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de Fazio et al. / Front Inform Technol Electron Eng 2021 22(11):1413-1442 1413
Wearable devices and IoT applications for symptom detection,
infection tracking, and diffusion containment of the
COVID-19 pandemic: a survey
Roberto DE FAZIO1, Nicola Ivan GIANNOCCARO1, Miguel CARRASCO2,
Ramiro VELAZQUEZ3, Paolo VISCONTI‡1
1Department of Innovation Engineering, University of Salento, Lecce 73100, Italy
2Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibáñez, Peñalolén, Santiago 7941169, Chile
3Facultad de Ingeniería, Universidad Panamericana, Aguascalientes 20290, Mexico
E-mail: roberto.defazio@unisalento.it; ivan.giannoccaro@unisalento.it; miguel.carrasco@uai.cl;
rvelazquez@up.edu.mx; paolo.visconti@unisalento.it
Received Feb. 15, 2021; Revision accepted June 7, 2021; Crosschecked Oct. 9, 2021
Abstract: Until a safe and effective vaccine to fight the SARS-CoV-2 virus is developed and available for the global population,
preventive measures, such as wearable tracking and monitoring systems supported by Internet of Things (IoT) infrastructures, are
valuable tools for containing the pandemic. In this review paper we analyze innovative wearable systems for limiting the virus
spread, early detection of the first symptoms of the coronavirus disease COVID-19 infection, and remote monitoring of the health
conditions of infected patients during the quarantine. The attention is focused on systems allowing quick user screening through
ready-to-use hardware and software components. Such sensor-based systems monitor the principal vital signs, detect symptoms
related to COVID-19 early, and alert patients and medical staff. Novel wearable devices for complying with social distancing rules
and limiting interpersonal contagion (such as smart masks) are investigated and analyzed. In addition, an overview of implantable
devices for monitoring the effects of COVID-19 on the cardiovascular system is presented. Then we report an overview of tracing
strategies and technologies for containing the COVID-19 pandemic based on IoT technologies, wearable devices, and cloud
computing. In detail, we demonstrate the potential of radio frequency based signal technology, including Bluetooth Low Energy
(BLE), Wi-Fi, and radio frequency identification (RFID), often combined with Apps and cloud technology. Finally, critical
analysis and comparisons of the different discussed solutions are presented, highlighting their potential and providing new insights
for developing innovative tools for facing future pandemics.
Key words: Wearable devices; IoT health-monitoring applications; Medical sensors; COVID-19 pandemic; Symptom detection
https://doi.org/10.1631/FITEE.2100085 CLC number: TP212.9
1 Introduction
Globally, as of 11 February 2021, there have
been more than 106 990 000 confirmed cases of
coronavirus disease COVID-19, including more than
2 347 000 deaths reported by the World Health
Organization (WHO). The virus has been contracted
on almost every continent (Fig. 1). Furthermore, the
pandemic is causing severe damage to the economic
systems of each country. Fig. 2 shows the gross
domestic product (GDP) growth rate of the European
Union (EU) countries and the related forecasts for
2021. In 2020, all the nations belonging to the Euro
area suffered from reduced GDP between 4% and
10% (Fig. 2a). However, forecasts for 2021 indicate a
strong rebound in GDP with a mean increase higher
Frontiers of Information Technology & Electronic Engineering
www.jzus.zju.edu.cn; engineering.cae.cn; www.springerlink.com
ISSN 2095-9184 (print); ISSN 2095-9230 (online)
E-mail: jzus@zju.edu.cn
‡ Corresponding author
ORCID: Roberto DE FAZIO, https://orcid.org/0000-0003-0893-
138X; Paolo VISCONTI, https://orcid.org/0000-0002-4058-4042
© Zhejiang University Press 2021
Review:
de Fazio et al. / Front Inform Technol Electron Eng 2021 22(11):1413-1442
1414
than 4% (Fig. 2b). The virus continues to affect every
region of the world, and while some countries appear
to have primarily controlled the virus, others are
experiencing high and rising infection rates. To
reduce transmission and control the epidemic as soon
as possible, the scientific community focuses on
different critical activities (UK Health Security
Agency, 2020). In this context, technology constitutes
a powerful tool for remote monitoring of user
parameters, determining health issues, and making
decisions about the most suitable therapy for
improving the user’s lifestyle (Visconti et al., 2018,
2019; Gaetani et al., 2019). In particular, the scientific
community is developing contact tracking processes.
Contact tracing is the process of identifying, assessing,
and managing people who have been exposed to a
disease to prevent onward transmission. Contact
tracing for COVID-19 requires identifying persons
who may have been exposed to COVID-19 and
following them daily for 14 d from the last point of
exposure.
The process is challenging to put into practice
because it takes a long time and, above all, because
virus can be transmitted between people without
evident symptoms. Therefore, remote patient health
status diagnostic and monitoring methods become
fundamental to reduce the virus’s spread (Donaghy et
al., 2019; CDC, 2020). The diagnostic and monitoring
methods involve considerable data processing
difficulties, particularly regarding data security and
privacy (Rights Office for Civil, 2020). Wearable
devices have an enormous potential to curb the spread
of the COVID-19 pandemic and other infective
diseases. Several efforts have been made in the last
months by the scientific community and companies to
develop advanced, portable, low-power, and
multifunctional wearable devices to detect the onset
of the symptoms of COVID-19 diseases, such as fever,
cough, reduced blood oxygenation (i.e., low SpO2),
and increased heart-rate variability (HRV), and thus
intervene more quickly before the deterioration of the
patient’s physical condition.
In this paper we investigate the different Internet
of Things (IoT) solutions and wearable sensing
devices reported on the market and in the scientific
literature for early detection of symptoms of
COVID-19. Specifically, wearable devices and the
IoT system are currently employed by hundreds of
millions of people worldwide to detect different
biophysical and environmental parameters, such as
body temperature, heart rate (HR), SpO2, and
respiration rate (RR). These devices, supported by
cloud platforms and mobile applications, can process
the acquired information continuously and in real
time to detect the early stages of the infection. We
provide an overview of the different wearable
solutions for complying with social distancing rules;
particularly, the main countermeasures for reducing
the spreading of COVID-19 are the use of the mask
and social distancing. Several solutions have been
developed to help workers check and maintain social
distancing in the workplace, thus allowing the
continuation of production activities (European
Centre for Disease Prevention and Control, 2020).
2020 2021
Percentage
of GDP
≥+6%
≥+5%
≥−4%
≥+4%
≥+3%
≥+2%
≥−6%
≥−8%
≥−10%
<−10%
(a) (b)
Fig. 2 Geographic maps of the gross domestic product
(GDP) growth rate of the European Union countries in
2020 (a) and as expected for 2021 (b) (European Commis-
sion, 2020; WHO, 2020)
Fig. 1 COVID-19 infections (a) and deaths (b) trends by
continent (WHO, 2020)
47 606 632
36 132 951
5 924 791
2 694 171
1 509 522
13 122 278
Confirm ed
February 11, 2021
18 5 932 Americas 16 0 815 Europe
23 538 South- Ea st Asia 25 957 Eastern Mediterranean
10 577 Af rica 71 39 W estern Pa cific
Mar 31 Jun 30 Sep t 30 D ec 31
Americ as
Europe
East ern M editerrane an
Afric a
Western Pacific
South-Eas t Asia
(a)
Deaths
Americas
Europe
Eastern Mediterrane an
Afric a
Western Pacific
South-Eas t Asia
Feb ruar y 1 1, 202 1
67 47 Am ericas 46 89 Europe
31 5 South-East Asia 38 2 Eastern Mediterranean
403 Africa 25 1 W estern Pacific
Mar 31 Jun 30 Sep t 30 Dec 31
1 112 708
800 491
138 45 9
67 225
26 613
201 50 6
(b)
de Fazio et al. / Front Inform Technol Electron Eng 2021 22(11):1413-1442 1415
Different models of smart masks have been
considered and analyzed as an essential tool for
continuing control of pandemic diffusion, as
suggested by the WHO guidelines.
Recent studies have demonstrated a correlation
between cardiovascular diseases and COVID-19, so
we have investigated an implantable solution for
remotely monitoring heart conditions. State-of-the-art
tracing systems for containing the COVID-19
pandemic are also reported. In fact, companies and
state governments introduced tracing applications for
tracking citizens’ movements from the first months of
the pandemic, and can thus rebuild the contagion
chain once a user is found to be infected, limiting the
pandemic’s spread. Finally, we apply critical analysis
and performance comparisons to the discussed
applications and point out the advantages, limitations,
and future potentials for obtaining the tools that are
necessary to face future pandemics.
To our knowledge, the current scientific
literature does not include a review work that is as
comprehensive and in-depth as this paper, which
concerns not only prototypes derived in scientific
work but also commercial and post-development
devices for commercialization. In particular, this
study considers a wide range of last-generation
wearable and portable devices reported in the
scientific literature and on the market to detect the
symptoms of COVID-19, remotely monitor infected
patients, trace the contagion chain, and follow the
patient’s post-illness status. Furthermore, novel IoT
tracing systems, also supported by wearable devices,
are deeply explored, focusing on the numerous
solutions proposed by research centers and
governmental bodies during the two years 2020–2021
to contain the COVID-19 pandemic. The aim is to
cover as many technologies and solutions as possible
to provide the reader with a comprehensive view of
the treated topics. Finally, critical analysis of the
different devices and strategies is reported, providing
a comparison to highlight potential benefits and
shortcomings to help develop the tools to tackle future
pandemics; we consider this one of the main
contributions offered by this work.
This review paper is arranged as follows:
Section 2 covers an overview of different IoT
solutions and wearable sensing devices for detecting
the first symptoms of COVID-19. In Section 2.1, we
discuss the different types of wearable devices and
sensors to detect COVID-19 symptoms. In Section
2.2, various models of smart masks for safety and
detection purposes are analyzed. Section 2.3 presents
an analysis of implantable devices for detecting the
effects of COVID-19 on the human body. Section 3 is
an overview of wearable solutions to help users
maintain social distancing in the workplace. Section 4
presents the state of the art in tracing systems for
containing COVID-19. In Section 5, we provide
performance comparisons and critical analysis of the
devices, technologies, and architectures described in
the previous sections. Finally, Section 6 concludes the
paper.
2 IoT solutions and wearable sensing de-
vices to tackle the COVID-19 pandemic
2.1 Wearable devices and sensors to detect
COVID-19 symptoms
Here we explore innovative wearable solutions
(reported in scientific works) that are aimed at
detecting the onset of the first symptoms of
COVID-19, such as fever, cough, respiratory issues,
and low blood oxygenation, and innovative sensors
for detecting infected people in a rapid and
non-invasive manner. Different solutions have been
proposed in the scientific literature for remote
tracking and monitoring of patient vital signs to detect
worsening of their conditions early in the disease
process (Chung et al., 2020; Greenhalgh et al., 2020;
Menni et al., 2020).
Body temperature is the primary indicator of
possible contagion by the COVID-19 virus; for body
temperature higher than 37.5 °C, self-quarantine is
suggested to the patient to avoid an eventual diffusion
of the disease, and a reverse transcriptase-polymerase
chain reaction (RT-PCR) test is performed. Therefore,
solutions for remotely monitoring body temperature
have been presented on the market or proposed in
scientific works. Mondal et al. (2020) proposed a
low-cost and lightweight solution for remotely
monitoring body temperature, ensuring 98% accuracy.
