# A Survey on Radio Frequency Identification as a Scalable Technology to Face
Pandemics

Giulio M. Bianco, Cecilia Occhiuzzi, Nicoletta Panunzio, and Gaetano Marrocco

###### Abstract

The COVID-19 pandemic drastically changed our way of living. To minimize life
losses, multi-level strategies requiring collective efforts were adopted while
waiting for the vaccines’ rollout. The management of such complex processes
has taken benefit from the rising framework of the Internet of Things (IoT),
and particularly the Radiofrequency Identification (RFID) since it is probably
the most suitable approach to both the micro (user) and the macro (processes)
scale. Hence, a single infrastructure can support both the logistic and
monitoring issues related to the war against a pandemic. Based on the COVID-19
experience, this paper is a survey on how state-of-the-art RFID systems can be
employed in facing future pandemic outbreaks. The three pillars of the
contrast of the pandemic are addressed: 1) use of Personal Protective
Equipment (PPE), 2) access control and social distancing, and 3) early
detection of symptoms. For each class, the envisaged RFID devices and
procedures are discussed based on the available technology and the current
worldwide research. This survey that RFID could generate an extraordinary
amount of data so that complementary paradigms of Edge Computing and
Artificial intelligence can be tightly integrated to extract profiles and
identify anomalous events in compliance with privacy and security.

###### Index Terms:

RFID, COVID-19, pandemic, social distancing, contact tracing, Healthcare
Internet of Things, personal protective equipment

##  I Introduction

††footnotetext: Authors are with the Pervasive Electromagnetics Lab,
University of Rome Tor Vergata, Rome, Italy. www.pwevasive.ing.uniroma2.it.
Corresponding author<EMAIL_ADDRESS>This is the
author’s version of an article that has been accepted for publication in IEEE
Journal of Radio Frequency Identification. Changes were made to this version
by the publisher prior to publication. The final version of record is
available at http://dx.doi.org/10.1109/JRFID.2021.3117764

COVID-19 pandemic abruptly changed the way we live, starting from social
interactions and healthcare, up to commerce, transportation and entertainment.
The high spreading capabilities (even by asymptomatic individuals [1]) and the
severe impact on public health [2] forced the Governments to adopt urgent
restrictive countermeasures [3]. Although at the time of writing the emergency
is mitigating, the earned experience so far has taught us that at least two
consecutive phases must be promptly managed to proficiently face the future
pandemics and allow a rapid recover of activities and daily life [4]: (_i_)
social distancing with home confinement and/or lockdown until the availability
of vaccines, (_ii_)__ technological support to mitigate the infection risk
during reopenings, to manage COVID-19 patients in hospitals and at home, and
to handle the logistics issues of the distribution of vaccines [5].

The pillars of countermeasures against a respiratory infectious pandemic can
be identified as:

(_i_) the usage of Personal Protective Equipment (PPE), like facemasks and
hygiene products, to reduce the spread of infecting particles;

(_ii_) social distancing and access control to avoid, or at least reduce,
possible channels of infections among people, and contact tracing to identify
recent interactions with infected people;

(_iii_) early detection of symptoms (e.g. fever, blood saturation, irregular
breath, cough, in synergy with Nasopharyngeal/Oropharyngeal Swabs and
serologic tests).

Furthermore, due to the scientific and clinical evidence of the presence of
infectious droplets floating in the air for a long time, especially in indoor
environments [6], the continuous verification of air quality and ventilation
is recommended to reduce the risk of inhalation and of infection [7].

Figure 1: Synopsis of applications of RFID technology to pandemic
countermeasures.

The fight against the spread of the infection can benefit from pervasive
equipments that are currently adopted in daily life (i.e. smartphones and
wearable devices), namely, the rising framework of the Internet of Things
(IoT). This technology can be specialized in a fan of options sharing same
infrastructures, standards, and expertise [8]. Different IoT protocols like
Bluetooth Low Energy (BLE), Wireless Fidelity (Wi-Fi), Global Positioning
System (GPS), have already gained popularity for providing solutions to
pandemic-related challenges in both domestic and hospital scenarios [9, 10,
11]. The Radiofrequency Identification (RFID), that is nowadays one of the
pillars of the Healthcare Internet of Things (H-IoT) [12], is probably the
most suitable technological approach to be scaled up in order to support, by
means of a single infrastructure, almost all the logistic and monitoring
issues related to the war against a pandemic. RFID transponders [13] can be
manufactured over large scale in any shape and material, including plastics,
paper, elastomers, as well as dissolvable organics compounds. The tag can be
embedded into things and can be attached onto the human skin as well. Readers
can be rugged and multifunction, integrable into a smartphone and even into a
smartwatch-like personal device. Not least, RFID relies on standard protocols
and is intrinsically low-cost.

While the original purpose of RFID was the electronic labeling of goods, it is
nevertheless quickly moving to the monitoring of objects, industrial
processes, and people, by empowering low-cost devices with sensing
capabilities [13]. Measurement of temperature, pressure, humidity, just to
list some, are now feasible with commercial-off-the-shelf (COTS) tags [14].
Advanced applications, involving chemical parameters (pH, chlorides) and
biophysical signals (EMG, EKG, skin resistance), are under advanced
investigation in worldwide research labs [15, 16]. Hence, there is a creative
wealth of technology all around that could be usefully put in phase for
application to pandemic emergencies.

The aim of this paper is, therefore, showing how the existing RFID devices
(technology introduced in Section II) and the most advanced related research
efforts could be synergically used to fight a COVID-19-like pandemic. Three
macro-topics (as schematized in Fig. 1) are considered: PPE supply, usage and
management (Section III), Access control, contact tracing and indoor ambient
quality (Section VI), and Predictive and Preventive Healthcare (Section V).
For each topic, the main issues and needs are resumed, and consequently the
most appropriate RFID devices and processes are identified. In some cases, the
technology has already been adopted in the proposed scenario. In other cases,
applications are in different fields but be employed in the fight against
pandemics. To improve readability and enable a direct access to the required
information and solutions, each of the three sections can be considered as a
standalone chapter. For each application, a table lists the real-life problems
and the possible solutions through RFID technology. References are accordingly
grouped to directly find the source of the discussed topics.

