecture diagram based on cloud service.
Our system used a cloud service software architecture. The device did not need to
be directly connected to the computer via a data cable. The data was sent directly to the
server via Wi-Fi, and the server’s program calculated the monitored data in real time
by unpacking and computing the socket data. The data was saved on the server, and
the software obtained and displayed the parameters in real time through POST and GET
methods. The software system architecture diagram based on cloud service is shown
in Figure 12. Our cloud server used Alibaba Cloud’s ECS server, and the server uses
Ubuntu 18.04 system. The algorithm was transplanted and packaged into a dynamic link
library of the Linux system (.so file). The netty framework was used on the server side to
realize real-time data unpacking and algorithm calculation and store it in the database. The
server used the RESTFUL framework of node.js, and the computer software obtained the
calculated data through GET and post methods.

Sensors 2021,21, 7893 11 of 18
As shown in Figure 12, the algorithm must be run on a remote server for the system
to implement cloud computing. Since the server system was Linux without an operation
interface, we needed to transplant the algorithm. Considering the data was transmitted
to the server through a socket, the socket framework used by the server was called netty,
which was in java environment, so we need to package the algorithm of the C++ language.
The C++ language algorithm needed to replace and rewrite the MFP-specific functions,
and then define all functions as “extern ‘C’ int __declspec(dllexport)” so that the packaged.
So, the file can be called externally. We needed to transfer the transplanted algorithm file
into the computing server environment used and package the C++ language algorithm
into the Linux platform dynamic link library by running the “g++ linuxDataCalculate.cpp
-fPIC -shared -o libDataCal_1.so” command under bash.
After the algorithm was transplanted, it combined the netty framework to realize
real-time unpacking and calculation of the data, and the result was stored in the server.
The display software could obtain real-time data through the RestFUL API.
2.3.6. The PC-Terminal Display Interface Design
The software functions designed based on this algorithm are roughly introduced as
follows:
(1)
Control the start, pause, and end of data collection and transmission functions;
(2)
Real-time display of time-domain waveforms of breathing and heartbeat signals;
(3)
Spectral analysis of breathing and heartbeat signals
(4)
Estimate and display the respiratory rate and heart rate numerically.
The PC-terminal display interface design was shown in Figure 13.
Sensors 2021, 21, x FOR PEER REVIEW 11 of 18
Algorithm program
Algorithm, remove
platform
dependency, add
interface function,
cross compile
DLL library for
debugging
Millimeter wave radar
module equipment
Conta ineriz ed cluster
Data verification/
unpacking
Alg orithm Linux
library .so
Respiration and
heartbeat data streams
transferred to different
algorithm libraries
Data storage using
SQL+ noSQL
RESTful API
Identity verification/
authorization
deadline
Millimeter wave
radar windows
display interface
TCP
X86
Linux
Win
GET
POST
Hetero geneous algorithm Cloud computing and storage
Figure 12. Software system architecture diagram based on cloud service.
As shown in Figure 12, the algorithm must be run on a remote server for the system
to implement cloud computing. Since the server system was Linux without an operation
interface, we needed to transplant the algorithm. Considering the data was transmitted to
the server through a socket, the socket framework used by the server was called netty,
which was in java environment, so we need to package the algorithm of the C++ language.
The C++ language algorithm needed to replace and rewrite the MFP-specific functions,
and then define all functions as “extern ‘C’ int __declspec(dllexport)” so that the pack-
aged. So, the file can be called externally. We needed to transfer the transplanted algo-
rithm file into the computing server environment used and package the C++ language
algorithm into the Linux platform dynamic link library by running the “g++ linuxData-
Calculate.cpp -fPIC -shared -o libDataCal_1.so” command under bash.
After the algorithm was transplanted, it combined the netty framework to realize
real-time unpacking and calculation of the data, and the result was stored in the server.
The display software could obtain real-time data through the RestFUL API.
2.3.6. The PC-Terminal Display Interface Design
The software functions designed based on this algorithm are roughly introduced as
follows:
(1) Control the start, pause, and end of data collection and transmission functions;
(2) Real-time display of time-domain waveforms of breathing and heartbeat signals;
(3) Spectral analysis of breathing and heartbeat signals
(4) Estimate and display the respiratory rate and heart rate numerically.
The PC-terminal display interface design was shown in Figure 13.