The resulting wearable design is comfortable and can
be integrated into our daily lives amid the current
COVID-19 pandemic. In addition, Chen XY et al.
(2020) developed an in-ear thermometer for
monitoring body temperature with smartphone support.
Also, several watch-type thermometers are present on
de Fazio et al. / Front Inform Technol Electron Eng 2021 22(11):1413-1442
1416
the market, allowing continuous monitoring and
comfort. Examples of such devices are the iFever,
iTherm, and Tadsafe™ wrist thermometers, all
equipped with Bluetooth connectivity, remotely
monitoring the patient temperature and suitable for
infants (iFever, 2018; Indiegogo, 2018; TADSAFE,
2019). Conversely, plaster-type thermometers
represent a practical solution for continuously
detecting neonate body temperature without staff
carrying the device. Fever Scout (Fig. 3a), TempTraq
(Fig. 3b), and Tucky (Fig. 3c) are examples of these
devices, representing hand-free, easy-to-use, reusable
solutions to monitor body temperature (VivaLNK,
2020; E-takescare, 2021; TempTraq, 2021).
Impaired respiratory activity induced by
COVID-19 also significantly affects cardiovascular
activity. Thus, the HR is a good indicator of the
body’s physiological stress caused by the viral
infection. Wearable devices constitute an effective,
convenient, and handy solution for monitoring the HR
of COVID-19 patients. Patil et al. (2019) developed a
wireless device to continuously monitor heart activity.
The system acquires and processes the data from a
photoplethysmography (PPG) sensor and provides
short message service (SMS) to medical staff and
parents for quick rescue. Furthermore, Sharma et al.
(2019) introduced a novel acoustic sensing method
for cardiac monitoring in wearable devices. The
technique is based on detecting heart sounds and the
pulse waves from the radial artery in the wrist.
Shahshahani et al. (2017) introduced a non-invasive
ultrasound technology to detect heart motions and
thus determine the HR. The developed wearable
prototype detects the time-of-flight (TOF) and
amplitude of an ultrasound signal reflected from the
chest. Furthermore, Quy et al. (2019) proposed a
wrist-type wearable device to monitor the HR based
on highly sensitive and ultrastable piezoresistive
pressure sensors. This device includes a multi-layer
graphite/polydimethylsiloxane composite structure.
Because the main consequence of COVID-19
is bilateral pneumonia, which compromises the
respiratory capacity of the infected patient, the
monitoring of SpO2 is the primary indicator for
establishing severity and advancement of COVID-19.
Xue et al. (2015) developed a wearable device to
continuously monitor SpO2 and temperature. The
system includes an AFE44x0 chip for implementing
the pulse-oximeter, with the sensing probe applied to
the earlobes. Son et al. (2017) used a wearable device
for SpO2 measurement to monitor the user’s health
condition in real time. The developed sensing unit is
based on the reflective PPG principle, and detects the
reflected light emitted by infrared and red light
emitting diodes (LEDs) using a photodiode.
Similarly, Adiputra et al. (2018) developed a
low-power and low-cost device to monitor SpO2 and
HR. Acquired data are wirelessly transmitted using a
network gateway IoT application, where the data are
displayed, stored, and analyzed. Chen QG and Tang
(2020) developed a wearable system to monitor blood
oxygenation from the PPG signal and proposed a new
adaptive cancellation algorithm based on adaptive
filtering to delete motion-induced interference.
RR is another fundamental indicator for
establishing a user’s health status and can suggest the
onset of a respiratory disease such as COVID-19. Chu
et al. (2 019) develop ed a disposable respiration sensor
in a typical patch form factor. This sensor includes a
strain gauge that converts chest movements due to
breathing into a resistance variation, which is
processed to extract the RR data. The RR can be
estimated from the acceleration data acquired by a
wearable device. Hung (2017) demonstrated that
chest-acceleration data can provide a reliable
estimation of the waveform and the RR. This method
can detect some respiratory disease, such as obtrusive
apnea. Tadi et al. (2014) presented a seismocardio-
graphy (SCG) method based on accelerometer data
for determining both the RR and information related
to the rest phase during the cardiac cycle obtained
from myocardial movements (atrial and ventricular).
The obtained results demonstrated a high linear
correlation between the derived measures of RR and
Fig. 3 Example of plaster-type thermometers: (a) Feve
r
Scout device, manufactured by Vivalnk Co. (VivaLNK,
2020); (b) TempTraq thermometer, manufactured by Blue
Spark Technologies, Inc. (TempTraq, 2021); (c) Tucky
wearable thermometer, produced by E-takescare Co.
(E-takescare, 2021)
(c)
(b)
(a)
de Fazio et al. / Front Inform Technol Electron Eng 2021 22(11):1413-1442 1417
myocardial movements with reference ones acquired
by an electrocardiogram (ECG) and respiration belt.
A research group of the Department of Engineering,
University of Cambridge used small conductive fibers
realized by 3D printing to sense breathing in a
tridimensional modality (Wang et al., 2020) (Fig. 4);
the corresponding process is called inflight fiber
printing (iFP). The researchers have developed a
micro-scale 3D printed composite fiber composed of
a silver core covered by a thin film of PEDOT:PSS
(i.e., poly(3,4-ethylenedioxythiophene) polystyrene
sulfonate) conductive polymer. These sensors are
lightweight, cheap, small, and can be easily integrated
into fabric. Because the fibers are fully biocompatible
and have dimensions compatible with biological cells,
they can deploy biological cells. Alternatively, RR
can be assessed by monitoring body temperature
variations, humidity, and CO2 using wearable devices
(Liu HP et al., 2019). In addition, the RR can be
determined using respiratory airflow detected
acoustic transducers (microphone) applied in
different areas of the human body, such as the mouth,
nose, and ear canal.
PMD Solutions has proposed RespiraSense, “a
wearable device that continuously monitors the RR
and has high tolerance to the user’s motion” (PMD
Solutions, 2021). The device uses piezoelectric
thin-film sensors arranged in an array to measure the
deformation and angles of the abdominal wall that
occur during respiration and to convert them into an
electrical signal. RespiraSense detects the sensor
signals and determines the mean RR during a 15-min
time interval using a proprietary algorithm, and
discards insignificant data. Comparing the measures
provided by the device with those of the capnography
system, the experimental results demonstrated a 95%
confidence level between ±2%, confirming the device
accuracy in the considered RR range (i.e., 9–21 BPM,
BPM=breaths per minute).
The data provided by an ordinary smartwatch
can also be used to detect preliminary symptoms of
COVID-19. Specifically, Mishra et al. (2020)
presented a smartphone App that collects the
smartphone and activity tracker data, along with self-
reported information and diagnostic test results, to
identify symptoms that are indicative of COVID-19
disease onset. They demonstrated that considering a
combination of sensor and self-provided data, the
developed model produces an area under the curve
(AUC) equal to 0.8 in discerning positive from
negative symptomatic patients, which is more
performant than the model that considers only the
symptoms (AUC=0.71, and impulsive relapse
questionnaire (IRQ) in the range of 0.63–0.79). The
performance of these techniques can be improved by
collecting data from other low-cost multifunctional
wearable devices. Also, the availability of innovative
sensors like wristbands, tattoos, textiles, patches, and
rings can aid in detecting biophysical and environ-
mental parameters. For instance, Moreddu et al. (2020)
developed a contact lens sensor to detect analytes (i.e.,
glucose, proteins, nitrite ions, etc.) in the tears to
PEO
XY hea ter stage
300 nm
Ag
C (PEO)
TEM
EDX
Ag precursor or PEDOT: PSS
Core-s hell nozz le
100 μm
PEO sheath
Cont act pad
Ag core
Fiber
bond
20 μm
Fiber bond cross-s ection
XPS be am ~ 100 μm
PEO
Layer 0
Layer 19
Diffusive
bou ndary
~6 0 μm
Cu
Normalized XPS int.
1.0
0.5
0.0
048121619
Number of layers
Ag
C-C
Cu
Fig. 4 Schematic representation of the inflight fiber printing (iFP) process (a), schematic view of the iFP fibers’ deposition
(b), transmission electron microscope (TEM) and electron diffraction spectroscopy (EDX) images of a single iFP fiber (c),
scanning electron microscopy (SEM) image depicting fiber bond with a contact pad (d) and related cross-section (e), and
X-ray photoelectron spectroscopy (XPS) profiling on the Ag fiber bond (f)
Reprinted from Wang et al. (2020), Copyright 2020, with permission from the authors, licensed under CC BY 4.0
(f)
(e)
(c)
(b) (d)
(a)
de Fazio et al. / Front Inform Technol Electron Eng 2021 22(11):1413-1442
1418
monitor the ocular health both in clinics and at
point-of-care settings. The lens includes microfluidic
channels in the form of a ring and four lateral
branches realized by laser ablation and biosensors
placed in the branch ends. The experimental tests,
carried out using synthetic tears and colorimetric
detection by a MATLAB algorithm executed by a
smartphone, demonstrated rapid and accurate
detection of considered analytes. In addition, smart
tattoos implanted under the skin are an efficient and
practical solution for monitoring the blood glucose
level. These tattoos are composed of an array of
biosensors implanted inside the subcutaneous tissue
detecting the local glucose change in the interstitial
fluid (Meetoo et al., 2019). If the glucose level
overcomes a predetermined threshold, color changes
are produced which are detectable using a non-
invasive optical reader; these technologies can be
easily extended to detect other analytes, like the
COVID-19 virus. Also, Mojsoska et al. (2021)
proposed a proof-of-concept patch-based COVID-19
assay to detect the SARS-CoV-2 spike surface protein.
The patch is based on a working graphene electrode
that is engineered with the spike protein antibody; the
variation in the cyclic voltammogram of the ferri/
ferrocyanide solution after the spike protein-antibody
binding is ascribable to a change of current in
[Fe(CN)6]3−/4−, which increases the spike protein
concentration. The experimental tests demonstrated
that the sensor’s detection is limited to 260 nmol/L.
Jeong H et al. (2020) described a tracking and
monitoring device developed by researchers at
Northwestern University and Chicago’s Shirley Ryan
AbilityLab. They proposed a soft, flexible, wirelessly
connected, and thin wearable device with the size of a
postage stamp placed just below the suprasternal
notch (Fig. 5). The device produces continuous
streams of data and uses artificial intelligence to
discover life-saving information and track patients.
Precisely, it continuously measures and analyzes
cough and chest movements (which indicate labored
or irregular breathing), breath sounds, HR, and body
temperature (including fever) in ways that are
impossible for traditional monitoring systems. After
these measurements, it transmits the data wirelessly to
a health insurance portability and accountability act
(HIPAA) protected cloud, where computer algorithms
produce custom graphical summaries to make the
monitoring even more immediate and easy.
A team at Australia’s Central Queensland
University used a “WHOOP wristwatch to detect the
early warning signs of COVID-19” (Labs DI, 2020;
Miller et al., 2020). Specifically, the team analyzed
RR changes to establish the risk of COVID-19
infections and developed a model to determine the
probability of COVID-19 positivity as a function of
the RR during sleep. Experimental tests have
demonstrated that the proposed model can identify
80% of COVID-19-positive users after a two-day
training phase.