The overall purpose of the paper is definitely to provide a survey on what
could be immediately applied to the pandemic-related problems, as well as to
draw a picture of the open challenges for the finalization of current
researches.

##  II UHF RFID Technology: Basic Principles and Latest Advancements

Figure 2: A sketch of the RFID system. A reader _(2)_ interrogates the tag
_(1)_ , the behavior of which is influenced by a changing physical feature
$\Psi$. Adapted from [13].

An RFID system (sketch in Fig. 2) comprises two components: _(1)_ the _tag_ ,
that is attached onto the object (or even on the person) to be labeled,
comprising an antenna and a microchip transponder (Integrated Circuit, IC),
and _(2)_ the local querying system (or _reader_), that energizes the tag from
remote and collects data reflected back from it throughout backscattering
modulation. The backscattering communication scheme applies for both short
range (namely HF/NFC systems working at $13.56$ MHz) and long range (working
in the UHF 860-960 MHz frequency band) platforms [17]. In the former case the
interaction with the reader is mostly one-to-one (inductive coupling), whereas
in the latter case a single reader can simultaneously collect information from
hundreds of tags, being the radiating elements conventional antennas operating
in far-field conditions. Accordingly, the UHF band has the advantage to be
tailored to control, within a unitary framework, both single items and complex
processes [18], with extremely low power consumption and maintenance cost
(passive and battery-less systems). This paper is therefore focused on systems
working in the UHF frequency band, which currently provide the largest amount
of COTS devices for any kind of material and are readable up to more than $15$
m [17].

The tags can be passive, when they harvest energy from the interrogating
system, semi-active (BAP: Battery Assisted Passive), when a battery is
included to only feed the sensors and the internal memory, or fully active. In
the latter case, a local source directly feeds both a microcontroller and the
transmitting radio, such to sensibly increase the reading distance and enable
autonomous operations.

In passive and BAP technology (see again Fig. 2), at the beginning of the
reader-to-tag communication protocol, the reader first activates the tag by
sending a continuous wave. It charges an internal capacitor, providing the
required energy to the tag to perform actions. During the next steps of the
communication, the tag receives commands from the reader, and finally sends
back the data through backscattered modulation of the continuous wave provided
by the reader itself. In this case, the tag’s IC acts as a programmable
switching device between a low impedance and a high impedance state, thus
modifying the reflectivity of the responding tag itself, and hence the
strength of the reflected power.

(a) (b) (c)

(d) (e)

Figure 3: Medical PPE used during the COVID-19 pandemic [19]. (a) FFR and
closed shoes (adapted from [20]). (b) PAPR, medical gloves and gown (adapted
from [21]). (c) Goggles and face shield (adapted from [22]). (d) Medical mask
(adapted from [23]). (e) Heavy duty gloves, apron and boots (adapted from
[24]).

During a typical RFID communication, reader and tag share different types of
data:

  * •

_Electronic Product Code_ (EPC) and _User memory_ , that are the unique
IDentification name assigned to the tag and up to 8k byte of data; both can be
writable many times and password protected;

  * •

_Received Signal Strength Indicator_(RSSI), that is related to the power
backscattered by the tag toward the reader;

  * •

_Turn-on power_ , that is the minimum power the reader should emit to wake-up
the tag at a given distance;

  * •

_Phase_ , that is the phase of the signal backscattered by the tag and related
to the differential radar cross section $rcs$ among the two modulating states.

The last three parameter are strictly related to the tag antenna operating
features. Any variation of the surrounding environment $\Psi$, able to affect
the antenna gain and input impedance, is transduced into a variation of the
signals transmitted and received by the reader, and can be hence related to a
sensing activity performed by the tag itself. This analog approach, denoted as
_sensor-less sensing,_ can be applied to every passive tag if properly
designed [25]. Extremely low-cost wireless sensors, namely disposable, can be
obtained at the price of a modest accuracy [26].

Table I: Possible applications of tagged PPE during pandemics. PPE problem |  Possible RFID Application |  Application examples |  References
---|---|---|---
SUPPLY CHAIN MANAGEMENT |  PPE shortages |  Localization and tracking inside hospitals to reduce lost and stolen PPE |  [27][28][29][30][31][32][33][34]
|  Optimization of HC supply chains |  Supply chains of hospitals and pharmacies |  [35][36][37][38][39][40]
|  Anti-counterfeiting |  Supply chain integrity |  [41][42][43][44]
|  |  Post-supply chain control through block-chain |  [45][46]
APPROPRIATE USE OF PPE |  Check if the HCW is wearing the proper PPE |  Gate-based check |  [47][48][49][50][51][52][53][54]
|  |  A body-worn portable reader monitors the PPE worn by the worker |  [55][56][57][58]
PPE INTEGRITY |  Correlate the PPE with sensed data about its conditions |  Maintenance of respirators optimized through their life cycle data |  [53]
|  |  Pressure sensor to monitor the correct use of the PPE |  [59]
|  |  Embedded humidity-sensing to monitor the effectiveness of the filtering mask |  [60]
PPE DISPOSAL |  Monitor the disposal of the PPE (which is a medical waste) |  Tracking the medical wastes to be disposed |  [61][62][63][64][65]
|  Improvements in the supply chain and in the reverse logistic of the PPE to reduce the environmental footprint |  Environmental footprint reduction by adopting the RFID in supply chains and in the reverse logistic of medical wastes |  [66][67][68]
HAND HYGIENE AND DISINFECTION |  Ensure the compliance with the hand hygiene procedures |  Identify the HCW who needs to perform the hand hygiene |  [69][70][71]
|  |  Monitor the correction of the hand hygiene procedure |  [72][73][74][75][76]

Nowadays, off-the-shelf RFID ICs with augmented sensing capabilities are
available [77, 78, 79, 80]. They include high-speed non-volatile memory
(EEPROM), typically integrate an embedded temperature sensor, and are provided
with programmable I/O ports for connecting general-purpose microcontrollers
and sensors. Sensing data are digitally encoded into the internal memory and
transmitted through standard backscattering communication. If compared with
the analog sensing, the collected physical information is definitively more
specific and robust [13]. When provided with battery, such ICs can also act as
data-loggers, and they can gather sensing data even without the need to
receive energy from the reader. These devices can be considered as a
convergence point among fully passive tags and the autonomous sensor nodes
having local computational capability with already assessed applications in
Industry [18] and Healthcare [81].