Figure 13. The PC-terminal display interface design.
Figure 13. The PC-terminal display interface design.
In Section 2.4, the hardware and software design of the 60 GHz millimeter wave sensor
was completed. The transceiver circuit was built based on the TI transceiver integrated
chip IWR6843, and the transceiver system was composed of antennas. Design of peripheral
circuits, including power supply, high-speed data interface, etc., constitutes a complete
system. Then, the software configuration framework was designed according to the
communication protocol format of the chip, and the RF and digital front-end configuration
was carried out. The LFMCW sawtooth wave transmitting waveform is created, and the
ADC sampling mode is configured to sample the front-end data, which makes preliminary
preparation for the subsequent signal processing.
2.4. Physiological Parameter Detection Algorithm of Millimeter Wave Radar
Figure 14 shows the systematic process of the detection of the human heartbeat and
respiratory signals. According to the whole algorithm processing process, the middle
frequency signal was first sampled by ADC, and then the range dimension FFT processing
to calculate the target distance. Next, FFT calculation was performed for each chirp to find

Sensors 2021,21, 7893 12 of 18
the distance unit corresponding to the target. Then, the phase information corresponding
to the distance unit was extracted for subsequent algorithm processing.
Sensors 2021, 21, x FOR PEER REVIEW 12 of 18
In Section 2.4, the hardware and software design of the 60 GHz millimeter wave sen-
sor was completed. The transceiver circuit was built based on the TI transceiver integrated
chip IWR6843, and the transceiver system was composed of antennas. Design of periph-
eral circuits, including power supply, high-speed data interface, etc., constitutes a com-
plete system. Then, the software configuration framework was designed according to the
communication protocol format of the chip, and the RF and digital front-end configura-
tion was carried out. The LFMCW sawtooth wave transmitting waveform is created, and
the ADC sampling mode is configured to sample the front-end data, which makes prelim-
inary preparation for the subsequent signal processing.
2.4. Physiological Parameter Detection Algorithm of Millimeter Wave Radar
Figure 14 shows the systematic process of the detection of the human heartbeat and
respiratory signals. According to the whole algorithm processing process, the middle fre-
quency signal was first sampled by ADC, and then the range dimension FFT processing
to calculate the target distance. Next, FFT calculation was performed for each chirp to find
the distance unit corresponding to the target. Then, the phase information corresponding
to the distance unit was extracted for subsequent algorithm processing.
Range FFT
Phas e
Extract ion
Phas e
Unwrapping
0.1Hz to 0.6Hz
Filt er
0.8Hz to 4.0Hz
Filt er
Peak Interval
Esti mat ion
Peak Interval
Esti mat ion
Breathing Rate
Heart Rate
Figure 14. The two band pass filters were set to 0.1–0.6 Hz and 0.8–4.0 Hz corresponding to the
respiratory and heartbeat bands, respectively, where the respiratory filter start frequency was 0.1 H
z to filter out the interference of DC noise [9–12].
(1) The two band pass filters were set to 0.1–0.6 Hz and 0.8–4.0 Hz corresponding to the
respiratory and heartbeat bands, respectively, where the respiratory filter start fre-
quency was 0.1 H z to filter out the interference of DC noise;
(2) Filters were designed using the fir1 function of order 41 and the window function
selected a Hamming window;
(3) The raw data was filtered using the filter function.
The FIR filter can effectively isolate the breathing signal. Although it has some noise
cancellation effect on the heartbeat signal, the heartbeat frequency information cannot be
extracted from it, so the chest wall displacement signal will be processed by wavelet trans-
form to isolate the heartbeat signal. Wavelet analysis was performed using a series of
functions in MATLAB's wavelet transformation toolkit, and the overall procedure was as
follows:
(1) The appropriate wavelet basis function and decomposition layers are selected and
one-dimensional discrete wavelet decomposition of the signal using the wavedec
function;
(2) Use approximate coefficient and detail coefficient to extract the decomposition
though appcoef and detcoef functions;
(3) The waverec function was reconstructed using the extracted approximation and de-
tail coefficients to obtain the component sizes corresponding to the signal at different
frequencies.
Section 2.4 briefly describes the signal detection and separation methods of human
heartbeat and breathing. Section 3 will further verify the accuracy and stability of the sys-
tem, and we conducted comparative experiments with traditional contact devices.