Hassantabar et al. (2020) proposed a framework
called CovidDeep, which uses a deep neural network
(DNN) to evaluate the data provided by commercial
wearable devices to determine positive COVID-19
cases. The data were provided by wearable devices
and questionnaires compiled by the user and were
available on a suitable smartphone application. A
DNN was trained with data collected by 87 people
and achieved 98.1% accuracy in discerning positive
COVID-19 cases. El-Rashidy et al. (2020) introduced
a fog network framework to fill the gap between
medical technologies and the healthcare system to
detect users affected by COVID-19. The proposed
architecture integrates wearable devices, cloud
computing, fog computing, and clinical decision
support systems to obtain an efficient model to
identify infected individuals. The authors developed a
classifier that is based on a deep convolutional neural
network (CNN) applied to X-rays of the chest to
identify a patient as infected or normal by assigning
weights to the different aspects/features induced by
COVID-19 on the lungs. The proposed end-to-end
framework allows real-time monitoring of the patient
at home and early detection of infected individuals, so
Fig. 5 Demonstration of the application of the device on
the skin
Reprinted from Jeong H et al. (2020), Copyright 2020, wit
h
permission from the authors, licensed under CC BY 4.0
de Fazio et al. / Front Inform Technol Electron Eng 2021 22(11):1413-1442 1419
their contacts can be tracked to break the contagion
chain.
Wearable solutions for remotely monitoring the
user’s biophysical condition are receiving great
attention from the scientific community and
companies during the COVID-19 pandemic, because
they permit non-critical patients to be moved away
from hospital facilities, ensuring a satisfactory level
of assistance (Fig. 6a). In this field, LifeSignals Co.
has developed “a single-use biosensor for the
COVID-19 pandemic to monitor a patient’s main vital
signs” (LifeSignals, 2020). The device has to be
applied to the user’s chest and detects movement, the
heart’s electrical activity with a two-channel ECG,
blood oxygenation, and blood pressure (BP); the
smart patch is waterproof, resilient, and lightweight;
these are essential considering the application.
Acquired data are wirelessly transmitted through a
gateway to a cloud platform where the data are
displayed and analyzed for different monitored patients.
Deterioration of respiration or heart parameters
triggers an alert, allowing early intervention of the
medical staff.
Celsium has presented a wearable thermometer,
placed at the armpit, to remotely monitor the user’s
temperature (Celsium, 2020). The device is
Bluetooth-connected with a mobile App, which is
synchronized with an intelligent platform that
integrates a custom algorithm that accurately
determines the body temperature. “The smart
thermometer is designed for hospital environments,
where multiple devices are connected to a central
dashboard, allowing medical operators to monitor the
temperature of numerous patients and in real time.”
The dashboard warns the medical staff if the
temperature overcomes a set threshold over a short
time interval (Fig. 6b).
Another wearable device for monitoring and
tracking the patient’s health conditions is the
ECGalert manufactured by Savvy Co. (ECG Alert,
2020) (Fig. 7a). It can automatically detect atrial
fibrillation in COVID-19 patients, usually from some
particular treatments like with hydroxychloroquine
and azithromycin. Automatic and early detection is of
paramount importance in these cases to ensure that
doctors can administer timely treatments. The smart
Oura Ring continuously monitors body temperature,
allowing early detection of COVID-19 cases, leading
to earlier insolation and testing to curb the spread of
the infectious disease (Oura, 2020). “The Gen2 Oura
Ring uses infrared (IR) LED technology, and does not
include SPO2 monitoring, whereas the Gen3 Oura
Ring includes red and green LED, in addition to the
IR one, and will include SPO2 as a future feature.”
Another significant value that is monitored is the
HRV. Not everyone is aware of this value, but it is
essential in describing a person’s state of stress or
well-being. HRV is closely related to the autonomic
nervous system, and its variation is fundamental for
proper functioning of the parasympathetic system. A
low HRV indicates low reactivity of the parasym-
pathetic system and a longer recovery from physical
and emotional exertion. Cognet Things proposes the
COVID-19 WorkSafr Tracker, “a smart wearable
device with multi-faceted solutions for COVID-19
tracking and tracing, to improve the patient safety and
provide actionable insights” (Fig. 7b) (Cognet Things
Inc., 2020). “The device allows real-time warnings,
monitoring, and incident reporting, essential in
monitoring COVID-19 patients and location tracking
and tracing with geo-fencing. The COVID-19 Patient
Fig. 6 Smart patch developed by LifeSignals Co. for mon-
itoring patients’ main vital signs (a) and a wearable ther-
mometer “Celsium” produced by the Smartr Health
company, placed in the armpit, to remotely monitor the
user’s temperature (b) (Celsium, 2020)
(a) (b)
Fig. 7 ECG Alert wearable device (ECG Alert, 2020)
(a) and WorkSafr wearable device (Cognet Things Inc.,
2020) (b)
(a) (b)
de Fazio et al. / Front Inform Technol Electron Eng 2021 22(11):1413-1442
1420
Tracker automatically detects temperature, HR, and
blood oxygen saturation automatically.”
8 West has proposed the COVID-19 remote early
warning (CREW) system to aid healthcare staff who
works on the frontline (8 West, 2020). The CREW
system includes: (1) a wearable digital thermometer
sensor for detecting body temperature, (2) a sensor
platform, e.g., smartphone, smartwatch, or wearable
IoT device running the CREW App, and (3) a
cloud-based server running the CREW system that
monitors the incoming data and generates automatic
alarms if temperature thresholds are breached.
The CREW system regularly acquires body
temperature and triggers alerts if a temperature
threshold has been breached. The system consists of
sensors, smart devices, and a cloud-based monitoring
and alerting system. The system has been tested by
the medical staff of the Emergency Department of
Cork University Hospital (CUH). By integrating
wearable technologies and collecting data from
different sensors in an intelligent and scalable
monitoring solution, 8 West believes that a solution
can reassure frontline healthcare workers and provide
useful data to hospitals and treatment centers related
to their valued staff (Fig. 8).
The Vital Patch, manufactured by VitalConnect
Co., is a wearable device for remotely monitoring the
health condition of the patient who wears it
(MediBioSense, 2020) (Fig. 9a). The device detects,
in real time, eight biophysical parameters (body
temperature, HR, HRV, body posture, RR, single ECG,
fall-detection, activity level), and wirelessly transmits
the acquired information to the patient-monitoring
platform for storage and analysis. The self-isolation
and social distancing related to the COVID-19
pandemic are inducing several psychological problems,
like high stress level, anxiety, and panic attacks,
which are just as insidious as the virus consequences.
Lief Therapeutics proposed a wearable device, called
Lief Rx, which “monitors HR and HRV, extracts
information about psychophysical conditions, and
provides biofeedback to the user to reduce their
anxiety” (Lief Therapeutics, 2019). Similarly, Skiin
has presented a wide range of garments (underwear,
bras, shirts, and sleep masks) equipped with several
sensors to continuously monitor vital signs, like sleep
quality, activity, temperature, and ECG levels
(Fig. 9b).
2.2 Overview of innovative masks for limiting the
spread of COVID-19
Currently, several innovative masks have been
developed and proposed for the market that can
perform their filtering function and detect biophysical
parameters. AirPoP Co. patented the Active+ Halo
mask, “which consists of a flexible filtering
membrane that adapts to the user’s face ensuring a
99.3% particle filtration efficiency (PFE) and 99.9%
bacterial filtration efficiency (BFE)” (AirPoP Co.,
2020). The innovative mask integrates a sensor array
to detect breath parameters and send them to a mobile
App, and an onboard LED signals the breath rate.
Also, LG Group is developing the PuriCare Wearable
Air, “a smart mask equipped with two high-
performance filters that capture up to 99.5% of virus,
bacteria, and particles” (Fig. 10a) (LG Group, 2020).
Thanks to a double fan and an RR sensor, the smart
mask provides fresh and purified air to the user; the
Fig. 8 Overview of the main components included in the
CREW system: (a) sensor platform; (b) wearable digita
l
thermometer sensor; (c) cloud platform (8 West, 2020)
(c)
(b) (a)
Fig. 9 (a) Vital Patch, manufactured by VitalConnect Co.,
monitors biophysical parameters (MediBioSense, 2020);
(b) the smart garment, produced by Skiin, has differen
t
sensors to monitor the user’s vital signs (Skiin, 2019)
(b) (a)
de Fazio et al. / Front Inform Technol Electron Eng 2021 22(11):1413-1442 1421
sensor detects all the phases of the RR and the
respiration volume, and regulates the fan speed
accordingly. The fan automatically accelerates to
support air inhalation and decelerates to reduce
resistance during expiration. The mask case also
includes a wireless recharging system and ultraviolet
(UV) LEDs to eliminate germs and viruses.
Similarly, Razer has created project Hazel, “a
smart mask that ensures N95 medical-grade
respiration protection” (Fig. 10b) (Razer, 2020). It
includes an active ventilation system that adjusts
incoming airflow as a function of the respiration
parameters detected by a built-in microphone and
speaker, which are also used by the mask to
understand when the user wears the mask. Also, in
this case, a cover is equipped with a wireless
recharging system and UV lamp for the auto-
sterilization function to kill bacteria and viruses. Also,
a University of Leicester research team developed a
3D printed face mask that can be created in just
30 min, and is thus suitable for mass production. It
must be printed using Copper3D filament, that is, a
polylactic acid (PLA) filament loaded with copper
nanoparticles (NPs), which make the mask
antimicrobial, antiviral, reusable, and eco-sustainable
(NanoHack 3D, 2020).
Furthermore, Xiaomi has requested a patent for
“the Xiaomi Purely Mask equipped with several
sensors for monitoring respiration status in real time”
(Xiaomi, 2020) (Fig. 11a). Thanks to integrated
sensors, “the mask measures the amount of absorbed
pollutant, the air quality index (AQI), RR, and the
user’s movement using integrated accelerometers and
gyroscopes to determine variations of lung capacity.
The acquired data are preprocessed by an onboard
processing unit and then transmitted to a suitable
mobile application where the data are displayed,
processed, and stored.”
The Guardian G-Volt mask, produced by LIGC
Applications, is “a novel graphene-based filter
composed of a laser-induced graphene (LIG) layer
and a graphene foam obtained by CO2 laser cutting
technique” (Dezeen, 2019; Stanford et al., 2019). The
resulting filter can quickly reach a temperature higher
than 300 °C, exploiting the Joule-heating mechanism,
self-sterilizing the mask. The thermal stability and
relatively high surface of the LIG filter make it very
efficient in reducing infection in hospital settings. The
G-Volt mask features high filtering efficiency for
particles over 0.3 µm and 80% for smaller particles
(Fig. 11b). “The mask is periodically connected to a
portable power bank to destroy bacteria and virus
deposited on the surface, allowing its complete
sterilization.”