##  III Personal Protective Equipment Management

According to the WHO guidelines [19], HealthCare Workers (HCWs) and general
public must wear proper PPE depending on the activities to be performed and on
the occupied spaces. The set of appropriate PPE to protect against pandemics
(Fig. 3) comprises:

  * •

medical masks, filtration respirators (N95 or FFP2), cloth masks, higher
filtering masks, and Powered-Air Purifying Respirators (PAPR) [82, 83];

  * •

gowns;

  * •

medical or heavy-duty gloves;

  * •

eye protections (e.g., goggles or face shields);

  * •

aprons;

  * •

boots or closed shoes.

In addition to these requirements, a proper hand hygiene is mandatory [82].

The management of PPE requires:

  1. 1.

an efficient supply chain and control of counterfeit products [84, 85], along
with solutions to face their shortage, especially in the case of misuse and
overuse [86];

  2. 2.

to check the compliance of the HCWs with the PPE procedures and guidelines,
which moreover can be time-consuming or even confusing [87], also considering
the difficulty of working while wearing the PPE [88, 89];

  3. 3.

to check the condition of PPE, as it overuse can cut down its effectiveness
[90, 91];

  4. 4.

the care of the environmental impact produced by the wasted PPE, in particular
regarding their correct disposal [92].

Similar issues are related to the pharmaceutical chain, in particular to
vaccines and life-saving drugs [5], whose supply and correct distribution is
directly proportional to their effectiveness.

RFID systems that can be employed in the PPE management are resumed in Table
I.

(a) (b)

(c) (d)

Figure 4: System’s architecture to monitor the PPE worn by the workers
(adapted from [47]): (a) terminals checking, eventually equipped with cameras,
and (b) body-worn readers checking through communication with the Central Unit
Microcontroller (CUM) embedded in the worker’s Personal Digital Assistant.
Examples of implemented systems: (c) a gate to monitor the compliance with the
worn PPE (adapted from [20], [52]); (d) a portable RFID reader worn by a nurse
(adapted from [93]).

###  III-A Supply Chain Management

RFID technology has been massively exploited for the supply chain in
healthcare [35] and pharmaceutical [40, 43, 44] branches, with promising
results in terms of cost reduction and patient safety [35].

####  III-A1 Shortages of PPE

The shortages of PPE can be partly addressed by adopting RFID equipment-
tracking system within the hospital environment to increase their utilization
and reduce the number of lost and stolen items [27, 30]. RFID-based
localization and tracking systems in healthcare scenarios are resumed in [33,
94, 95, 34]. RFID can also be hybridized with either 2D-barcodes [28] or with
infrared localization [32].

####  III-A2 Inventory of Vaccines

Even though hospitals still widely employ barcodes to label equipments, drugs
and other medical items, RFID tags can be more effective thanks to the
possibility to store additional data on the tagged objects [36], and, above
all, for the potential upgrading to sensing capabilities. In particular, RFID
systems have already been employed in the supply chain of the transfusion
blood [38, 39] and in cold chain operations [96, 97, 98]. Hence, they could be
applied in the management of vaccines that need to be kept at controlled
temperature [99].

####  III-A3 Anti-counterfeiting

RFID tags could also be adopted for anti-counterfeit purposes [41, 42], in
both pharmaceutical and PPE sectors [43, 44]. The supply-chain integrity can
be monitored by the synergistic action of RFID and blockchain systems [45],
especially when an unambiguous ownership is missing [46], for example, in most
of the single-use medical PPE.

###  III-B Appropriate Use of PPE

PPE is often cumbersome and difficult to use; therefore, HCWs may not strictly
follow the guidelines, consequently hindering the effectiveness of the
equipment. If the PPE is provided with an RFID tag, a reader could check if
the worker is wearing the proper pieces of equipment [55, 48, 53, 54]. A more
robust PPE check can be achieved by combining ID and sensing data. For
instance, tagged hardhats equipped with pressure sensors can ensure that the
worker is effectively wearing the equipment and not just carrying it with him
[59].

Overall, two kinds of architecture can be implemented (sketch in Fig. 4) to
check if the workers carry their PPE:_(i)_ check-points, i.e. readers on gates
and terminals (Fig. 4.a), and _(ii)_ portable/wearable readers (Fig. 4.b).

(a) (b) (c)

(d)

Figure 5: Sensorized FFR. (a) Volunteer wearing a tagged facemask. (b) FFR-
integrated humidity tag. (c) Zoomed-in view of the textile humidity-sensing
tag. (d) Measured water moisture collected by the tagged and tag-less FFRs
after 30 and 60 minutes of physical exercise with a stationary bike. Adapted
from [60].

####  III-B1 Gate-based Check

Gate-based systems located in strategic points exploit RFID tags on the PPE to
either control the presence of the PPE itself when entering dangerous areas
[47, 48, 49, 50, 53, 54] or __ to identify the worker while a camera checks
for the PPE compliance through images processing [51, 52].

####  III-B2 Body-worn Readers

Since RFID gates cannot check if HCWs remove the PPE after the checkpoint,
portable readers can be adopted for continuous monitoring of the workers.
Readers could be either body-worn and connected to a Central Unit
Microcontroller (CUM) [55, 57], or embedded in the workers’ Personal Digital
Assistants (PDA) [56]. Body-worn portable readers could also be used to
recognize the activity carried on by the HCW [93], as the _iBracelet_ in [58].

