Instrumentation & Measurement Magazine 25-2 - 18

S mT t   ( )
00
B
 exp j



x mT
 

 
exp j




4 440KR f R fc x mT( )
00
exp 2j ft

Bm
fB 
m 
where fc
c
 

t
2BR 0
cT
4 f R f x mT R

  0
c
00  4 ( )4 4( )
0

x mT
c






c cc
  0

t 
4Kt

c
c cc
(7)
(8)
(9)
and λc are the central carrier frequency and its corresponding
wavelength. The fast Fourier transform (FFT) can be
applied for each chirp to separate multi-targets from the range
profile. Then, the phase evolution across multiple sweeps can
be obtained as:


( ) arg
m  BS mT nTs exp 2 Bs
N 0n


1
N
1   j f nT
ˆ

(10)
where arg[·] is the operation of taking the phase angle. From
(9), it is seen that the time-varying phase of the baseband
signal has a linear relationship with the vibration displacement.
Therefore, through the interferometric phase evolution
tracking of the baseband signal, the variation of the vibration
displacement of the adjacent sweep period can be obtained as:
 
4
x
c
Microwave Vital Sign Monitoring
The research on micro-motion monitoring using radio signals
began to rise in the field of human healthcare. This non-contact
vital signs monitoring method is non-invasive. Since it is not
directly in contact with the skin, the technique does not cause
discomfort to the human body, resulting in long-term monitoring
unconsciously that is especially useful in sleep quality
monitoring. Thus, it has a broad application prospect in the
healthcare of infants and
the elderly. Moreover, the
microwave radar-based
remote detection of vital
signs has brought several
potential applications,
such as the detection of abnormal
breathing and the
search and rescue of survivors
after an earthquake.
In recent years, vital
signs monitoring based
on microwave sensi
ng ha s been wide l y
studied and achieved several
preliminary results.
The fluctuation of the
18
(11)

0KR f R 44 4 f
human thoracic cavity will produce nonlinear phase modulation
of the microwave signal (Fig. 2). In general, the mechanical
displacement related to breathing commonly varies by 1-10
mm and that of heartbeat is in the range of 0.1-1 mm. The
respiration commonly exhibits approximately sinusoidal
movements that can be easily detected and monitored. In contrast,
the cardiac movement has very small displacement,
which is easily affected by random body movement. In addition,
the respiratory harmonic interference will also affect the
accuracy of vital signs monitoring.
The elimination of human movement clutter is of great significance
for the accurate extraction of breathing and heartbeat
frequency in real scenarios. A heartbeat signal extractor is provided
in [10], which decomposes the respiratory and heartbeat
waveforms from the chest movement by iterative optimization.
The proposed method models the chest fluctuation as
the superposition of body motion, respiratory motion, heartbeat
motion and random phase noise. Specifically, a penalty
factor is applied to balance the bandwidth constraint and data
fidelity. Additionally, the desired waveforms are extracted by
alternatively updating each component and its corresponding
center frequency until convergence. The exacted time corresponding
to each heartbeat can be identified by peak detection
of the extracted heartbeat waveform, which is crucial to the
analysis of heart rate variability (HRV) and has important research
value in the field of instantaneous heart rate change and
emotion recognition. In addition to eliminating the influence
of large human movements from the perspective of signal processing,
a method termed near-field coherent sensing (NCS)
using RF identification tags was proposed in [11] for vital sign
monitoring. RF tags are integrated into the chest or wrist area
of clothes, which can concentrate the electromagnetic wave energy
and also effectively reduce the impact of body movement.
Then, the NCS modulates the surface and internal body movements
to the multiplexed radio signals.
In general, breathing causes chest fluctuations, which are
one to two orders of magnitude greater than the heartbeat,
and the third or fourth harmonic of respiration are possibly
close to the heartbeat fundamental frequency. Therefore,
Fig. 2. Block diagram of microwave vital sign monitoring system. (From [9], ©2020 IEEE).
IEEE Instrumentation & Measurement Magazine
April 2022
Instrumentation & Measurement Magazine 25-2

Table of Contents for the Digital Edition of Instrumentation & Measurement Magazine 25-2