A University of California research team is
developing a wearable sensor that is applied to a mask
for detecting the presence of proteases, which are
enzymes that speed up the breakdown of proteins that
are related to the COVID-19 virus (Yim et al., 2020;
Labios, 2021) (Fig. 12a). The sensor includes a small
blister and a strip used to collect the proteases present
in the exhaled breath. To carry out a test, the user must
squeeze the blister, causing the NPs to contact the
strip surface, which changes color in the presence of
proteases and thus the COVID-19 virus. Harvard and
MIT researchers are developing a face mask that
generates a fluorescent emission if a person infected
Fig. 11 Xiaomi smart mask, produced by Xiaomi, fo
r
monitoring the respiration status in real time (Xiaomi,
2020) (a) and Guardian G-Volt mask, manufactured by
LIGC Applications, equipped with a self-sterilizing gra-
phene filter (Dezeen, 2019; Stanford et al., 2019) (b)
(a) (b)
Fig. 10 PuriCare wearable air smart mask developed by
LG Group (LG Group, 2020) (a) and a smart mask man-
ufactured by Razer and equipped with an automatic ven-
tilation system (Razer, 2020) (b)
(b) (a)
de Fazio et al. / Front Inform Technol Electron Eng 2021 22(11):1413-1442
1422
with COVID-19 breathes, coughs, or sneezes.
Ribonucleic acid (RNA) or deoxyribonucleic acid
(DNA) genetic material is deposited by a lyophilizer
on the fabric surface, which collects the aqueous
particles that carry the virus without killing them
(Fig. 12b). The virus binds with the deposited
material producing a radiative emission, not visible to
the naked eye but quantifiable with a fluorimeter. The
proposed solution requires two conditions for
activation: the sample’s moisture (saliva, mucus, etc.)
and the virus’s genetic sequence. Rabiee et al. (2020)
developed point-of-use rapid detection of the
COVID-19 virus in the form of a mask coated by
metallic NPs doped with an organo-metallic frame-
work; interaction with the virus changes the optical
properties of NPs, resulting in color variation of the
mask’s surface. The authors also provided an
overview of the different diagnostic methods and
techniques for rapid detection of COVID-19 using
optical techniques that exploit the easy absorption/
desorption of the nanostructured materials (i.e., gold,
silver, magnetic, and metal-organic NPs) (Ghasemi et
al., 2015; Nejad et al., 2020; Rabiee et al., 2020). The
aim is to develop point-of-care solutions that identify
the presence of a virus or even its concentration in the
air in a rapid and non-invasive way (de Fazio et al.,
2021). Giovannini et al. (2021) investigated different
techniques and critical technical aspects of detecting
virus by analyzing exhaled breath, including electro-
chemical, chemoresistive, biological gas sensors, or
the breath’s liquid phase (i.e., exhaled breath aerosol
(EBA) or exhaled breath condensate (EBC)) using
polymerase chain reaction (PCR) based detection
methods.
2.3 Study of implantable devices for detecting the
effects of COVID-19 on the human body
Recent studies have been aimed at determining
the effects of COVID-19 on the human body; in fact,
the data suggest a correlation between cardiovascular
diseases and COVID-19, emerging several months
after negative virus testing. Particularly, several
infected patients show acute cardiovascular events in
addition to other complications correlated with
COVID-19 (Diller et al., 2020; Paramasivam et al.,
2020). Implantable cardiac monitoring devices are a
smart solution to remotely check the cardiac muscle’s
integrity and functionality, avoid long hospital stays,
better respect a fixed schedule of exams, and rapidly
detect the onset of deteriorations in clinical conditions.
The COVID-19 pandemic has significantly changed
medical activities inside the hospital; the main
objective is to reduce the number of individuals
accessing the hospital facility to minimize the
contagion risk for patients and caregivers.
The introduction of remote monitoring of the
patient’s cardiovascular condition was proposed by
Mabo et al. (2012), who reported the main results of
the COMPASS trial, a randomized trial for long-term
remote monitoring of patients implanted with a
pacemaker. “Home Monitoring®, produced by
Biotronik, has been employed to automatically
transmit the data acquired by implantable devices to a
secure Internet site, where the medical staff analyzes
the data to determine the patient’s status” (Biotronik
Inc., 2020). The COMPASS trial involved 538
patients, who were randomly selected for remotely
monitoring follow-up observation for 18.3 months.
The authors demonstrated that there was a change in
pacemaker programming or drug therapy in 62% of
cases, versus 29% in the control group. Remote
monitoring is a safe and efficient solution for
long-term follow-up of permanently paced patients,
reducing healthcare costs and the risk of hospital
overcrowding.
Furthermore, Versteeg et al. (2014) analyzed the
influence of remote patient monitoring systems on the
outcome of patients’ clinical course. The study, called
REMOTE-cardiac implanted electronic device
(CIED), considered 900 patients affected by cardiac
diseases and with an implantable cardioverter-
defibrillator, monitored every 3–6 months over a
2-year time period. The results obtained demonstrated
Fig. 12 A sensor developed by the University of Califor-
nia which can detect the presence of the proteases related
to the COVID-19 virus in exhaled breath (a) and details
of the sensor before application on the mask (b) (Yim et
al., 2020; Labios, 2021)
(b) (a)
de Fazio et al. / Front Inform Technol Electron Eng 2021 22(11):1413-1442 1423
that remote patient monitoring could help implement
a centralized care monitoring system. An increase in
intracardiac pressure is the main indicator of a
worsening heart condition and is crucial for early and
rapid intervention, which reduces the probability of
complications. “CardioMEMS HF®, manufactured by
Abbott, is a wireless monitoring platform for
detecting variations in pulmonary artery pressure, an
indicator of heart condition” (Sandhu et al., 2016;
Abbott Inc., 2021). The system allows real-time
notification of the patient’s conditions, secure data
analysis and access, and tailored management of user
follow-up. The implantable micro-electro-mechanical-
system (MEMS) device is battery-free, but it uses
radio frequency (RF) technology to acquire and
transmit pulmonary pressure data, exploiting the
energy provided by a proprietary electronic
monitoring system (Fig. 13a).
The sensor includes a coil and pressure-sensitive
capacitor enclosed in a silica cover and two nitinol
loops to hook it up to the pulmonary artery branch
(Fig. 13b). The capacitance value depends on BP and
varies with the resonance frequency accordingly; the
frequency shift signal is processed by the electronic
section to extract the pressure waveform. The
acquired data are then sent to a secure database and
are available to the medical staff on the CardioMEMS
proprietary website. The clinical test was reported in
the CHAMPION trial, which considered 550 heart
failure (HF) individuals, demonstrated a great
reduction in hospitalization time, and showed the
device ability to monitor hemodynamic parameters of
the HF patient in real time (Givertz et al., 2017).
Similarly, Endotronix Inc. (Chicago, IL, USA)
“has developed the Cordella system, suitably
designed for HF patients for continuous monitoring of
health conditions and remote treatment definition”
(Mullens et al., 2020; Endotronix Inc., 2021). The
Cordella system includes an implantable MEMS
pressure sensor designed to be placed inside the
patients’ pulmonary artery (Fig. 14a) and wirelessly
provide real-time data on pulmonary artery pressure
to a portable reader (Fig. 14b). The collected data are
then wirelessly transmitted to a cloud platform, where
the data are stored and analyzed by the company staff.
Furthermore, Walton and Krum (2005)
described the “HeartPOD system, manufactured by
Abbott Inc., to measure the left atrial pressure (LAP);
the system is composed of an implantable sensor
interfaced with a subcutaneous antenna coil, an
elaboration module, and a remote clinical software
platform. The sensing section does not have a battery,
instead is powered by an external telemetry coil and
positioned across the inter-atrial septum via
trans-septal catheterization. The elaboration module
receives and processes the telemetry data and
provides patient feedback about the frequency of BP
measurements.”
V-LAPTM, produced by Vectorious Medical
Technologies, is a miniaturized, wireless, battery-free
monitoring system for providing detailed information
concerning the LAP (Perl et al ., 2019; Vectorious Inc.,
2021). “The implant must be positioned directly on
(a) (b)
Fig. 14 Positioning of the Cordella sensor, produced by
Endotronix Inc., inside the pulmonary artery to monito
r
blood pressure (a) and details of the Cordella sensor (b)
(Mullens et al., 2020)
5–10 nm
Fully biodegra dable
Capacitor
inductor
Variatio n
diel ectric
prop erties
sens ing layer
External
stimulus
Detection signal with
external coil varia tion
resona nt f requency peak
Sub strate
Pack aging (wit h open
top cavity where the
sensing lay er is located)
Sen sing la yer
RLC resonator
CardioMEMS
sensor
(a)
(b)
Fig. 13 Positioning of MEMS sensors inside the pulmonary
artery branch (a) and resonant detection circuit employed
by MEMS sensors to detect pressure variations (b)
Reprinted from Sandhu et al. (2016), Copyright 2016, wit
h
permission from Elsevier
de Fazio et al. / Front Inform Technol Electron Eng 2021 22(11):1413-1442
1424
the heart’s interatrial septum (Fig. 15a) and wirelessly
transmits daily hemodynamic data to a cloud
platform.” The implant includes three components
(Fig. 15b): (1) a pressure cup, including the MEMS
pressure transducer, positioned on the left atrial side,
(2) a sealed tube containing the electronic section and
the transmission module, and (3) a braided scaffold,
made of nitinol, to anchor the implant to the heart wall.
When opened, the fixation scaffold has an
18-mm diameter on the left atrium and a 16-mm one
on the right atrium; the implant features a maximum
diameter of 3.8 mm and a length less than 18 mm. The
system includes an external unit to supply power to
the implant, wirelessly receives the data every day,
and shares data with the cloud platform (Fig. 15c).
3 Overview of commercial wearable solu-
tions for complying with social distancing
rules
In this section we investigate innovative
wearable applications for complying with the new
measures to contain the COVID-19 pandemic,
especially in terms of social distancing. In particular,
with the global outbreak of the COVID-19 pandemic,
several companies have addressed their efforts at
developing solutions that can trace the contacts of
infected users in previous weeks. Several standard
communication technologies (Bluetooth Low Energy
(BLE), Wi-Fi, Long-Term Evolution (LTE), etc.) are
employed, using properly defined metrics, to
determine and track the duration and intensity of the
social contact. Furthermore, several wearable IoT
solutions have been proposed to remotely monitor
biophysical conditions and detect symptoms
commonly associated with COVID-19 early (cough,
shortness of breath, low SpO2 level) (Calabrese et al.,
2020; de Fazio et al., 2020b; Grant et al., 2020;
Larsen et al., 2020; Seshadri et al., 2020; Visconti et
al., 2020).
The Close-to-me device, manufactured by
Partitalia Srl, is a device worn by two or more people
in the same room, and guarantees one meter of “social
distance” (Partitalia, 2020a). The device is based on
RF technology and generates a low-frequency radio
bubble around the user, which is not invasive (Fig. 16).
When the distancing requirement is not satisfied,
there will be an acoustic sound and a vibration signal
notifying the user that he/she is less than a meter away.
Furthermore, through simple implementations, the
device can be used for access control, attendance
detection, and payment to the company canteen.
Close-to-me can be customized and purchased as a
bracelet or as a key ring, thus remaining a non-
invasive device, lightweight, and almost maintenance-
free. It is aimed at simplifying the procedures related
to the reopening of companies because it can be easily
implemented quickly.