(a) (b)

(c)

Figure 6: RFID-based systems to monitor hand hygiene compliance. (a) A smart
sink identifying the user through RFID badge (adapted from [69]). (b) A
dispenser of hand sanitizer exploiting RFID badges (adapted from [71]). (c) A
system to monitor dispenser activation, which couples the ID and the sensing
information given by two different tags (adapted from [76]).

###  III-C PPE Integrity

The PPE health conditions must be in optimal conditions to guarantee their
effectiveness. As an example, since the filtering capabilities of the
facemasks are reduced by the breath humidity [90, 91], the WHO advises
replacing the masks as soon as they become damp [100], or at least after a
specific use-time. An embedded humidity-sensing tag is capable of
discriminating normally used Filtering Facepiece Respirators (FFRs) from
abnormally wet ones (having more than $50$ mg of moisture on the internal
surface; Fig. 5) [60]. Moreover, RFID tags integrated inside the mask can
monitor the health status of the wearer [101], as discussed next in Section
V-B.

###  III-D PPE Disposal

A used PPE is an infectious medical waste that must be properly disposed of,
especially during pandemics [102]. The correct disposal of wastes through RFID
has been a well-studied topic in civil and industrial sectors [103] to reduce
the environmental impact [66]. RFID could be effectively exploited also for
medical waste management [61, 65]. In such systems, the tags are read thrice
[63, 62]: when leaving the hospitals, inside the trucks through truck-mounted
readers, and when arriving at the disposal site. The RFID can also be
exploited for the optimization of the route to collect the medical wastes
[64], with benefits also in recycling procedures and reverse logistics [67].

Table II: RFID applications for patient management, contact control, access control and the indoor ambient quality verification. Goal |  RFID Application |  Application examples |  References
---|---|---|---
ACCESS CONTROL |  Authorization verification |  The individual entering a secured zone should be detected, and its trustfulness should be automatically verified |  [104][105][106][107][108][109][110]
|  Occupants limitation, Individuals flow, and Resident time |  Entrance/exit reports should be recorded to keep track of the check-in or check-out status of each visitor |  [107][108][111][109][110]
|  PPE-based authorization |  Properly placed gates and terminals deny access if the entering individual is not wearing the appropriate PPE |  [53][54][48][49][50][47][51][52][60]
|  Temperature-based authorization |  Properly placed gates and terminals deny access if an anomalous body temperature is detected |  [112][113][114]
PATIENT IDENTIFICATION AND MANAGEMENT |  Labelling of the patient |  Assigning an ID to the patient |  [115][116][117][118]
|  |  Assign the patient’s medical records to the tag |  [119]
|  Tracking of the patient |  Univocally assign a reader to a room |  [120][121][122][123][124][125][126][127]
|  |  Assign a tag to the location of the nearest reader |  [128][129]
|  |  Hybrid system exploiting RFID and Wi-Fi |  [130]
CONTACT TRACING AND INTERPERSONAL DISTANCING |  Contact tracing |  Readers in checkpoints |  [131][132][133][134]
|  |  Tag-to-tag communication |  [135][136][137][138][139][140][141][142]
|  Social distancing |  Proximity detection |  [143][144]
|  |  Estimation of the tags’ orientation to monitor face-to-face contacts |  [145][146][147][148][149][150][151]
INDOOR MICROCLIMATE |  Room temperature |  The monitoring of environmental temperature is carried out by deploying multiple sensor units to constitute a USN |  [152][153][154] [109]
|  Room relative humidity (RH) |  The monitoring of environmental humidity is carried out by deploying multiple sensor units to constitute an USN |  [155][156][157][158][159][160][161][154][109]
|  Air exchange (windows opening) |  The occurrence of windows opening is enabled by properly distributed light sensors and on-window transponders |  [152][162][154][109]

###  III-E Hand Hygiene and Disinfection

Hand hygiene is crucial to correctly wear and undress the PPE [163, 164].
Available RFID solutions are summarized in Fig. 6. RFID can identify the HCW
suggesting to perform the hand hygiene with a given periodicity [69, 70, 71].
Then, the HCW RFID badge can activate the hand sanitizer dispenser [72, 73,
74, 75]. Alternatively, a sensor tag can monitor the dispenser activation by
measuring the propanol and ethanol concentration in the air [76], linked to
the presence of the HCW ID in the same room.

##  IV Access Control, Contact Tracing and Indoor Ambient Quality

(a) (b)

(c) (d)

Figure 7: (a) Access control system testbed on a prison floor, with details of
biometric-based access control protocol (adapted from [106]). (b) Gate access
control setup at the door, able to discriminate the flow of tagged individuals
(adapted from [111]). (c) RFID card-based access control. Example of a summary
of processed requests (adapted from [107]). (d) A subset of signals collected
by an RFID sensors network in case of authorized access to a restricted area
(adapted from [165]).

During pandemic emergencies, to allow a safe interaction among people,
especially in indoor spaces, three key strategies have been identified: _(i)_
access control, _(ii)_ contact tracing, and _(iii)_ social distancing. Mobile
applications [166] and/or wearable radios [137] can be employed for
interactions monitoring. A specific case of access control and tracing is the
management of patients and staff in hospitals, points of care, and nursing
homes, especially in case of overwhelmed situations that could lead to delayed
cures [167, 168, 169]. In addition, since infectious aerosols remain viable
for up to 3 hours [6], the simultaneous presence of people within a restricted
environment demands continuous air recycle. Moreover, if room temperature and
humidity are not appropriate, the risk of inhalation and infection increases
[7]. Hence, ventilation and air quality management play a key role in
preventing the spread of respiratory infections indoors [170, 171].

RFID-based access control, contact tracing and social distancing require that
people wear an RFID tag within their badge or on PPE. Furthermore, since RFID
tags empowered with sensing capabilities can enable the possibility to get
also the “state” of the tagged entity, the monitoring of ambient quality
becomes feasible too. Table II shows how RFID systems could be employed in the
former scenarios, as detailed next.