Figure 14.
The two band pass filters were set to 0.1–0.6 Hz and 0.8–4.0 Hz corresponding to the
respiratory and heartbeat bands, respectively, where the respiratory filter start frequency was 0.1 H z
to filter out the interference of DC noise [9–12].
(1)
The two band pass filters were set to 0.1–0.6 Hz and 0.8–4.0 Hz corresponding to
the respiratory and heartbeat bands, respectively, where the respiratory filter start
frequency was 0.1 H z to filter out the interference of DC noise;
(2)
Filters were designed using the fir1 function of order 41 and the window function
selected a Hamming window;
(3)
The raw data was filtered using the filter function.
The FIR filter can effectively isolate the breathing signal. Although it has some noise
cancellation effect on the heartbeat signal, the heartbeat frequency information cannot
be extracted from it, so the chest wall displacement signal will be processed by wavelet
transform to isolate the heartbeat signal. Wavelet analysis was performed using a series of
functions in MATLAB’s wavelet transformation toolkit, and the overall procedure was as
follows:
(1)
The appropriate wavelet basis function and decomposition layers are selected and
one-dimensional discrete wavelet decomposition of the signal using the wavedec
function;
(2) Use approximate coefficient and detail coefficient to extract the decomposition though
appcoef and detcoef functions;
(3) The waverec function was reconstructed using the extracted approximation and detail
coefficients to obtain the component sizes corresponding to the signal at different
frequencies.
Section 2.4 briefly describes the signal detection and separation methods of human
heartbeat and breathing. Section 3will further verify the accuracy and stability of the
system, and we conducted comparative experiments with traditional contact devices.
3. Results
3.1. Experimental Scene Setting
This experiment was carried out in a laboratory environment, as shown in Figure 15.
Subjects were two healthy women aged 28 years and 26 years old, measured under normal
respiration. The subjects were within normal detection range, facing the millimeter-wave
sensor and chest facing the sensor. Since this experiment was non-contact detection, the
measured environment and human body’s movement were greatly disturbed, so it should
be kept stationary as far as possible. There should be no metallic object between the sensor
antenna and the subject.

Sensors 2021,21, 7893 13 of 18
Sensors 2021, 21, x FOR PEER REVIEW 13 of 18
3. Results
3.1. Experimental Scene Setting
This experiment was carried out in a laboratory environment, as shown in Figure 15.
Subjects were two healthy women aged 28 years and 26 years old, measured under normal
respiration. The subjects were within normal detection range, facing the millimeter-wave
sensor and chest facing the sensor. Since this experiment was non-contact detection, the
measured environment and human body’s movement were greatly disturbed, so it should
be kept stationary as far as possible. There should be no metallic object between the sensor
antenna and the subject.
mobile power
mmwave radar
USB type-c Power supply
HR and BR
display interface
Figure 15. The millimeter-wave radar remote monitoring system for elderlies living alone based on
WIFI communication.
The debugging program was downloaded to the IWR6843 through the software Uni-
Flash provided by TI. The tester sat in front of the radar and remained as still as possible
during the test. The radar test distance can reach about 2.5 m. Because the single-channel
ECG module of the comparison device limited the measurement distance, the radar test
distance is about 1 m. Figure 16 shows the measurements of the tester while maintaining
normal breathing.
0 5 10 15 20 25 30 35 4
0
-0.04
0.00
0.04
0.08
Amplitude (V)
time
(
s
)
Time(s )
0 5 10 15 20 25 30 35 40
0.0
0.1
0.2
0.3
0.4
0.5
Frequency(Hz)
Time (s)
Time(s)
(a) (b)
0 5 10 15 20 25 30 35 40
-0.5
0.0
0.5
Amplitude (V)
Time (s)
Time(s)
0 5 10 15 20 25 30 35 40
0.0
0.5
1.0
1.5
2.0
Frequency(Hz)
time
(
s
)
Time(s)
(c) (d)
Figure 16. The experimental data. (a) Respiratory amplitude diagram, (b) Respiratory frequency domain analysis, (c)
Heartbeat amplitude diagram, (d) Heartbeat frequency domain Value Hz.
3.2. Experimental Comparison with Medical Grade Single-Lead ECG Equipment
The previous article completed the separation of breathing and heartbeat signals and
studied the influence of their harmonics. The relevant adaptive filtering algorithm has
been proposed for simulation and experimental verification. Furthermore, to verify the
Figure 15.
