IEEE Circuits and Systems Magazine - Q1 2021 - 60

The reported process gain of this work was 32.83 dB.
Considering the reflected powers at a target at 5 m distance, the floor noise and the progress gain, the SNR
is estimated to be 70 dB for the person and 53 dB for
the chest surface.
Random body movement rejection in vital sign detection scenarios is one of the main challenges faced by
the researchers. Researchers in [64] tackled this issue
by first identifying the hidden in the modulated phase
of the artifacts using CWT algorithm, prior to applying
the moving average method to smooth the signal in
those locations. Meanwhile, in [65] the features of the
frequency spectrum of vital signs while undergoing random body motion are analyzed. This work utilized the
motion modulation effect and extracted the direction
of the body motion with the new position of the respiration peaks. Since body movements introduce frequencyshifts in the spectrum, the direction and amount of this
frequency shift depends on the direction and the speed
of the body motion. Thus, this feature was used to account for the body motions in the spectrum to detect
the respiration rate accordingly. Meanwhile, the work
in [66] effectively reduced the random movement using
two methods. The complex signal demodulation (CSD)
and the arctangent demodulation methods were implemented in the Doppler radar detection of vital signs. It
was targeted for sleep monitoring and baby monitoring
to eliminate false alarm caused by random movements.
The CSD is more immune against the effects of the dc
offset, whereas the AD reduces the effect of harmon-

Sampled Data

ics and intermodulation interference and high carrier
frequencies. Finally, an adaptive phase compensation
method was used for random body movement cancellation in [67]. To measure the random body movements of a subject, a camera was integrated in the radar system. The camera measurement was fed back
into the system as the phase information. Using the
phase compensation avoids potential saturation of
the high gain baseband in the presence of large body
movements. A simple video processing was also performed to extract the random body information without using any markers.
In this section, many of the algorithms used for
vital sign detection were reviewed, regardless of the
platforms on which they were implemented. Some of
these algorithms focus on the rejection of clutter and
noise, and thus on improving the accuracy. Meanwhile
other algorithms focused on the separation of HR from
RR and the extraction of the required features. Several
important algorithms discussed here are the orthogonal matching pursuit, compressive sensing, singular
value decomposition, and state space method. Another important aspect in radar detection for human vital
sign, is the selection of processing platform. Summary
of the algorithms, process gain/SNR feature and some
remarks of their advantages and drawbacks have been
listed in Table 6. The different processing platforms
used in the literature will be discussed in the next
section. The discussion will have special focus on the
FPGA as a processing platform.

Random Matrix
Ψ

Zero Padding

IFFT
Module

Find Max
Module

Residual
R

Matrix-Matrix/Vector
Multiplications
Module

Gram-Schmidt
Orthogonalization
Module

Ψt
CG Iteration
Module

Update Residual R
Module

Figure 19. Architecture of FPGA top level entity implementation [68]. Licensed under Creative Commons attribution license
https://creativecommons.org/licenses/by/4.0/
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IEEE Circuits and Systems Magazine - Q1 2021

Table of Contents for the Digital Edition of IEEE Circuits and Systems Magazine - Q1 2021