(a) (b) (c)

Figure 8: (a) Wearable fabric-based RFID skin temperature monitoring patch,
glued on T-shirt (adapted from [112]). (b) RFID portal for the automatic
temperature screening of crossing people wearing epidermal sensors (adapted
from [113]). (c) Schematic drawing of the automatic detection of temperature
data of people during the crossing of a surveillance gate (adapted from
[114]).

(a)

(b)

Figure 9: (a) Map of RFID readers (blue sketches) installed in an emergency
department. The outlined boxes in the images are different areas (numbered as
reported near to the red squares), and multiple readers per area are deployed
(adapted from [129]). (b) Sketch of T2T systems, wherein an illuminator allows
the communication between tags (adapted from [138]).  Figure 10: RFID-enabled
ambient control on the basis of local data and context data. Adapted from
[162].

###  IV-A Access Control

Access control systems can leverage the RFID technology [108]. Regarding
public areas, such as shops, restaurants, workplaces, classrooms, and
hospitals, access should be granted only if a set of criteria is
simultaneously satisfied: (_i_) the person has the right privileges to enter
the restricted area (Fig. 7.a); (_ii_) the number of people inside the venue
does not exceed the maximum capacity limit [172]; (_iii_) the individual
correctly wears the required PPE, which at least includes the protective
facemask; (_iv_) the body temperature is below $37\
\textnormal{\"{\i}\textquestiondown\oe C}$ [173].

####  IV-A1 Detection of People Density

RFID-based access control systems allow to count the number of people inside
space and also to generate log reports to keep track of check-in and check-out
status of each visitor [107, 108] (Fig. 7.c). RFID passive tags [110] can be
integrated within the user badge and revealed by one or more readers at the
access gates for the automatic identification and the storage of entrance/exit
records. In particular, the monitoring of people flow in closed areas can be
achieved by applying phase-based methods [111] capable of discriminating
incoming and outgoing tags (Fig. 7.b). By evaluating the time spent inside the
venue, the risk of exposure to infection can be estimated accordingly.

In these applications, RFID tags can also be distributed in the environment to
be controlled so that the interaction with people will cause a detectable
change in their communication link to the reader. For instance, the RFID
device in [154] is used as a perimeter sensor, placed on the floor and close
to the access points, so that variations in the returned RSSI can be exploited
to detect/identify the people passage. When deployed in a real-world
application [174, 109, 165], the RFID network is able to reveal unauthorized
access in critical areas (Fig. 7.d), such as hospital wards or vaccine storage
warehouses.

####  IV-A2 Preventing the Access of Non-Compliant People

PPE-based access control systems should be implemented as a countermeasure to
deny access if the individual does not wear the required tagged PPE (e.g., the
facemask in Fig. 5, [60]). In addition, wearable tags with temperature sensing
capabilities permit to accomplish another easy countermeasure to deny access
to potentially ill individuals. The wearable tags can be integrated into
clothes (e.g. textile RFID tags [112, 175, 176, 110], Fig. 8.a) or even adhere
directly on the skin (e.g. epidermal RFID tags [177], see Fig. 12, 13, 14).
Such a temperature-based access control system enables the automatic detection
of the anomalous temperature of the individuals during the crossing of a
surveillance gate [113, 114], accordingly triggering an alarm and/or
preventing the doors from opening (Fig. 8.b,c).

(a)

(b) (c) (d)

Figure 11: (a) Architecture of an RFID sensors network for both ambient
monitoring and access control, with a subset of the measured electromagnetic
signals (adapted from [109]). (b) Moisture sensing label incorporating two
RFID tags to be integrated over walls (adapted from [156]). (c) Calibration
curve of the humidity inkjet-printed sensor tag (adapted from [157]). (d)
Measurement setup for the overnight humidity exposure with a doped tag placed
outside an external wall and interrogated by an RFID reader from the inside
(adapted from [158]).

###  IV-B Patient Identification and Management

####  IV-B1 Identification

Labeling of patients can be achieved by passive wristband tags, with the aim
of identifying them along clinical paths and optimizing the clinical
practices. RFID wristbands can be assigned before entering the hospital, e.g.
when accepted on the ambulance [115] or during the triage [117]. RFID-
wristband can also be applied to identify the pre-surgery patients as well as
to manage their clinical records [116]. RFID systems, moreover, permit to
optimize the waiting times of the treatments [118] and also to keep track of
medications within the Intensive Care Units (ICUs). For instance, when an
intravenous medication has to be administered to the patient, the nurse can be
required to validate the process by reading together both the patient and the
medication ID [119].

####  IV-B2 Tracking

Patients tracking can be achieved by linking one (or more) reader(s) to one
room so that the presence of the patients is retrieved from the corresponding
detected on-body tags [124, 125]. In this case, each reader is assigned to a
specific area. Alternatively, the assignment can be based on the maximum
signal strength received by the readers [120] or by the tag [128, 127]. In any
case, the last reader illuminating the tag of the patient can write its ID on
the tag so that other readers can retrieve its last known position [123]. More
accurate localization can be obtained by active RFID tags that also exploit
Wi-Fi signals for the localization of the tagged individual [130].

###  IV-C Contact tracing and Interpersonal Distancing

RFID-based contact tracing is based on _(i_) readers placed in checkpoints
(Fig. 9.a), or _(ii_) tag-to-tag (T2T) interaction (Fig. 9.b).

####  IV-C1 RFID-based Check-Points

This first architecture is similar to the standard people tracking strategy,
wherein people are tagged, and properly placed readers monitor specific areas.
RFID systems were successfully deployed in Taiwan and Singapore [131, 178]
during the epidemic outbreak of the Severe Acute Respiratory Syndrome (SARS).
In both cases, the systems were based on active UHF RFID tags and differed
only for the people that were tagged: in Singapore everyone entering the
hospital was tagged; instead, in Taiwan, only the medical staff. It is also
possible to place a reader on each patient’s bed to identify approaching
nurses and physicians. This system can successfully track almost every
interaction up to a range of $1.75$ m and can prevent nosocomial infections
[132, 179].