The millimeter-wave radar remote monitoring system for elderlies living alone based on
WIFI communication.
The debugging program was downloaded to the IWR6843 through the software
UniFlash provided by TI. The tester sat in front of the radar and remained as still as
possible during the test. The radar test distance can reach about 2.5 m. Because the single-
channel ECG module of the comparison device limited the measurement distance, the
radar test distance is about 1 m. Figure 16 shows the measurements of the tester while
maintaining normal breathing.
Sensors 2021, 21, x FOR PEER REVIEW 13 of 18
3. Results
3.1. Experimental Scene Setting
This experiment was carried out in a laboratory environment, as shown in Figure 15.
Subjects were two healthy women aged 28 years and 26 years old, measured under normal
respiration. The subjects were within normal detection range, facing the millimeter-wave
sensor and chest facing the sensor. Since this experiment was non-contact detection, the
measured environment and human body’s movement were greatly disturbed, so it should
be kept stationary as far as possible. There should be no metallic object between the sensor
antenna and the subject.
mobile power
mmwave radar
USB type-c Powe r supply
HR and BR
display interface
Figure 15. The millimeter-wave radar remote monitoring system for elderlies living alone based on
WIFI communication.
The debugging program was downloaded to the IWR6843 through the software Uni-
Flash provided by TI. The tester sat in front of the radar and remained as still as possible
during the test. The radar test distance can reach about 2.5 m. Because the single-channel
ECG module of the comparison device limited the measurement distance, the radar test
distance is about 1 m. Figure 16 shows the measurements of the tester while maintaining
normal breathing.
0 5 10 15 20 25 30 35 4
0
-0.04
0.00
0.04
0.08
Amplitude (V)
time
(
s
)
Time(s )
0 5 10 15 20 25 30 35 40
0.0
0.1
0.2
0.3
0.4
0.5
Frequency(Hz)
Time (s)
Time(s)
(a) (b)
0 5 10 15 20 25 30 35 40
-0.5
0.0
0.5
Amplitude (V)
Time (s)
Time(s)
0 5 10 15 20 25 30 35 40
0.0
0.5
1.0
1.5
2.0
Frequency(Hz)
time
(
s
)
Time(s)
(c) (d)
Figure 16. The experimental data. (a) Respiratory amplitude diagram, (b) Respiratory frequency domain analysis, (c)
Heartbeat amplitude diagram, (d) Heartbeat frequency domain Value Hz.
3.2. Experimental Comparison with Medical Grade Single-Lead ECG Equipment
The previous article completed the separation of breathing and heartbeat signals and
studied the influence of their harmonics. The relevant adaptive filtering algorithm has
been proposed for simulation and experimental verification. Furthermore, to verify the
Figure 16.
The experimental data. (
a
) Respiratory amplitude diagram, (
b
) Respiratory frequency domain analysis, (
c
) Heart-
beat amplitude diagram, (d) Heartbeat frequency domain Value Hz.
3.2. Experimental Comparison with Medical Grade Single-Lead ECG Equipment
The previous article completed the separation of breathing and heartbeat signals and
studied the influence of their harmonics. The relevant adaptive filtering algorithm has been
proposed for simulation and experimental verification. Furthermore, to verify the system’s
accuracy and stability, a comparative experiment was conducted with the conventional
contact equipment. The selected contact device was Single channel ECG module. The
investigation adopted the method of control variables to achieve synchronous detection.
Two healthy subjects were measured in the normal breathing state, and two devices,

Sensors 2021,21, 7893 14 of 18
millimeter wave sensor, and single channel ECG module were used for the experiment.
Millimeter wave sensor will be detected by an algorithm based on the adaptive filter. A
complete detection cycle was selected as a set of experimental data. In addition, respiration
rate could not be detected in this experiment, so only heart rate data was compared. First
of all, as shown in Figure 17, the comparison experiment was set. Subjects were asked
to hold the electrodes on both sides of the single channel ECG module with both hands
under normal breathing state to measure heart rate, and at the same time, heart rate was
measured with the millimeter wave sensor.