The VITA wearable device, on the other hand, is
designed for constant observation of vital parameters
in patients who are treatable by telemedicine, in all
cases in which it is considered essential to highlight a
possible infection (Partitalia, 2020b). The device is
equipped with a high-efficiency battery that lasts
more than two weeks. It also integrates different
sensors for detecting HR, oxygen saturation, and
body temperature, and performs an ECG. Thanks to
these features, wearables can be used to monitor
biophysical conditions of long-term patients, as well
as employees in the workplace.
Close-to-me
1 m
The we arable de vice for
social distancing
(a) (b)
Fig. 16 Graphic representation of the Close-to-me system
(a) and front view of the wearer to monitor social dis-
tancing (b) (Partitalia, 2020a)
Fig. 15 V-LAPTM implant for remotely monitoring the left
atrial pressure (a), its main sections (b), and the V-LAPTM
external unit (c) (Vectorious Inc., 2021)
Reprinted from Perl et al. (2019), Copyright 2019, with per-
mission from Springer Nature
(c)
(b) (a)
de Fazio et al. / Front Inform Technol Electron Eng 2021 22(11):1413-1442 1425
Safe Spacer™ is a patent-pending wearable
device that helps users keep social distance within
safe limits by accurately detecting when other devices
are within a 2-m radius and warning the user with
visual, vibrating, or audible alarms (Safe Spacer, 2020)
(Fig. 17). Ultra-wideband (UWB) technology delivers
10 times more accuracy than Bluetooth for superior
performance, allowing companies to safely reopen
the workplace and help stop the spread of COVID-19.
In addition, the device emits visual, acoustic, and
vibratory warning signals to alert the user of a
colleague’s presence at a close distance. Safe Spacer
can be comfortably worn by users on a bracelet,
lanyard, or keychain and features a unique
identification (ID) tag and built-in memory that
allows contact tracing in the event of coronavirus
exposure, keeping organizations safe.
Similarly, Abeeway (2019) developed a Smart
BadgeTM to aid the user in respecting the social
distancing rules in the workplace. It is lightweight and
portable, is equipped with sensors that support
multiple geolocation technologies, and provides
accurate and continuous geolocation data. The device
uses a BLE beacon, multi-constellation Global
Navigation Satellite System (GNSS)/Global
Positioning System (GPS), low-power GPS, and
Wi-Fi technologies to detect other workers’ proximity,
and evaluates the distance between them. Also, an
ultra-low-power LoraWAN (wireless area network)
communication module is employed and dynamically
manages the localization technology as a function of
the operating scenario to optimize the device
autonomy (de Fazio et al., 2020a). CrowdLED Inc.
has launched the CrowdRanger social distancing
wearable device, based on RF technology to sense
when another device is within a set range (Crowd-
saver, 2020). If this condition is verified, the device
generates a visual, acoustic, and vibratory warning
signal to maintain social distancing, increasing the
audio volume, warning color, and vibration intensity
depending on the distance and contact duration. The
alarm stops when the two users move beyond the safe
distance. The device records the duration and distance
of each contact up to 28 d, allowing the administrator
to check the data to identify potentially infected
people.
Nymi Inc. has developed a workplace wearable
wristband based on near-field communication (NFC)
and BLE technology for contact tracing and
social-distancing applications (Nymi Inc., 2021).
Specifically, the device uses these technologies to
determine and quantify social contacts, providing
suggestions to the administrators concerning work-
place safety. At the end of the day, each wristband
sends the acquired information to a central unit to
determine potential risks and worker behaviors. The
device identifies the user via an integrated fingerprint
reader and biometric parameters to ensure the safety
and security of connected workers.
The iFeel-You bracelet is also equipped with
sensors to monitor human parameters and warn the
user if his/her body temperature is higher than 37.5 °C
(Italian Institute of Technology, 2020) (Fig. 18a).
Furthermore, using Bluetooth frequencies, the device
monitors the distance between people and detects
movements and the distance between bracelets. Once
two bracelets are too close, they vibrate and sound,
making the wearer aware and ready to keep a safe
distance. Similarly, Estimote, which specializes in
beacon location devices, used its skills to develop a
device to contain the COVID-19 pandemic (Estimote,
2020) (Fig. 18b). A new line of wearable products
was proposed to monitor the coronavirus diffusion
potential between users in the workplace. These
devices represent a powerful solution to keep track of
any potential contagion between workers and limit
the local spreading of the disease before it becomes
uncontrollable. The hardware section consists of a
passive GPS receiver and proximity sensors based on
Bluetooth and ultra-wideband radio signal analysis, a
rechargeable battery, and a built-in LTE transceiver. It
also includes a manual control to modify the user’s
health condition, and assigns to the user the certified
state of healthy, symptomatic, or COVID-19 infected.
If the wearer’s status changes, indicating possible or
(a) (b)
Fig. 17 Safe Spacer™ wearable sensor (a) and an example
of an application scenario (b) (Safe Spacer, 2020)
de Fazio et al. / Front Inform Technol Electron Eng 2021 22(11):1413-1442
1426
verified infection, the system also updates others with
whom the wearer has been in contact depending on
proximity and location-data history. The status is also
updated in a health dashboard that provides detailed
logs of possible contacts for centralized management.
4 Tracing systems for containing the COVID-
19 pandemic
In this section we analyze current technologies
(including Apps, integrated sensors, and ad hoc
devices) to overcome the problems arising from this
pandemic. The focus is on the various technological
approaches that could be applied to break down
infection and return to everyday life.
Immuni (2020) focused on the Immuni App. It
introduces a new approach for containing epidemics,
starting with COVID-19. The App has a contact
tracing feature based on Bluetooth technology. When
users discover that they have tested positive for the
COVID-19 virus, the Immuni notification system
allows them to anonymously alert people with whom
they have been in close contact and who may also
have been infected. By being promptly informed
(potentially even before developing symptoms), users
can contact their general practitioner (GP) to discuss
their situation. It is available for iOS and Android
operating systems. The source code was developed by
Bending Spoons S.p.A., and released under the GNU
Affero General Public License version 3. Immuni can
determine that a risky exposure has occurred between
two users without knowing who those users are or
where they met. The App does not collect any data
that would identify the user, such as name, date of
birth, address, telephone number, or email address. To
determine the contact, Immuni uses BLE technology
(a variant of the standard Bluetooth that uses much
less power for communication) and does not use
geolocation data of any kind, including GPS data.
Immuni has paid great attention to safeguarding user
privacy during its design and development. The data
are collected and managed by the Ministry of Health
and stored on servers located in Italy. All App data
and connections with the server are protected. Also, to
ensure that only users who test positive for the
SARS-CoV-2 virus can upload their keys to the server,
the upload procedure can be performed only in
cooperation with an authenticated healthcare provider.
The provider asks the user to provide a code
generated by the App and inserts it into a back-office
tool.
A high-level description of the system is as
follows. Once installed and configured on a device
(device_A), the App generates a temporary exposure
key (randomly generated and changed daily). The
App also starts transmitting a BLE signal that
contains a proximity identifier (ID_A1, which is
assumed fixed for simplicity). When another device
(device_B) using the App receives this signal, it
registers ID_A1 locally in his/her memory. At the
same time, device_A records the identifier of
device_B (ID_B1, which is also considered fixed).
Suppose that the user of device_A subsequently tests
positive for SARS-CoV-2. In this case, he/she can
upload the temporary exposure keys to the Immuni
server. The Immuni App can obtain the recently
transmitted proximity identifiers from the server
(including ID_A1 of device_A). Device_B checks its
local list of identifiers for new keys uploaded to the
server, and if ID_A1 matches, the App warns the user
of device_B that he/she may be at risk and provides
advice on what to do next (e.g., isolate and call the
doctor) (Fig. 19).
If the owner of device_B is in proximity to the
user of device_A, there is no certainty that he/she is at
risk. Immuni evaluates this risk based on the distance
between the two devices and the duration of exposure,
which is estimated from the attenuation of the BLE
signal received by device_B. The longer the exposure
and the closer the contact, the greater the transmission
risk. Generally, an interaction lasting only a couple of
minutes and occurring several meters away is
considered low risk. However, the risk model may
evolve as more information on the transmission
(a) (b)
Fig. 18 iFeel-You bracelet developed by the Italian Insti-
tute of Technology (Italian Institute of Technology, 2020)
(a) and line of wearable products manufactured by Es-
timote Co. for containing the COVID-19 pandemic (Es-
timote, 2020) (b)
de Fazio et al. / Front Inform Technol Electron Eng 2021 22(11):1413-1442 1427
properties of SARS-CoV-2 becomes available. Note
that the distance estimation is affected by different
errors. The BLE signal attenuation depends on factors
such as the relative orientation of the two devices and
the presence of obstacles (including human bodies).
Also, note that the App has been heavily downloaded
and used since it was released (Fig. 20).
Wearable and tracking solutions can be used to
prevent cross-infection among individuals, and
provide measures of temperature, HR, blood oxygen
saturation, and real-time positioning information.
Singapore was the first country to try to contain the
COVID-19 pandemic using a tracing App.
TraceTogether Tokens represents an intelligent
solution to address COVID-19 using technology
(TraceTogether Tokens, 2020). The local authorities
declare that the application has quickly received wide
popularity and covers 2.1 million people, representing
35% of the population. The system includes wearable
devices that support the contact tracing App in
identifying individuals potentially infected by users
who tested positive for COVID-19 (Fig. 21a). The
wearable devices allow thousands of vulnerable
elderly people who do not have a smartphone to be
covered by the tracking system. Each user is assigned
a national ID that is used by the TraceTogether App
for the tracking process; if a user tests positive for
COVID-19, he/she must report his/her token to local
health authorities because he/she is not able to
transmit data over the Internet. The tracking system
uses the log data to identify and warn others who
might have been infected. The mobile App correlates
the verified infected user with those who have been in
contact with him/her. The App uses a time-sensitive
ID to determine the user, and the relative signal
strength indicator (RSSI) readings between two
smartphones to identify the proximity and duration of
the contact between two users (Fig. 21b).
Simmhan et al. (2020) presented another contact
tracing App, called GoCoronaGo (GCG), which
exploits the potential of BLE technology. The authors
developed and analyzed the first experiments after
distributing the App to more than 1000 users at the
Indian Institute of Science campus in Bangalore. This
App uses BLE technology and includes a unique
device ID, called a contact, which is recognized by
the other nearby devices with App scanning. The App
stores information on the local device. If a user
verifies his/her COVID-19 positivity, the Bluetooth
contacts are uploaded to a central database and
contacts are notified. This mechanism can drastically
reduce the time needed to track contacts and slow the
spread of the virus.
The main limitation of the Bluetooth technology
is the low reliability and asymmetry in detecting
nearby users and the low accuracy in distance
Fig. 19 Graphical representation of how the Immuni App
works (Immuni, 2020)
Fig. 20 Graphs and histograms reporting some Immuni
App statistics (Immuni, 2020)
Fig. 21 Schematic representation of the TraceTogethe
r
tracking system (a) and mobile application used to support
the tracking system (b) (TraceTogether Tokens, 2020)
(b) (a)
de Fazio et al. / Front Inform Technol Electron Eng 2021 22(11):1413-1442
1428
measurement. The high number of adoptions
necessary for contact tracing to be effective leads us
to believe that it is still important to use
complementary digital contact tracing with manual
methods. The proposed App (GCG) for digital
tracking aims to solve these limitations. The key
feature of the proposed approach is the collection of
the device contact trace data in a centralized database,
regardless of whether the person is diagnosed as
positive or negative for COVID-19. The proximity
data collected from all App users are used to create a
time contact chart. The vertices are devices, and the
edges indicate the proximity between devices for a
certain period and with the given Bluetooth signal
strength. According to the WHO guidelines, when a
user of the GCG App becomes positive for
COVID-19, graphical algorithms quickly identify the
primary, secondary, and other contacts. Also, even if
the infected user has lost the Bluetooth connection,
successful scans from other nearby devices can alert
the relevant contacts, increasing detection reliability.