####  IV-C2 Face-to-Face Tracing

The current bottleneck of massive implementation of RFID-based contact tracing
is the high cost of readers. This critical issue could be overcome by
resorting to the architecture of Tag-to-Tag (T2T) communication [138], which
uses a much simpler reader, namely a continuous wave generator. T2T
communication is based on _reader tags_ (also known as _talkers_[139]),
capable of interrogating _listener tags_ by backscattering the field generated
by an RF source (the _illuminator_ [138]). When tags of different items get in
close proximity, they start interacting and sharing information, hence
recording the meeting event. Although the T2T maximum achievable communication
distance can be as low as $25$ mm [138, 139], a demodulator capable of
extending the T2T communication range up to $1.5$ m has been developed [140].
A different kind of T2T communication involves receiving-only RFID listeners,
called _augmented RFID receiver_(ARR), creating an _augmented RFID system_
that monitors the contacts of tagged people near the listener [141], up to a
distance of $3$ m [143]. A type of ARR named _Sense-a-Tag_ can be embedded
into a wristband to sense the interactions of the user with tagged objects
[144] and possibly detect face-to-face contacts that are known to require
extra-distancing between people to prevent airborne infections [180].

Table III: Wearable RFID Sensors for the monitoring of positive or suspected infected individuals. Clinical Evaluation of COVID-19 |  Measured Parameters |  Wearable RFID Sensors Potentials |  References |  Phases
---|---|---|---|---
CLINICAL SYMPTOMS MONITORING |  Body Temperature |  • Comfortable on-skin plaster-like devices • Continuous or periodic measurements of body temperature • Fever detection |  [114][181][182][183][184][185][186][187][188][113][189][190][178] |  I, II
|  Cough |  • Fabric-integrated devices • Coughing fit detection • Indirect breath monitoring (RSSI variations) |  [110] |  I, II
RESPIRATORY ASSESSMENT |  Respiratory Rate, Respiratory Depth, Respiratory Cycle, Breath Effort |  • Comfortable facemask or on-skin plaster-like devices, or fabric-integrated devices • Abnormal respiration patterns detection (e.g. apnea, tachypnea, …) • Direct (breath temperature or humidity) or indirect (phase or RSSI variations) breath measurements |  [191][192][193][194][195][196][197][198][199][200][201][202][203][204] |  I, II
|  Blood Oxygen Saturation |  • Continuous pulse oximetry monitoring • Very low power consumption |  [205] |  II
CARDIOVASCULAR STATUS |  ECG, Hearth Rate |  • Comfortable on-skin or fabric-integrated devices • Continuous or periodic hearth rate and multichannel ECG monitoring • Very low power consumption |  [206][207][200][208][189][209][210][211] |  II
|  Cuffless Blood Pressure |  • Fabric-integrated devices • Continuous blood pressure monitoring in static condition (in care facilities) |  [200] |  II
PSYCHO-PHYSICAL STRESS MONITORING |  Sweat |  • Comfortable on-skin plaster-like devices or fabric-integrated devices • Continuous or periodic sweat pH, biomarkers and compound measurements • Psycho-physical stress detection • Direct (sweat chemicals) or indirect (RSSI variations) sweat measurements |  [15][185][212][175] |  I
|  Sleep Disorders |  • Fabric-integrated and ambient tags • Continuous overnight sleep quality evaluation • Overnight body movements detection • Possible integration with other RFID sensors (e.g. body temperature, sweat compound, …) |  [204][195][201][113][213][214][215] |  I, II

###  IV-D Indoor Microclimate

The American Society of Heating, Refrigerating and Air-Conditioning Engineers
(ASHRAE) suggests doubling the outside air changes per hour with respect to
the recommended ventilation rate before the pandemic
($14\,\textnormal{$\textnormal{m}^{3}$/hr}$) [216]. Moreover, indoor
temperature and relative humidity (RH) should be maintained within determined
ranges (e.g., $20$-$24\ \textnormal{\"{\i}\textquestiondown\oe C}$ and
$40$-$60\,\%$ respectively) to mitigate the virulence of infectious agents
[171, 170].

####  IV-D1 Windows Opening

RFID tags embedding light sensors can be used to monitor the windows opening
to foster the exchange of air [152, 162, 154]. For instance, by sensing the
light level, a fan located in an office room can be activated only when
certain conditions are verified [162] (Fig. 10). More elementary strategies
can involve passive tags disposed on the windows, whose opening/closure is
retrieved by processing the RSSI from the tags [165].

####  IV-D2 Indoor Temperature and Humidity

The microclimate of a room, namely the temperature and humidity (Fig. 11.a),
can be monitored by sensor-oriented tags, thus providing a further indication
about critical environmental conditions that require air recycling.
Environmental temperature can be monitored by means of battery-less UHF RFID
transponders provided with a temperature sensor [152, 153]. Humidity sensors
can be instead designed providing the RFID tag with a material having a high
ability to absorb moisture and changing its dielectric properties accordingly
[155, 156, 157, 158, 159, 160, 161] (Fig. 11.b,c,d). The same reader
infrastructure deployed for the access control can be used for the
interrogation of the above environmental sensors.

##  V Predictive and Preventive Healthcare

The health monitoring of suspected or confirmed infected people can be
referred to three different and often consecutive phases: (I) Preventive early
diagnosis, (II)__ Domestic disease monitoring, (III) Hospitalization.

In the former phase, preventive testing programs, both viral (swabs) and
physical, should be performed. Examples are temperature monitoring [173],
cardiovascular and respiratory screening (i.e. cardiac rate, cough and altered
respiration [217], and oxygen saturation [218]), especially in case of
individuals belonging to controlled and restricted communities, such as
hospitals, nursing homes, assisted living facilities, factories and workplaces
in general, schools and prisons [219].

In the second and third phases, the health status of isolated individuals
should be continuously monitored to intervene in a timely manner in case of
rapid worsening. For instance, the domestic monitoring of symptoms of COVID-19
positive patients for signs of deterioration has demonstrated to effectively
decrease the mortality rates [220] and the hospital loads in China.