Sensors 2021, 21, x FOR PEER REVIEW 14 of 18
system’s accuracy and stability, a comparative experiment was conducted with the con-
ventional contact equipment. The selected contact device was Single channel ECG mod-
ule. The investigation adopted the method of control variables to achieve synchronous
detection. Two healthy subjects were measured in the normal breathing state, and two
devices, millimeter wave sensor, and single channel ECG module were used for the ex-
periment. Millimeter wave sensor will be detected by an algorithm based on the adaptive
filter. A complete detection cycle was selected as a set of experimental data. In addition,
respiration rate could not be detected in this experiment, so only heart rate data was com-
pared. First of all, as shown in Figure 17, the comparison experiment was set. Subjects
were asked to hold the electrodes on both sides of the single channel ECG module with
both hands under normal breathing state to measure heart rate, and at the same time,
heart rate was measured with the millimeter wave sensor.
mmwave HR
and BR display
int erfac e
Single lead
ECG patch
displa y inte rface
mmwave
Radar
Single channel
ECG module
Figure 17. Comparative experiment of millimeter wave radar and single channel ECG module.
Then, through the comparative experiment of millimeter wave radar and single chan-
nel ECG module in Figure 18, ECG signal and heartbeat signal were collected simultane-
ously.
Figure 17. Comparative experiment of millimeter wave radar and single channel ECG module.
Then, through the comparative experiment of millimeter wave radar and single
channel ECG module in Figure 18, ECG signal and heartbeat signal were collected simulta-
neously.

Sensors 2021,21, 7893 15 of 18
Sensors 2021, 21, x FOR PEER REVIEW 15 of 18
0 5 10 15 20 25 3
0
-1.6
-0.8
0.0
0.8
1.6
Amplitude (mV)
time
(
s
)
0 5 10 15 20 25 30
-0.8
-0.4
0.0
0.4
0.8
Amplitude (V)
Time (s)
Time(s)
Time(s)
ECG module
Mmwave radar
Figure 18. ECG and millimeter wave signals collected at the same time.
The figure shows the ECG and millimeter wave signals collected by the single lead
ECG equipment. The heart rate calculated by millimeter wave radar was 63, and the heart
rate calculated by medical single channel ECG device was 64.
Continuous collection and testing 60 times of heart rate ratio; two comparative ex-
periments (see Figures 19 and 20):
mmwa ve Rada r
mmwave HR and
BR displa y interface
Single lead ECG
patch display
int erface
0 102030405060
50
60
70
80
90
100
Heartbeat (bpm)
Number of tests
ECG modul e
mmwa ve rada r
Figure 19. Tester 1.the comparative experiment of millimeter wave radar and single channel ECG
module.
Figure 18. ECG and millimeter wave signals collected at the same time.
The figure shows the ECG and millimeter wave signals collected by the single lead
ECG equipment. The heart rate calculated by millimeter wave radar was 63, and the heart
rate calculated by medical single channel ECG device was 64.
Continuous collection and testing 60 times of heart rate ratio; two comparative experi-
ments (see Figures 19 and 20):
Sensors 2021, 21, x FOR PEER REVIEW 15 of 18
0 5 10 15 20 25 3
0
-1.6
-0.8
0.0
0.8
1.6
Amplitude (mV)
time
(
s
)
0 5 10 15 20 25 30
-0.8
-0.4
0.0
0.4
0.8
Amplitude (V)
Time (s)
Time(s)
Time(s)
ECG module
Mmwave radar
Figure 18. ECG and millimeter wave signals collected at the same time.
The figure shows the ECG and millimeter wave signals collected by the single lead
ECG equipment. The heart rate calculated by millimeter wave radar was 63, and the heart
rate calculated by medical single channel ECG device was 64.
Continuous collection and testing 60 times of heart rate ratio; two comparative ex-
periments (see Figures 19 and 20):
mmwa ve Rada r
mmwave HR and
BR displa y interface
Single lead ECG
patch display
int erface
0 102030405060
50
60
70
80
90
100
Heartbeat (bpm)
Number of tests
ECG modul e
mmwa ve rada r
Figure 19. Tester 1.the comparative experiment of millimeter wave radar and single channel ECG
module.
Figure 19.
Tester 1.the comparative experiment of millimeter wave radar and single channel ECG
module.