Of course, centralized contact data collection
has some drawbacks, specifically, the privacy
implications of tracking interactions. However, the
system implements some precautions to try to remedy
this inconvenience:
1. The App is designed not on a municipal,
regional, or national scale, but only for distribution
within institutions and closed campuses; therefore,
the data collected are owned by the host institution
and not by a central authority.
2. Users are not required to share any personal
information, and the devices are identified using a
randomly generated ID.
From an architectural perspective, Fig. 22 shows
a high-level diagram of the developed traceability
system. Authorized institution users are provided
with individual invitation codes by a separate entity
within the institution, typically the information
technology (IT) department. The office maintains a
mapping from the user’s unique invitation code to the
actual individual along with their contact details
(Fig. 23).
The user can download the GCG App from an
institutional link or the Google Play Store. During
installation, users enter the invitation code in the App,
which validates it with the GCG backend servers and
returns a unique user ID, a personal identification
number (PIN), and the device ID. Other required
information collected by the App during installation
is the operative system version and phone model. This
information identifies the strength of the Bluetooth
signal and translates it into a distance estimate. Thus,
the GCG App acts as both a client and a server when
using BLE scanning and advertising modalities.
In addition to tracking Bluetooth contact data,
the GCG App offers several functionalities to inform
users about COVID-19 and engage them in
preventing its spread. Screenshots of these user
interface elements are shown in Fig. 24.
Unfortunately, COVID-19 has many strange
long-term symptoms; for example, many symptoms
disappear entirely before they suddenly start to
worsen, and other patients who are declared negative
are later positive again. The high number of strange
cases highlights the need for continuous monitoring
of patient health. For these reasons, the device
proposed by Jeong H et al. (2020) provides round-the-
clock monitoring of COVID-19 patients and those
exposed to them. The device can monitor hospitalized
patients and continue supervision at home. The device
Advert ise dev ice I D
Scan for device IDs
Cloud-hosted
GCG services Interface with
hea lth center
Store
data
Acquire
data
Send
alerts
Ana lyze
data
Upload proximity
data periodica lly
GCG App
on sma rt phones
Fig. 22 Graphical representation of the architecture of the
GCG traceability system
Reprinted from Simmhan et al. (2020), Copyright 2020, wit
h
permission from Indian Institute of Science
Fig. 23 Identifier mapping during GCG App installation
Reprinted from Simmhan et al. (2020), Copyright 2020, wit
h
permission from Indian Institute of Science
de Fazio et al. / Front Inform Technol Electron Eng 2021 22(11):1413-1442 1429
can monitor the progress of COVID-19 patients, and
provide early warning signals to frontline workers
who are more at risk of contracting this disease.
Girolami et al. (2020) analyzed the wireless BLE
signals commonly used by commercial mobile
devices. Their work is based on the SocializeME
framework, which is designed to collect proximity
information and detect social interactions across
personal and heterogeneous mobile devices. The
experimental results, obtained by several measure-
ment tests on real users, highlighted the technical
limitations and qualitative performance of the
proposed technique in terms of the received signal
strength (RSS), packet loss, and channel symmetry
for different body positions. Specifically, they
obtained a dataset with thousands of Bluetooth
signals (BLE beacons) collected over 11 h. They
analyzed the results obtained by the SocializeME
Detector (SME-D) algorithm, which is designed to
automatically detect social interactions based on
collected wireless signals; an overall accuracy of
81.56% and an F-score of 84.7% were achieved. The
proposed work aims to design software and analytical
tools that can detect face-to-face interactions without
adopting customized hardware and provide users with
a non-invasive technological solution (hardware/
software). An essential aspect of this work concerns
the SocializeME solution that uses BLE technology,
allowing mobile devices to transmit advertising
information and capture their intentions.
The first version of the SocializeME App was
developed and finalized to advertise the presence of a
local device, and thus its owner, to other devices
within a circle with a radius of a few meters and store
the information of the received third-party advertising
packages. The App presents some drawbacks. For
example, only Android devices support background
scanning and advertising modes; in contrast, iOS
automatically stops advertising and scanning
operations while the App switches to the background.
Various experimental sessions were conducted to
analyze the impact of user position on signal quality
and, consequently, on the correct detection of
face-to-face interactions in a completely realistic
context. The tests were carried out based on a
face-to-face interaction characterized by three
different physical distances between the participants:
(1) non-interaction (3–3.5 m), (2) approach (3–2.5 m),
and (3) interaction (2.5–1 m).
Table 1 summarizes the results of their
experimental activity. The table shows, for each
session, the number of volunteers, the number of
different smartphone models, the number of beacons
collected, and the overall duration of the session tests.
Then, the duration column provides the average time
it took for volunteers to complete a specific session.
Table 1 Experimental results reported in Girolami et al.
(2020)
Session Number of
volunteers
Number of
smartphone
models
Number of
beacons
Duration
(min)
1 8 4 53 375 111
2 9 6 87 467 114
3 10 4 205 152 111
4 8 5 193 603 130
5 8 3 247 776 130
6 6 4 27 886 73
Total 49 26 815 259 669
(a) (b) (c)
(d) (e) (f)
Fig. 24 User interface and analytics in the GCG v0.7 An-
droid App: (a) main screen; (b) hourly contacts; (c) hourly
scans; (d) device density heatmap; (e) contact network;
(f) alerts panel
Reprinted from Simmhan et al. (2020), Copyright 2020, wit
h
permission from Indian Institute of Science
de Fazio et al. / Front Inform Technol Electron Eng 2021 22(11):1413-1442
1430
The SocializeME framework relies on the
analysis of beacon messages collected in their
experimental campaign (Girolami et al., 2020). In
detail, the authors used the RSS value experienced by
each pair of users involved in an interaction and the
beacon loss rate as the main parameters of their
algorithm called SME-D. This framework analyzes
the time series of beacon messages received by each
dyad using a sliding time window of predefined
duration (Δup) and evaluates the following conditions
to identify the start time of the social interaction: (1)
at least a given percentage (p%) of the expected
beacons are received; (2) the RSS of received beacons
is greater than or equal to a threshold value Trss.
If the two conditions are met in at least one of the
dyad’s two directions, social interaction is determined
to have started in that time window. Therefore, the
interaction is considered active until the closing
condition is detected. Namely, the time interval
between the last beacon received (with RSS≥Trss) is
greater than or equal to Δdown.
In Ashraf et al. (2020), a smart edge surveillance
system was proposed to monitor, alert, and detect the
user’s heartbeat, body temperature, heart conditions,
heart frequency, and some of the radiological
characteristics, to detect infected (suspected) persons
who use the wearable smart gadgets. Thanks to the
proposed framework, a continuously updated map/
diagram of the contact chain of people infected with
COVID-19 was also created. Thus, the proposed
model helps detect and trace the contagious person
and retain the patient data record for analysis.
To collect data, they used two main modules
(wearable and non-wearable); the wearable one
includes an HR sensor and an infrared temperature
sensor (Fig. 25a). The non-wearable module is
connected to the file entrance of the airport passage
gates or even in shopping malls where large human
crowds are expected. According to WHO guidelines,
the wearable module provides BP and respiratory data
of a person suspected of having COVID-19, to control
infected users (Fig. 25b). This mechanism acquires
real-time values (from sensors in wearable and
non-wearable gadgets) and transmits them to the file
multi-edge layer nodes, where the possible COVID-
19 users’ data are analyzed. The proposed framework
consists of five stages (Fig. 26):
1. Preliminary phase: the data related to possible
COVID-19 cases are transmitted to the edge and
cloud layers.
2. Central HUB (data processing and analysis):
the data are processed and passed on to the action
trigger and graphical mapping unit for nearby alarms
and affected authorities.
(a) (b)
Arduino+ Ra sp.
shield
BP
meter
(KODE A)
E-health
shield
Arduino Lilly pad
Pulse
sensor
IR temp.
sensor
Fig. 25 Setup of wearable (a) and non-wearable (b) devices
Reprinted from Ashraf et al. (2020), Copyright 2020, with
permission from IEEE
Prelimi nary st age
Wearab le device s
Non-w earab le dev ices
Telco a uthorities
Central HUB
Data computing &
analytic s
Map nodes to graph
transfusion
Report to concerned autho rities
Recomme ndations &
ass istance
Notifications and
awaren ess
Trac kin g a nd trac ing of
suspected persons
Mobile
App
1234
5
Fig. 26 Block diagram of the proposed system framework
Reprinted from Ashraf et al. (2020), Copyright 2020, with permission from IEEE
de Fazio et al. / Front Inform Technol Electron Eng 2021 22(11):1413-1442 1431
3. Action activation: this stage deals with the
transmission of notifications of possible COVID-19
cases.
4. User interface: this stage interacts with the end
user through an Android application.
5. Graphic display: this component summarizes
the framework’s results in a suitable graphical
interface, allowing the suspected COVID-19 nodes
(users) to be tracked down.
In Tripathy et al. (2020), an electronic solution,
called EasyBand, was presented to manage a safe and
gradual opening after removal of restrictions imposed
to contain the spread of COVID-19. In particular, it
was intended to limit new positive cases through
automatic contact detection and encourage social
distancing. The device includes sensors for detecting
similar nearby devices (from 1 to 4 m), helping
citizens stay safe by automatically detecting possible
COVID-19 cases. The EasyBand electronics consists
of specific building blocks such as a power
management unit (PMU) to provide adequate direct
current (DC) voltage to all the internal blocks and
liquid crystal display (LCD) display, a programmable
system chip (PSoC), wireless stack, sensors, vibration
motors, and I/O units (Fig. 27).
The system uses BLE devices to detect the
distance between them, and the Wi-Fi unit creates the
data connection with a city server via a Transmission
Control Protocol/Internet Protocol (TCP/IP)
connection. EasyBand also has LEDs with three
colors, yellow, green, and red. The green LED
indicates a safe condition, yellow a slightly suspicious
condition, and red a very doubtful one. These devices
can record information (such as device ID, timestamp,
and period) over 15 d for other devices in close
proximity in the same area. Before removing the lock
file from a zone, all people have to be released with an
EasyBand with an active green light as the mobility
pass. The device also provides a vibrating alert if a red
or yellow device is present within 4 m. It will beep to
provide an even more critical warning if it comes into
closer proximity with a yellow or red device. If a
green device spends a lot of time in close contact with
a yellow or red device, its status is changed to yellow
(Fig. 28).
Dong and Yao (2020) discussed some IoT
systems for user tracing using GPS receivers (Paek et
al., 2010), microphones (Satoh et al., 2013; Burns et
al., 2016), and magnetometers (Jeong S et al., 2019).
