IEEE Circuits and Systems Magazine - Q1 2021 - 65

(time-domain cross-correlation) algorithm was developed and implemented on an FPGA for impulse radar.
This was performed with a carrier frequency of
5.77 GHz and transmit power of 30 dBm. The design
achieved cross correlation computation time of 121.63 µs
and a vibration spectrum monitoring of up to 50 Hz.
In this section, the commonly used processing platforms for vital sign detection have been reviewed. Researchers in the reviewed literature used PC, MCU, GPU,
FPGA, DSP, or a combination of these platforms. The
choice of the dedicated processing platform, such as
FPGA, is made by these researchers to achieve higher
processing and detection speeds. The different uses
of FPGAs in the context of vital sign detection and the
different algorithms implemented on FPGAs have also
been discussed.
VI. Detection and Communication
It is inevitable that detected signals via radars are required to be transferred/communicated in some way
to another location. Several frequency bands used for
communication in biomedical applications such as the
industrial, scientific and medical (ISM) band, the UWB
band, Radio Frequency Identification (RFID) band, Bluetooth frequency band, WLAN frequency band and Medical Body Area Network (MBAN) frequency band [20],
[85]. Recently, several approaches for vital sign detection integrated with communication approaches have
been proposed.

Buffer

Received
Signal

A. Detection
This subsection illustrates the techniques used for vital sign detection using communication devices (such
as WLAN routers). For example, the work in [86] proposed time reversal based respiration rate detection
within a very short period of time. This approach used
off the shelf WLAN devices and their channel state information (CSI) to capture small variations in the surroundings caused by respiration [86], [87]. This method can be easily implemented using any existing WLAN
hardware and networks available indoors. The two
prototypes in [86] were built using WLAN cards with
three omnidirectional antennas. One of the prototypes
works as the access point, while the other one works
as the station. The center frequency used was 5.765 GHz
with a bandwidth of 40 MHz. During experiments,
only two to three WLAN networks were observed to
be sharing the same channel, resulting in less than
1% of packet loss rate, which is insignificant and can
be ignored. Jian et al. in [88] proposed a system to detect both heart rate and breathing during sleep using
off-the-shelf WLAN (WiFi) devices. Similar to [86] and
[87], this system reused the existing WLAN network
and exploited the channel state information to capture
the tiny movement due to respiration. This experiment
was conducted in an 802.11n WLAN (Lenovo T500 Laptop) connected to a wireless access point (AP) (model
TP-Link TL-WDR4300) with a packet transmission rate
of 20 pkts/s.

Matching Result
Update and Index
Selection Unit

Memory
G

GIK_IK

Initialize and
Update
Matching Result
α

Support Set Ik

Sensing
Matric
αin it

G_inv

Index Selection

αinitlk

Update
W, V, U

Update
G -1,

VUWT
-VU
Parameter
Update Unit

Figure 20. Architectural block of algorithm FPGA implementation [44]. Licensed under Creative Commons attribution license
https://creativecommons.org/licenses/by/4.0/

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