Sensors 2021,21, 7893 16 of 18
Sensors 2021, 21, x FOR PEER REVIEW 16 of 18
mmwa ve Rada r
mmwave HR and
BR displa y inte rface
Single lead ECG
patch display
interface
0 102030405060
50
60
70
80
90
100
Heartbeat (bpm)
Number of tests
ECG module
mmwave radar
Figure 20. The comparative experiment of millimeter wave radar and single channel ECG module.
We use a medical contact respiratory acquisition device called sense-u to conduct
comparative experiments with millimeter wave radar. We recorded the respiratory rate
values of the two devices every 30 s, collected 30 times, and drew the comparison curve.
The experimental and comparative results are shown in Figure 21.
mmwave
Rader
Power
supply
mmwave HR
and BR display
interface
Sense-U
Breathing
Moni tor
display
interface
0102030
0
10
20
30
Heartbeat (bpm)
Number of tests
Contact breathing monitor
mmwave rader
Figure 21. Comparative experiment with contact breathing monitor.
4. Discussion
As can be seen from the experimental results of tester 1 and tester 2 in FIG. 19 and
FIG. 20, two healthy subjects were measured under normal breathing conditions, and mil-
limeter-wave sensor and single channel ECG module were used for experiments simulta-
neously. The two heart rate data were overlapped and compared, and the error was within
6–7 bpm. You can see that the error fluctuation is slight. In the breathing comparison ex-
periment in Figure 21, there was a certain gap between the contact breathing frequency
Figure 20. The comparative experiment of millimeter wave radar and single channel ECG module.
We use a medical contact respiratory acquisition device called sense-u to conduct
comparative experiments with millimeter wave radar. We recorded the respiratory rate
values of the two devices every 30 s, collected 30 times, and drew the comparison curve.
The experimental and comparative results are shown in Figure 21.
Sensors 2021, 21, x FOR PEER REVIEW 16 of 18
mmwa ve Rada r
mmwave HR and
BR displa y interface
Single lead ECG
patch display
interface
0 102030405060
50
60
70
80
90
100
Heartbeat (bpm)
Number of tests
ECG module
mmwave radar
Figure 20. The comparative experiment of millimeter wave radar and single channel ECG module.
We use a medical contact respiratory acquisition device called sense-u to conduct
comparative experiments with millimeter wave radar. We recorded the respiratory rate
values of the two devices every 30 s, collected 30 times, and drew the comparison curve.
The experimental and comparative results are shown in Figure 21.
mmwave
Rader
Power
supply
mmwave HR
and BR display
interface
Sense-U
Breathing
Moni tor
display
interface
0102030
0
10
20
30
Heartbeat (bpm)
Number of tests
Contact breathing monitor
mmwave rader
Figure 21. Comparative experiment with contact breathing monitor.
4. Discussion
As can be seen from the experimental results of tester 1 and tester 2 in FIG. 19 and
FIG. 20, two healthy subjects were measured under normal breathing conditions, and mil-
limeter-wave sensor and single channel ECG module were used for experiments simulta-
neously. The two heart rate data were overlapped and compared, and the error was within
6–7 bpm. You can see that the error fluctuation is slight. In the breathing comparison ex-
periment in Figure 21, there was a certain gap between the contact breathing frequency
Figure 21. Comparative experiment with contact breathing monitor.
4. Discussion
As can be seen from the experimental results of tester 1 and tester 2 in FIG. 19 and FIG.
20, two healthy subjects were measured under normal breathing conditions, and millimeter-
wave sensor and single channel ECG module were used for experiments simultaneously.
The two heart rate data were overlapped and compared, and the error was within 6–7 bpm.
You can see that the error fluctuation is slight. In the breathing comparison experiment
in Figure 21, there was a certain gap between the contact breathing frequency and the

Sensors 2021,21, 7893 17 of 18
non-contact breathing frequency, but the basic trend was the same. The measurement
results of the millimeter wave sensor based on the adaptive filtering algorithm were close
to those of the Single channel ECG module, which proved the measurement accuracy and
robustness of the system in this paper.
5. Conclusions
This paper designed a millimeter-wave radar remote monitoring system for the
elderly living alone, based on WIFI communication; a real-time and efficient life detection
system. This paper conducted a software and hardware construction of a millimeter-
wave sensor system and detected and separated it using an FIR digital filter and wavelet
transform method for respiratory and heartbeat signals. The design of the system has good
penetrating ability, can pass through obstacles such as clothes and bedding, is small in
size, easy to integrate, and has higher efficiency. At the same time, combined with cloud
computing, explore the possibility of remote vital signs monitoring by millimeter wave
radar.