The simplest method uses GPS technology (based on
coordinates) to track users’ trajectories and determine
the contact distance (Paek et al., 2010). Although it is
feasible, it is also not optimal due to high power
consumption and low distance resolution.
One of these tracking systems was shown in
Jeong S et al. (2019), whose operative principle is
depicted in Fig. 29. Specifically, a magnetometer-
based method for contact tracing was proposed which
exploits linear correlations of smartphones and
magnetometer readings to estimate the distance
between phones and detect close contact events
between individuals.
RF-based signals, such as Bluetooth (Liu S et al.,
2014), Wi-Fi (Sapiezynski et al., 2016), and radio
frequency identification (RFID) (Bolic et al., 2015),
Power s upply
Power m anagement
Voltage re gulator
Battery charg er
Coin cell
Analog se nso rs
Ser ial flash
Motion sensor
Keypad LCD di spla y
Programmable system-on-chip
(PSoC)
Keypad
controll er LCD
controller
Serial
commu nicatio n LED, buzzer,
vibrator
Analog fro nt
end (AFE)
SD car d (n on-
detachable )
Output drive
(PWM)
Full speed
USB 2. 0
BLE stack Wi-Fi s tack
SPI
Wi-Fi tower
Red indic ator
Highly suspect ed
Yellow ind icator
Mild suspect
Green indicator
Healthy
Fig. 27 Block diagram of the EasyBand system
Reprinted from Tripathy et al. (2020), Copyright 2020, wit
h
permission from IEEE
Fig. 28 Contact graph obtained by the EasyBand system
before and after time T from a positive detection
Reprinted from Tripathy et al. (2020), Copyright 2020, wit
h
permission from IEEE
Before time TF ound posit ive at TAfte r time T
Graph: vertex typ e Edg e: Contact durat ion
EasyBand Careful
warning
EasyBand
R/Y
Critical warnin g
from suspec t
1.8 m/6 ft apart
Careful warning
from sus pect
4 m/13 ft apart
No warning
Critical warnin g
G
de Fazio et al. / Front Inform Technol Electron Eng 2021 22(11):1413-1442
1432
are also widely used to detect proximity. Liu S et al.
(2014) built a model based on Bluetooth signal
propagation to calculate distance values starting from
Bluetooth RSS values. This model enables a precise
distance resolution of 1 m. Using BLE and Wi-Fi, Liu
S et al. (2014) and Sapiezynski et al. (2016) proposed
robust and accurate systems to estimate the distance
between individuals with high resolution (<0.5 m).
Bolic et al. (2015) used the backscatter signals from
RFID tags to derive proximity with a small error of
0.3 m.
Farrahi et al. (2014) derived cellular communi-
cation traces from information provided by online
social networks, which are excellent indicators for
contact tracing. Gupta et al. (2020) imagined a smart
city and smart transportation system to ensure social
distance. Polenta et al. (2020) employed the Wi-Fi
and Bluetooth signals from IoT devices to determine
if two individuals respect social distances and
developed a web App for users to manage the
collected data. Other interesting research can be
found in Tedeschi et al. (2020), who proposed an
IoT-based distance estimation scheme (using BLE)
for tracing COVID-19 contacts, called IoTrace.
IoTrace adopts a decentralized model that addresses
the privacy disclosure issues of the user device’s
location and overload.
RFID technology can also represent a good
solution for implementing tracing and tracking
systems to contain the spread of COVID-19.
Rajasekar (2021) introduced a tracking and tracing
solution based on an IoT framework for detecting and
identifying social contacts using RFID technology
and a portable wearable reader. The author considered
the NFC protocol that allows a mobile phone to act as
a reader. The mobile App detects if and when another
tagged user is close, records and collects information,
and passes it to an edge device for processing. Once a
suspected case is identified, the people who have been
intercepted by the reader are alerted, through the
mobile App, of possible contagion, indicating that
user quarantine is in order. Garg et al. (2020)
developed a new IoT model, based on RFID
technology, for infection control and tracing of
COVID-19, preserving user anonymity (Fig. 30). The
model relies on decentralization of the blockchain to
detect and retrieve the chain data. It uses the
blockchain to store the data, which ensures anonymity
by using distributed ownership and control of the
stored data; the data are then processed to alert users
who have been in contact with confirmed infected
cases. A mobile application supports the proposed
model, and generates and stores the encryption key.
5 Performance comparison and critical
analysis between discussed technologies,
devices, and architectures
In this section, we report comparative analyses
of the different technologies, sensing devices, and IoT
frameworks discussed in the previous sections for
early detection of patients affected by COVID-19.
These devices are designed to avoid the spread of the
virus, help people comply with social distancing, and
trace contacts of infected tested users; this section
highlights the advantages, limitations, and potential
Smartphone A
(confirmed infected)
Contact
No contact
N
Y
Compute
correlation ρ
AB
Trace
A
B
Trace
Smartphone B
(susceptible)
ρ
AB
>θ ?
Fig. 29 Smartphone magnetometer based method fo
r
contact tracing and social distancing
Reprinted from Jeong S et al. (2019), Copyright 2019, wit
h
permission from the authors, licensed under CC BY
RFID reader
Internet
Blockch ain network
Smart phone
(with DApp )
Internet
Internet
Internet
Internet
Internet
RFID tag
GPS +Tag
ID matching
algorith m
Large-s cale
storage
provide rs
Ex te rnal sy ste ms
Notification
smart
contact
Internet
Fig. 30 Blockchain diagram for an anonymity-preserving
IoT-based contact tracing system based on RFID technolog
y
Reprinted from Garg et al. (2020), Copyright 2020, with per-
mission from the authors, licensed under CC BY
de Fazio et al. / Front Inform Technol Electron Eng 2021 22(11):1413-1442 1433
for each category.
In Section 2, we analyzed several wearable
devices that track patient health conditions and
identify the first symptoms of COVID-19 infections,
and thus suggested further user control to check the
actual contagion. Table 2 reports a comparison of
the multi-parametric sensing devices discussed in
Section 2, from the viewpoints of detected parameters,
application position, support of a cloud platform,
detection technologies, and invasiveness, to infer
the most promising tools for facing future
pandemics.
As can be noted, the smart plaster reported in
Jeong H et al. (2020), developed by researchers at
Northwestern University and Chicago’s Shirley Ryan
AbilityLab, is lightweight, small, and minimally
invasive, and allows continuous monitoring of patient
parameters and remote therapy determination, thus
avoiding increased pressure on health systems. The
Oura ring is a practical and ergonomic solution for
detecting the user’s principal vital signs, which is
significant in diagnosing diseases with a heavy
impact on the cardio-respiratory apparatus, like
pneumonia induced by COVID-19, while maintaining
very low invasiveness and high autonomy (Oura,
2020). The VitalConnect patch is the most complete
device in terms of the number of detected parameters,
and collects data related to conditions of the
cardiovascular apparatus, posture, and activity level.
The device has been extensively used to remotely
monitor patient conditions inside a hospital as well as
in everyday life, given its dimensions (i.e., 10 cm× 3
cm) and reduced flexibility. We believe that these
patch-type devices require more effort to reduce the
size and increase the use of flexible and bio-
compatible support materials.
In Section 2.2, we provided an overview of the
various models of innovative masks, which are useful
in limiting the spread of COVID-19, detecting
biophysical parameters (e.g., RR), and can be adapted
as a function of these parameters. The considered
devices are fully reusable and employ a sterilizing
mechanism (e.g., UV radiation and Joule effect), thus
reducing the environmental impact related to their
disposal, a critical issue that is emerging these days
(Fadare and Okoffo, 2020). Table 3 reports a
comparison of the different smart masks discussed in
Section 2.2 regarding filtration efficiency, detected
parameters, availability of self-sterilization and
forced ventilation systems, and wearability.
In our opinion, the G-Volt mask is the best
solution for fighting future pandemics given its high
filtration efficiency (i.e., 99%), good wearability, and
mainly the self-sterilization capability implemented
Table 2 Comparison of the multi-parameter sensing devices reported in Section 2, in terms of detected parameters,
application position, support for a cloud platform, detection technologies, and invasiveness
Device Detected parameters Application
position Cloud platform Detection
technologies Invasiveness
RespiraSense patch
(PMD Solutions,
2021)
RR Chest Yes Piezoelectric Medium
Jeong H et al. (2020)’s RR, HR, Body temp. Suprasternal notch Yes ECG, accelerometry Low
LifeSignals patch
(LifeSignals, 2020)
ECG, SpO2, BP, RR,
skin cond.
Chest Yes ECG, PPG, GSR,
accelerometry,
microphone
Low
Celsium thermometer
(Celsium, 2020)
Body temp. Armpit No NTC thermistor Medium
ECG Alert patch
(ECG Alert, 2020)
ECG, HR Chest Yes ECG Medium
Oura ring (Oura, 2020) HR, SpO2, HRV Finger Oura cloud PPG Low
VitalConnect patch
(MediBioSense,
2020)
Posture, HR, RR, body
temp., ECG, fall detec-
tion, activity level
Chest Vis ta TM (USA),
HealthStream
(outside USA)
ECG, PPG, GSR,
accelerometry
Low
Lief Rx patch (Lief
Therapeutics, 2019)
HRV Chest No ECG Medium
BP, blood pressure; ECG, electrocardiogram; HR, heart rate; HRV, heart-rate variability; PPG, photoplethysmography; RR, respiration rate;
GSR, galvanic skin response; NTC, negative temperature coefficient
de Fazio et al. / Front Inform Technol Electron Eng 2021 22(11):1413-1442
1434
using a graphene-based filter that exploits local
Joule-heating due to a small current flowing inside it
(Dezeen, 2019). Despite its advanced functionalities
(i.e., sensing capability and availability of the forced
ventilation system), the wearability of the PuriCare
Wearable Air mask (LG Group, 2020) is reduced due
to its non-negligible weight (i.e., 126 g).
In Section 3, we highlighted the importance of
complying with social distancing rules as a tool to
break the chain of contagion, thus containing
thespread of the pandemic; we explored several
commercial solutions to warn the user of the spread of
the pandemic and the proximity of another user.
Different technologies have been used to implement
such devices (like Bluetooth, BLE, LoraWAN, and
RFID) and affect their detection and reliability
performance. Table 4 summarizes the devices
discussed in Section 3, and compares them based on
detection technology, detected parameters, biofeed-
back typologies, wearability, and cost.
As can be noticed, the considered solutions are
very inexpensive, allowing companies to easily
implement a monitoring system to maintain social
distancing in the workplace, and thus enable safe
reopening of economic activities.
From a practical point of view, badge-type
devices, like Smart BadgeTM (Abeeway, 2019),
feature lower wearability compared to wristband
devices, such as Close-to-me (Partitalia, 2020a),
which is particularly important in the workplace,
where hand-free solutions are the main objective. In
addition, sensor integration can provide a dual
function, compliance with distancing rules and
worker condition monitoring, thus enabling a detailed
and accurate check of pandemic spread.
