IEEE - Aerospace and Electronic Systems - March 2022 - 6

Feature Article:
DOI. No. 10.1109/MAES.2021.3138948
SimHumalator: An Open-Source End-to-End Radar
Simulator for Human Activity Recognition
Shelly Vishwakarma , Wenda Li , Chong Tang , and Karl Woodbridge ,
University College London, London WC1E 6BT, U.K.
Raviraj Adve
, University of Toronto, Toronto, ONM5S 3G4, Canada
Kevin Chetty , University College London, London WC1E 6BT, U.K.
BACKGROUND
Recent years havewitnessed significant growth in the proportion
of the aging population and the number of people with
long-term disability and chronic health conditions. According
to World Population Prospects 2019, it is expected that
by 2050, there will be one person in every six over the age of
65, which is 16% higher compared to 2015 [1]. The growth
in these numbers has already distressed the existing healthcare
systems worldwide due to increased demand for dedicated
residential care services that require continuous close
monitoring of the patients and older people. Therefore, there
is an urgent need for technologies that enable healthy living
to ensure the health, safety, and well-being of people of all
age groups, with a particular focus on supporting people livingindependentlyintheir
ownhomes.
There is considerable interest in developing cost-effective
and reliable technologies enabling healthy living. The
technologies are specifically tailored for the elderly population,
catering to their special needs ofnoninvasive continuous
monitoring while offering them complete freedom to
live a fulfilling life. The increased research interest in these
innovative technologies is evoked by their likely contribution
to the efficiency and effectiveness offuture healthcare.
The traditional sensing technologies use intrusive wearable
sensors such as wrist-worn units acting as emergency
Author current address: Shelly Vishwakarma, Wenda Li,
Chong Tang, Karl Woodbridge, and Kevin Chetty are
with the University College London, LondonWC1E 6BT,
U.K. (e-mail: vishwakarma@ucl.ac.uk; wenda.li@ucl.ac.
uk; chong.tang.18@ucl.ac.uk; k.woodbridge@ucl.ac.uk;
k.chetty@ucl.ac.uk); Raviraj Adve is with the University
of Toronto, Toronto, ON M5S 3G4, Canada (e-mail:
rsadve@comm.utoronto.ca)
Manuscript received May 25, 2021, revised November
29, 2021; accepted December 23, 2021, and ready for
publication December 25, 2021.
Review handled by Christof Schupbach.
0885-8985/21/$26.00 ß 2021 IEEE
6
rescue buttons to handle emergencies at home or outside
[2]. However, the usage of wearable sensors and
expensive camera systems is limited because they can operate
only under suitable environmental conditions and are
affected by perceived privacy risks, comfort levels, and
battery life. Moreover, the cameras can work only in the
line-of-sight conditions and thus have limited coverage [3].
X-ray signals possess good wall penetration capability.
However, the technology is bulky and expensive, and the
radiation can potentially harm the patient. Fortunately,
radio frequency (RF) sensing, being noninvasive, can elevate
the need for in-person monitoring services. It can
work even in nonline-of-sight scenarios (mainly throughthe-wall
scenarios), making it a safe alternative. RF sensing
is low-cost, noninvasive, privacy-preserving, and can continuously
monitor human activities remotely using commercial
off-the-shelfcomponents [4], [5].
Humans are rarely still. When illuminated by an RF
source, the human motions introduce a Doppler shift to
the radar signals. In addition, micromotions due to the
motion of hands and legs give rise to additional frequency
variations known as micro-Doppler. The micro-Doppler
possesses unique and discriminative features when
observed in a joint time-frequency space [6]. Extensive
research has exploited these signatures for indoor healthcare
applications, such as assisted living (for fall detection
in older adults) [5],[7], biomedical applications (for nonintrusively
monitoring patients), and detecting the debilitating
motor symptoms of Parkinson's patients [8].
Micro-Doppler data have been generally gathered
through actual measurements using active and passive RF
sensors [6], [9], [10]. Active sensing uses the system's transmissions
as the source ofillumination, whereas passive sensing
leverages existing opportunistic signals such as WiFi (or
other wireless) communications to capture RF returns passively.
Due to the number of advantages, such as its receiveonly
nature, low energy consumption, and presence ofmultiple
illuminators, the passive sensing systems have become
increasingly popular and are extensivelyusedfor short-range
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MARCH 2022
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