We hope to use our knowledge and technology and apply it to help disadvantaged
groups in society. The system designed in this paper can realize real-time and efficient
remote monitoring of vital signs (heart rate and breathing rate) of elderlies living alone,
preventing them from having accidents without care or in guardianship loopholes.
Author Contributions:
Conceptualization, H.Y. and K.G.; methodology, C.L.; software, K.G.; val-
idation, S.Z. (Senhao Zhang), S.Z. (Shasha Zhao) and C.L.; formal analysis, C.L.; resources, H.Y.;
data curation, J.L.; writing—original draft preparation, K.G.; writing—review and editing, S.Z. (Sen-
hao Zhang) and C.L.; visualization, K.G.; supervision, H.Y.; project administration, H.Y.; funding
acquisition, H.Y. All authors have read and agreed to the published version of the manuscript.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement:
Informed consent was obtained from all subjects involved in the
study.
Data Availability Statement:
The data presented in this study are available on request from the
corresponding author.
Acknowledgments:
This research was supported in part by the Project “National key R&D Program
of China” (2017YFB1304103).
Conflicts of Interest: The authors declare no conflict of interest.
References
1.
Zhao, H.; Hong, H.; Li, S.; Li, Y.; Zhu, X. Noncontact Physiological Dynamics Detection Using Low-power Digital-IF Doppler
Radar. IEEE Trans. Instrum. Meas. 2017,66, 1780–1788. [CrossRef]
2. Sharpe, S.M.; Seals, J.; Macdonald, A.H.; Crowgey, S.R. Non-Contact Vital Signs Monitor; Georgia Tech: Atlanta, GA, USA, 1990.
3.
Johnson, J.E.; Shay, O.; Kim, C.; Liao, C. Wearable Millimeter-Wave Device for Contactless Measurement of Arterial Pulses. IEEE
Trans. Biomed. Circuits Syst. 2020,13, 1525–1534. [CrossRef] [PubMed]
4.
Shafiq, G.; Veluvolu, K.C. Surface Chest Motion Decomposition for Cardiovascular Monitoring. Sci. Rep.
2014
,4, 5093. [CrossRef]
[PubMed]
5.
Aghasi, H.; Heydari, P. Millimeter-Wave Radars-on-Chip Enabling Next-Generation Cyberphysical Infrastructures. IEEE Commun.
Mag. 2020,58, 70–76. [CrossRef]
6.
Zhao, P.; Lu, C.X.; Wang, J.; Chen, C.; Wang, W.; Trigoni, N.; Markham, A. Human tracking and identification through a millimeter
wave radar. Ad Hoc Netw. 2021,116, 102475. [CrossRef]
7.
Kumari, P.; Vorobyov, S.A.; Heath, R.W. Adaptive Virtual Waveform Design for Millimeter-Wave Joint Communication-Radar.
IEEE Trans. Signal Process. 2019,68, 715–730. [CrossRef]
8.
Lei, P.; Liang, J.; Guan, Z.; Wang, J.; Zheng, T. Acceleration of FPGA based Convolutional Neural Network for Human Activity
Classification using Millimeter-Wave Radar. IEEE Access 2019,7, 88917–88926. [CrossRef]
9. Lin, J.C. Noninvasive microwave measurement of respiration. Proc. IEEE 2005,63, 1530. [CrossRef]
10.
Kim, H.J.; Kim, K.H.; Hong, Y.S.; Choi, J.J. Measurement of human heartbeat and respiration signals using phase detection radar.
Rev. Sci. Instrum. 2007,78, 1655. [CrossRef] [PubMed]

Sensors 2021,21, 7893 18 of 18
11.
Droitcour, A.D.; Boric-Lubecke, O.; Lubecke, V.M.; Lin, J.; Kovacs, G. Range correlation and I/Q performance benefits in
single-chip silicon Doppler radars for non-contact cardiopulmonary monitoring. Microw. Theory Tech. IEEE Trans.
2004
,52,
838–848. [CrossRef]
12.
Uenoyama, M.; Matsui, T.; Yamada, K.; Suzuki, S.; Kawakami, M. Non-contact respiratory monitoring system using a ceiling-
attached microwave antenna. Med. Biol. Eng. Comput. 2006,44, 835–840. [CrossRef]
13.