(a) (b) (c)

(d) (e)

Figure 12: On-skin temperature RFID sensors. (a) Worn prototype of the
epidermal RFID temperature sensor (adapted from [190]). (b) Flexible epidermal
RFID thermometer (adapted from [187], [113]). (c) Flexible and conformable
RFID thermometer (adapted from [183]). (d) i-TEK’s body temperature monitoring
system (adapted from [114]). (e) Dual-chip flexible epidermal RFID temperature
device (adapted from [182]).

Wearable and epidermal RFID sensors [177] exhibit a great potential to enable
a comfortable, easy-to-use and wireless monitoring of a significant set of
physiological parameters, such as temperature, breath anomalies,
electrophysiology, and biomarkers in sweat, through small devices to be
applied directly over the skin [221], [222]. Table III summarizes the possible
applications of RFID platforms versus some symptoms of the infectious disease
[223].

###  V-A Body Temperature

During the fight against the SARS emergency in 2006, UHF active tags with an
embedded thermometer, employed for the Location-based Medicare Service (LBMS)
project at the Taipei Medical University Hospital [178], demonstrated that the
automatic and real-time temperature taking reduces the risk of staff
infections by limiting the contact with patients. This system, moreover,
improved patients safety by detecting fever events in a timely manner.

RFID epidermal thermometers are suitable to be directly attached to the human
skin by means of bio-compatible transpiring membranes [190] (Fig. 12.a). Over
the years, they have become increasingly more flexible, conformable [113]
(Fig. 12.b), [183] (Fig. 12.c), and small [181] (Fig. 13), also paying
attention to their reusability to reduce fabrication costs and pollution
[188]. New pioneering studies [182] look in the direction of multichip RFID
temperature sensors to strengthen the correlation between the skin and core
body temperature (Fig. 12.e).

Figure 13: Plaster-like under-arm RFID thermometer. Correlation with an axilla
electronic thermometer. Adapted from [181].  Figure 14: Small-size flexible
RFID BAP epidermal sensor board and continuous temperature monitoring against
sporadic tympanic sampling. Adapted from [185, 186].

Passive RFID epidermal thermometers can be read up to $1.5$-$2\,m$ [224],
hence being compliant with an automatic and on-the-fly reading through gates
and check-points to enable the unsupervised detection of anomalous temperature
peaks [114] (Fig. 12.d). Moreover, envisaged applications are also in clinical
and domestic settings by means of fixed remote antennas (e.g. continuous
overnight temperature monitoring). In this sense, these thermal sensors have
already been experimented on a hospital ward [187] and have been deemed robust
and reliable. If placed in the armpit region (Fig. 13), they are well
correlated with a standard axilla electronic thermometer (Pearson’s
coefficient $p=0.78$) with a difference of less than $0.6\
^{\textnormal{o}}\textnormal{C}$ in the $95\%$ of measurements [181].

Continuous sampling of the skin temperature can also be achieved by exploiting
BAP RFID tags [185] (Fig. 14) working as data-loggers. Their validity was
demonstrated in [186] for the measurement of the skin temperature in realistic
conditions and in [184] for the evaluation of thermal stress of firefighters
during training.

###  V-B Respiratory Function and Cough

####  V-B1 Cough

The presence and the dynamics of coughing can be monitored by wearable RFID
devices sewed into clothes on the chest region (Fig. 17.c), exploiting the
backscattered power in combination with motion inertial sensors [110]. Another
simple mean to count cough events is based on sensorized facemasks that enable
the detection of cough’s periodicity and duration by detecting the temperature
spikes produced by cough shots [225] (Fig. 15).

Figure 15: RFID-enabled cough evaluation system through detection of
temperature spikes. Adapted from [225].

(a) (b)

(c)

Figure 16: RFID sensors for _direct_ respiratory function assessment. (a) RFID
breath humidity sensors for integration into a facemask (left) and over the
skin (right) (adapted from [198, 196]). (b) Different prototypes of RFID skin-
attachable breath temperature sensors (adapted from [197]). (c) Recording of a
respiratory pattern (adapted from [196]).

####  V-B2 Breath

RFID devices for breath monitoring can allow the avoidance of intrusive nasal
probes and chest bands [226].

The first category of devices relies on _direct_ measurement of the human
breath through RFID temperature or moisture sensors to be attached under the
nose or over the facemask. A flexible RFID sensor was demonstrated in [196]
and [198] (Fig. 16.a) for integration into a facemask or directly stuck on the
face and provided with a graphene-oxide electrode to monitor the moisture
emitted during breathing. This sensor is able to detect the
inhalation/exhalation cycles and abnormal patterns of respiration like apnea,
tachypnea, etc. (Fig. 16.c). Simpler and lower-cost breath detection [197] can
only involve a temperature sensor (even integrated into the RFID IC itself).
In this case, the overall device is smaller and suitable to be directly
applied close to the nostrils as an epidermal patch (Fig. 16.b). It measures
the temperature gradient of the air flux that is correlated to the inhalation
and exhalation rhythm and depth. To obtain better accuracy, bilateral breath
measurements can be achieved by dual-chip sensor tags as in [192].

(a) (b)

(c) (d)

(e) (f)

Figure 17: RFID sensors for _indirect_ respiratory function assessment. (a)
Sleep respiration monitoring system with passive COTS RFID tags (adapted from
[195]). (b) RSSI-based respiratory monitoring system with passive RFID tags
(adapted from [199]). (c) Motion inertial based cough monitoring RFID sensor
(adapted from [110]). (d) Fabric-integrated RFID tag for respiratory
assessment during sleep (adapted from [201]). (e) Non-invasive sleep
monitoring system based on bed-integrated RFID (adapted from [204]). (f)
Measurement setup of the RF-RVM system (adapted from [191]).

(a) (b)

(c) (d)

Figure 18: RFID sensors for cardiovascular evaluation. (a) Epidermal ECG RFID
device (adapted from [206]). (b) RF-ECG system with an array of COTS RFID tags
(adapted from [207]). (c) Fabric-integrated hearth rate RFID device (adapted
from [208]). (d) Battery-free multichannel ECG sensor (adapted from [211]).