We have discussed IoT-based frameworks for
contact tracking and tracing that combine wearable
technologies, mobile applications, and cloud
computing. These systems were developed to
mitigate the impact of the COVID-19 pandemic and
act quickly on local outbreaks. Various technologies
have been adopted, such as BLE, Wi-Fi, and RFID,
for implementing these systems, and are often jointly
employed to improve reliability and accuracy
depending on the application scenario. Table 5 reports
a comparison of the IoT-based tracking systems
discussed in Section 4 in terms of detection
technology, detected parameters, scalability, and
availability of supporting wearable devices, high-
lighting the advantages, limitations, and potential to
fight future pandemics. Scalability must be intended
as a system capability to be applied to a broad
audience of people, limiting the invasiveness on
users’ lives and ensuring their data security.
Bluetooth beacons, Wi-Fi networks, and cellular
signals have been widely analyzed to extract
information on several human behaviors, including
the social interactions. In addition, Bluetooth offers
several advantages, such as small device dimensions,
low cost and power consumption, and broad
compatibility with smart devices.
However, Bluetooth solutions have limited
scalability due to their limited interoperability with
manual tracking systems, because of the collaboration
with the health system, low download rates, and
security and privacy concerns. As an alternative to
Table 3 Comparison between the innovative masks discussed in Section 2.2 regarding filtration efficiency, detected
parameters, self-sterilization mechanism, presence of a ventilation system, and wearability
Device Filtration efficiency Detected
parameters
Self-sterilization
mechanism
Forced
ventilation Wearability
Active+ Halo (AirPoP
Co., 2020)
99.3% (PFE)
99.9% (BFE)
RR, AQI Washable No High
PuriCare Wearable Air
(LG Group, 2020)
93.5% (BFE), 97.3%
(virus), 99.1% (pollen)
RR UV radiation Yes Low
Project Hazel (Razer,
2020)
95% (BFE) – UV radiation Yes Medium
Xiaomi Purely Mask
(Xiaomi, 2020)
95% (BFE) RR, AQI,
acceleration
Disposable filter Yes High
G-Volt mask (Dezeen,
2019)
99% (BFE) – Joule-heating No High
AQI, air quality index; BFE, bacterial filtration efficiency; PFE, particle filtration efficiency; RR, respiration rate; UV, ultraviolet
de Fazio et al. / Front Inform Technol Electron Eng 2021 22(11):1413-1442 1435
BLE tracking, frameworks for collecting and
analyzing GPS and Wi-Fi localization data are widely
investigated by telecommunication companies and
government authorities to detect social contacts.
Wi-Fi positioning systems can accurately determine
the distance between two individuals from a set of
monitored access points, and are reliable and scalable,
without needing additional hardware components.
This approach requires Wi-Fi coverage, which is now
widespread in both indoor and outdoor environments.
Telecommunication operators can also exploit the
cellular signal to determine a mobile phone’s position.
Triangulating the information from the home location
registers (HLRs) of different mobile stations, the
user’s location can be established with great precision.
We believe that this approach is the best solution for
determining information on large-scale population
behaviors without any hardware modification,
because it processes data that are already available in
the communication infrastructure and captures the
mobility information of a significant number of
people in quasi-real time.
A fundamental aspect related to collection of
social interaction data is the privacy issue. Different
Table 4 Comparison between different wearable devices for maintaining social distancing, discussed in Section 3, in
terms of detection technology, detected parameters, biofeedback typologies, wearability, and cost
Device Detection technology Detected parameters Biofeedback Wearability Cost
Close-to-me (Partita-
lia, 2020a)
BLE – Visual vibration High Low (€64)
Safe Spacer™ (Safe
Spacer, 2020)
UWB – Visual acoustic
vibration
High Low (€100)
Smart BadgeTM
(Abeeway, 2019)
BLE beacon, low GPS,
Wi-Fi, LoraWAN
– Acoustic Low Low (€100)
CrowdRanger
(Crowdsaver, 2020)
UWB – Visual acoustic
vibration
Low Low (€89)
Nymi Workplace
WearablesTM (Nymi
Inc., 2021)
NFC BLE HR, ECG, accelerometer,
gyroscope, fingerprint
Visual High Medium (€200)
BLE, Bluetooth Low Energy; ECG, electrocardiogram; GPS, Global Positioning System; HR, heart rate; NFC, near-field communication;
UWB, ultra-wideband
Table 5 Comparison between different IoT-based frameworks for contact tracking and tracing applications re-
ported in the scientific literature, in terms of detection technology, detected parameters, scalability, and availability
of supporting wearable devices
Device Detection technology Detected parameters Scalability Availability
Immuni App (Immuni,
2020)
BLE Distance time duration Medium No
TraceTogether (TraceTo-
gether Tokens, 2020)
BLE Distance contact time duration Medium Yes
GCG (Simmhan et al.,
2020)
BLE Distance contact time duration Medium No
Girolami et al. (2020)’s BLE Distance contact time duration Medium Yes
EasyBand (Tripathy et al.,
2020)
BLE Distance contact time duration Medium Yes
Paek et al. (2010)’s GPS Distance Low No
Jeong S et al. (2019)’s Magnetometer-based Magnetic field contact time
duration
Low No
Polenta et al. (2020)’s BLE Distance Medium Yes
Garg et al. (2020)’s RFID, NFC Distance Low Yes
Rajasekar (2021)’s RFID, NFC Tag detection Low Yes
Sapiezynski et al. (2016)’s Wi-Fi Distance contact time duration High No
BLE, Bluetooth Low Energy; GCG, GoCoronaGo; GPS, Global Positioning System; IoT, Internet of Things; NFC, near-field communi-
cation; RFID, radio frequency identification
de Fazio et al. / Front Inform Technol Electron Eng 2021 22(11):1413-1442
1436
strategies are available for managing data collection:
centralized, semi-centralized, and decentralized. In
the first one, the user periodically sends the Bluetooth
device IDs to a backend service; in the semi-
centralized approach, the relationship between the
App and the device ID is remotely stored, whereas the
contact information is collected on the local device
(BlueTrace, 2020). In contrast, in a decentralized
solution, the Bluetooth device IDs related to social
contacts are stored in the local devices, sending them
only when the user tests positive. The contact data
should be collected in a remote central database in an
encrypted manner to protect them from dumping and
data breaches. For instance, the GCG system uses a
centralized approach based on a unique ID and device
ID assigned by the central server during the
installation phase to maintain user anonymity
(Simmhan et al., 2020). The TraceTogether App
employs IDs assigned by the central server or locally
generated during the contact tracing activity
(TraceTogether Tokens, 2020). Thus, the system does
not need any personal user data; the user is recognized
only by a random ID that is periodically changed. In
contrast, the Immuni App paradigm recently switched
from a centralized approach to a decentralized one to
enhance privacy protection. Particularly, the user
smartphone locally collects the random IDs of
persons who are in close proximity, which is
produced according to a key stored in the device
during the installation process. If the user contracts
the virus, an unlock code is provided to transfer the
acquired IDs to the central server (Immuni, 2020).
The semi-centralized approach is used in the
BlueTrace and Aarogya Setu (Aarogya Setu, 2020)
applications developed by the Australian and Indian
governments to deal with the COVID-19 pandemic.
Usually, the tracing Apps are supported by
geolocalization data provided by a GPS receiver and
stored in a local SQLite database on the smartphone,
making them periodically available to a central server.
Kuhn et al. (2021) explored the numerous
protocols proposed by the scientific community for
guaranteeing the privacy and anonymity of collected
tracking information according to the data protection
rules; the analyzed protocols involve both centralized
and decentralized approaches, such as decentralized
privacy-preserving proximity tracing (DP-3T) (DP-
3T, 2021), the Google-Apple exposure notification
(GAEN) framework (Apple Inc., 2021), pan-
European privacy-preserving proximity tracing
(PEPP-PT, 2020), and the robust and privacy-
preserving proximity tracing protocols (ROBERT)
(PRIVATICS Team Inria and Fraunhofer AISEC,
2021). Furthermore, the authors proposed a critical
analysis of the considered approaches to highlight their
strengths and weaknesses. Finally, the discussion
indicated that none of the discussed protocols could
ensure localization and identity protection from user
and server perspectives. In particular, a centralized
approach could expose the localization data of alerted
users, as well as the hybrid solutions, such as the
DESIRE (Castelluccia et al., 2020) and ConTra-
Corona (Beskorovajnov et al., 2020) protocols.
In this context, Sun et al. (2020) developed a
security and privacy detection method, called Covid
Guardian, which identifies the shortcomings of the
protection systems by combining three steps: personal
identification information (PII) analysis, dataflow
analysis to detect privacy hazards, and malware
detection. Using this assessment method, the authors
tested 40 Apps, including TraceToghether, COVID-
Safe (Australian Government Department of Health,
2020), and Aarogya Setu; the obtained results
demonstrated that no application could completely
safeguard user security and privacy from all threads.
6 Conclusions
The COVID-19 pandemic, afflicting the world
population, is pushing companies and the scientific
community to develop solutions to contain the spread
of the virus, detect the first symptoms of the infection
early, and monitor the health conditions of infected
patients during quarantine. This paper provides a
careful and in-depth analysis of IoT-based wearable
devices for remotely monitoring the biophysical
parameters related to COVID-19 to avoid crowding
the hospitals. We have also explored several
commercial wearable solutions to make users aware
of social distancing in workplaces, which is
fundamental for reopening economic activities that
are heavily affected by long periods of lockdown. We
have also provided an overview of innovative
architectures based on IoT, wearable devices, and
cloud computing that track the contacts of tested
de Fazio et al. / Front Inform Technol Electron Eng 2021 22(11):1413-1442 1437
infected individuals, thus breaking the contagion
chain. Finally, we have critically analyzed and
compared the different discussed solutions, provided
ideas for investigation, and highlighted the potential
for developing innovative tools for facing future
pandemics.
Our scientific work focuses on applications
based on wearable devices for fighting the COVID-19
pandemic, including the extremely popular mobile
tracing applications, unlike similar review papers that
cover other monitoring solutions (drones, robotic
applications, etc.) (Nasajpour et al., 2020; Al-Humairi
and Kamal, 2021). Furthermore, review papers
covering the same topics do not always consider
commercial devices, including intelligent masks,
which are extensively investigated in this paper
(Chamola et al., 2020; Kumar et al., 2021). In addition,
in our review paper, we dedicate an entire section to
wearable commercial solutions (e.g., smart badges,
smartwatches, and smart bracelets) for complying
with social distancing rules, mainly in workplaces,
which are allowing a rapid and safe resumption of
economic activities; similar works rarely consider this
topic (Yousif et al., 2021). Finally, we have also
explored several wearable and implantable
applications for monitoring the effects of COVID-19
on the cardiovascular system, usually not covered by
similar works (Behar et al., 2020; Hedayatipour and
Mcfarlane, 2020). Therefore, we believe that the
accuracy and completeness of this paper represent its
actual added value and provide the reader with a
comprehensive overview of IoT-based solutions for
tackling the COVID-19 pandemic.
Contributors
Roberto DE FAZIO and Paolo VISCONTI designed the
research and drafted the paper. Nicola Ivan GIANNOCCARO
and Miguel CARRASCO processed the data and helped
organize the paper. Roberto DE FAZIO, Paolo VISCONTI,
and Ramiro VELAZQUEZ revised and finalized the paper.
Compliance with ethics guidelines
Roberto DE FAZIO, Nicola Ivan GIANNOCCARO,
Miguel CARRASCO, Ramiro VELAZQUEZ, and Paolo
VISCONTI declare that they have no conflict of interest.
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