Brink, M.; Müller, C.; Schierz, C. Contact-free measurement of heart rate, respiration rate, and body movements during sleep.
Behav. Res. Methods 2006,38, 511–521. [CrossRef] [PubMed]
14.
Xu, Y.; Jie, C.; Dai, S.; Fang, G. Experimental Study of UWB Pulse Radar for Life Detection. In Proceedings of the 2011
First International Conference on Instrumentation, Measurement, Computer, Communication and Control, Beijing, China,
21–23 October 2011.
15.
Ren, L.; Wang, H.; Naishadham, K.; Liu, Q.; Fathy, A.E. Non-invasive detection of cardiac and respiratory rates from stepped
frequency continuous wave radar measurements using the state space method. In Proceedings of the 2015 IEEE MTT-S
International Microwave Symposium, Phoenix, AZ, USA, 17–22 May 2015.
16.
Ahmad, A.; Roh, J.C.; Wang, D.; Dubey, A. Vital signs monitoring of multiple people using a FMCW millimeter-wave sensor. In
Proceedings of the 2018 IEEE Radar Conference (RadarConf18), Oklahoma City, OK, USA, 23–27 April 2018.
17.
Nguyen, V.; Javaid, A.Q.; Weitnauer, M.A. Spectrum-averaged Harmonic Path (SHAPA) algorithm for non-contact vital sign
monitoring with ultra-wideband (UWB) radar. In Proceedings of the 2014 36th Annual International Conference of the IEEE
Engineering in Medicine and Biology Society, Chicago, IL, USA, 26–30 August 2014; Volume 2014, pp. 2241–2244.
18.
Ren, L.; Wang, H.; Naishadham, K.; Kilic, O.; Fathy, A.E. Phase-Based Methods for Heart Rate Detection Using UWB Impulse
Doppler Radar. IEEE Trans. Microw. Theory Tech. 2016,64, 3319–3331. [CrossRef]
19.
Liu, Y.; Weisberg, R.H.; Merz, C.R. Assessment of CODAR SeaSonde and WERA HF Radars in Mapping Surface Currents on the
West Florida Shelf. J. Atmos. Ocean. Technol. 2014,31, 1363–1382. [CrossRef]
20.
Poguntke, T.; Filho, D.D.D.C.; Ochs, K. A System-Theoretic View on Breathing Detection Using Chirp Sequence Modulated Radar
Sensors. In Proceedings of the 2017 New Generation of CAS (NGCAS), Genova, Italy, 6–9 September 2017.
21.
Luo, J.Z.; Da-Fang, Y.I. Discussion on the Mechanism of Vital Sign Detection with CW Radars. Telecommun. Eng.
2008
,48, 29–33.
22.
Hosseini, S.; Amindavar, H. UWB radar signal processing in measurement of heartbeat features. In Proceedings of the IEEE
International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, USA, 5–9 March 2017.
23.
Klauder, J.R.; Price, A.C.; Darlington, S.; Albersheim, W.J. The Theory and Design of Chirp Radars. Bell Syst. Tech. J.
2013
,39,
745–808. [CrossRef]
24.
Cardillo, E.; Li, C.; Caddemi, A. Millimeter-Wave Radar Cane: A Blind People Aid with Moving Human Recognition Capabilities.
In IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology; IEEE: Piscataway, NJ, USA, 2021. [CrossRef]
25.
Cardillo, E.; Li, C.; Caddemi, A. Vital Sign Detection and Radar Self-Motion Cancellation through Clutter Identification. In IEEE
Transactions on Microwave Theory and Techniques; IEEE: Piscataway, NJ, USA, 2021; Volume 69, pp. 1932–1942. [CrossRef]
26.
Yang, D.; Zhu, Z.; Liang, B. Vital Sign Signal Extraction Method Based on Permutation Entropy and EEMD Algorithm for
Ultra-Wideband Radar. IEEE Access 2019,7, 178879–178890. [CrossRef]
27.
Yang, D.; Zhu, Z.; Zhang, J.; Liang, B. The Overview of Human Localization and Vital Sign Signal Measurement Using Handheld
IR-UWB Through-Wall Radar. Sensors 2021,21, 402. [CrossRef]