IEEE - Aerospace and Electronic Systems - November 2019 - 39

Shah and Fioranelli
such as sleep apnea and bradycardia of an infant baby. In
order to monitor the blood pressure, heart rate, and respiratory pattern, an integrated vital signs monitoring system
was proposed in [83]. Exploiting a smartphone in conjunction with RFID chips, all the collected signals can be
exported to a server. The microcontroller has EEPROM
memory for recording all measured results continuously.
This healthcare solution improved the person's data collection procedures by decreasing the processing time and
increasing the overall accuracy of the system. The results
obtained for vital signs indicated that the percent of accuracy was around 90%.
The RFID applications for healthcare and safety sector
are summarized in Table 2.

OUTSTANDING CHALLENES
There are some outstanding challenges for RF sensing
technologies for assisted activities of daily living remain
to be incorporated to deploy these systems in real-world
scenarios. Specifically, issues related to the use of radar
for healthcare application arise from signal processing and
deployment perspective. Some of the challenges encountered for radar, Wi-Fi, and RFID technologies are discussed as follows.

OUTSTANDING CHALLENGES IN RADAR TECHNOLOGY
One of the biggest challenges is the presence of clutter in
indoor settings that may produce multipath effects such as
a result false/ghost target. There is also a strong possibility
of other people or pets present and moving in the area of
interest, thus brining complications to the signatures
obtained using radar, resulting in the generation of false
alarms. The radar waveforms generated by a radar for
healthcare applications have been in compliance with the
telecommunication governing bodies, encountering potential limitations in the context of transmitting power and
bandwidth utilized, which put constraints on the signal-tonoise ratio (SNR) and range resolution. The probability of
detecting human activities and critical events such as falls,
irrespective of the sequences of activities performed is
also one of the major challenges. A generalize system that
is capable of taking the specificity of a subject under
observation into account. The specificity may include
physical impairment of a patient or any other constraints
associated with specific person. This phenomenon makes
things complex in order to train a classifier effectively
irrespective of observation times and huge datasets associated with a particular subject and environment where the
experiments were performed.
The open challenges for using radar technology for
ADL can be addressed using multiple radar sensors
deployed in indoor settings and may be investigated as a
NOVEMBER 2019

future work including the exploitation of the complementarity of multiple technologies working in parallel. As far as
radar technology is concerned, an approach involving multistatic radar systems having various nodes with spatially
separated transmitter and receiver capabilities that can
cover the whole area in indoor environment. This approach
has produced promising performance for micro-Doppler
classification in outdoor settings [84]-[88]. The same
approach using radar technology can deliver promising
results in detecting fall events indoor environments [89]. A
simpler method could be the usage of multiple monostatic
radar sensors, instead of using multistatic radar system.

OUTSTANDING CHALLENGES IN Wi-Fi TECHNOLOGY
On the basis of examining some of the key application of
Wi-Fi sensing for healthcare application, all implementation methodologies use the basic principle of RF signal
propagating from Wi-Fi transmitter to the receiver through
multiple paths. The Wi-Fi signal propagating carries information about particular human activities. In order to put
Wi-Fi sensing for assisted activities of daily living from
the perspective of real-world application, following open
challenges need to be addressed:
- All the applications based on Wi-Fi sensing technologies discussed in this paper have provided adequate
robustness and accuracy. However, the experiments
are only performed in a controlled environment. The
results indicate that placement of the equipment's
distance between the subject and devices in use, multiple occupant within area of interest and behind-thewall detection of movement significantly affect and
influence the overall accuracy of the system. In this
context, the Wi-Fi sensing technology in conjunction
with other signal processing technique should be
designed in such a way that it should provide reliable
and robust performance in real-world scenarios
while keeping accuracy as high as possible.
- The WCI obtained using Wi-Fi signals can help in
achieving recognition tasks using commercially
available devices. However, the existing IEEE
802.11n protocol for Wi-Fi signals is the only solution for HAR. Other protocols such as 802.11ac and
802.11ah can be potentially used for healthcare
applications.
- Researchers have mostly used low-cost Intel 5300
network interface card for extracting channel state
information at the receiver side which only report
group of 30 OFDM subcarriers. Other platform such
as software defined radios can be used to extract the
same information where the number of OFDM subcarriers can be modified as per the requirement of
specific application.

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IEEE - Aerospace and Electronic Systems - November 2019

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