An _indirect_ breath monitoring avoiding sensors on the face can be instead
based on the measurement of the torso expansion and contraction by placing
regular RFID tags on the abdomen, or even on the bed [204], for overnight
monitoring. Breath identification is based on the measurements of the
variations of RSSI [195, 199, 200] and phase [194, 201, 191]. Sleep
respiration rate and sleep apnea can be detected through a fixed reader
antenna at the bedside, and RFID tags can be attached [195] (Fig. 17.a) or
fabric-integrated [201] (Fig. 17.d) at the abdomen level. For instance, the
_TagBreathe_ platform in [194] resorts to an array of COTS RFID tags to
enhance the measurement robustness. Similarly, the respiratory volume can be
retrieved by collecting the temporal phase information from tags attached to
the chest and abdomen [191] (Fig. 17.f). Moreover, by placing the RFID tag on
the abdomen, the respiratory rate in a medical examination-like situation
[199] (Fig. 17.b) can be evaluated too.

By adding other RFID tags, for example, at the wrist area, additional
physiological parameters can be simultaneously collected, such as heart rate,
blood pressure, and respiration rate [200].

####  V-B3 Oxygen Saturation

The oxygen saturation in the blood can be captured by a wearable RFID device
[205] comprising a reflective oxygen probe and the standard CMOS technology,
whereas an active RFID tag is used to store and stream the data.

###  V-C Cardiovascular Evaluation

ECG data can be collected and streamed in real-time by means of an ultra-low-
power RFID BAP device to be placed at the center of the chest, where standard
clip electrodes collect the ECG signal [206, 210] (Fig. 18.a). The Heart Rate
Variability (HRV) can be quantified as well [207] with an array of COTS RFID
tags attached to the chest area within the clothes (Fig. 18.b), exploiting the
processing of the RSSI as described above for breath. Although the RFID reader
captures the RF signal reflected from both the heart movement due to heartbeat
and the tag movement caused by respiration, the estimated HRV is comparable to
existing wired techniques. Hearth Rate monitoring for infants can be achieved
too by sewing an ECG-enabled RFID sensor [209], comprising fabric electrodes,
onto the front bodice of a baby onesie [208] (Fig. 18.c). Multi-electrode RFID
sensors (Fig. 18.d) have been experimented too, for a more accurate ECG
recording without the use of an on-board battery [211].

(a) (b)

Figure 19: RFID sensors for sweat monitoring. (a) Epidermal RFID device for pH
sweat monitoring (adapted from [15]). (b) Textile-integrated RFID sweat rate
sensor tags (adapted from [175]).

(a)

(b)

Figure 20: (a)_NightCare_ system for sleep monitoring (adapted from [213],
[214]). (b) Classification of sleep quality and different body postures
(adapted from [214]).

###  V-D Psycho-Physical Stress Monitoring

A pandemic may also impact the psychological sphere of patients and operators
[227]. For example, the onset of dehydration, elevated biochemical stress, and
increasing overnight sweat loss are critical indicators associated with
COVID-19 [228].

####  V-D1 Sweat Analysis

Early signs or precursors of psychological diseases can be identified by the
chemical analysis of the sweat [229]. The pH index, which is correlated with
the presence of altered electrolytes, can be wirelessly monitored through a
flexible RFID device (Fig. 19.a) [15] equipped with an electrochemical printed
iridium-oxide sensor. Measurements can be taken on the fly in battery-less
mode from a distance up to $1$ m or continuously, provided that the device
works in battery-assisted mode [185]. Hence, monitoring can be executed even
during motion [212], as in the case of nurse activity in a hospital. Sweat
RFID sensors can also be integrated into textiles (by carving electro-textiles
or by screen-printing onto regular threads, Fig. 19.b), exploiting the change
in the tag’s substrate when it absorbs sweat [175].

####  V-D2 Quality of Sleep

COVID-19, and in general emergency conditions, also affects the quality of
sleep. As suggested by [9], measurements during sleep might provide a
significant insight on the health status, and in some cases, it may be the
only way to monitor a worker’s health effectively. For instance, the
_NightCare_ system [213, 214, 215] (Fig. 20.a) can quantitatively evaluate the
quality and phenomenology of the sleep through a set of wearable passive tags
placed onto the sleeper’s clothes and by also exploiting ambient tags placed
over the bed and onto the nearby carpets (Fig. 20.b). Finally, the sleeper can
be provided with wearable temperature sensor tags to additionally detect
anomalous temperature transients during the night [113].

##  VI Conclusions

The presented analysis suggests that an RFID-based country-level
infrastructure could provide valuable support to the management of pandemics.
The availability of devices is rich and currently increasing. Research
outcomes from laboratories could quickly move to proof of concepts and then to
products.

When applied over the large scale to a pandemic-like scenario, RFID frameworks
are expected to generate an unprecedented amount of data. Hence Edge Computing
[230] is needed to mitigate the data stream, whereas Artificial Intelligence
[231] should be used to extract profiles and identify anomalous events out of
the sea of data.

Furthermore, even more worrying issues for the social and industrial
acceptance of the RFID-centric approach to the pandemic war are (_i_) the
implementation costs of RFID systems and (_ii_) the privacy. Regarding the
implementation, although the costs of RFID systems in hospital is not
negligible [36], the estimated return of investment would be worth the initial
cost [29, 37] since the same system can be used for multiple purposes (e.g.
for the tracking staff and equipment [121, 126], even across different
hospitals [31]). Then, concerns about the privacy and safety related to the
RFID labellig of humans can lead to oral and practical forms of resistance, as
the tampering or the misuse of the systems [232]. To preserve the anonymity of
users, a _Crowd System_ [233] can be employed, wherein the tags inside “crowd
zones” repeatedly swap their identifiers. To secure the contact tracing, RFID
readers can exploit a blockchain architecture through the internet [133] so
that a person wearing the RFID tag can be notified if he/she has encountered a
confirmed infected person without disclosing his/her identity. Moreover, in
all the cases, the coverage of the reading infrastructure needs to be
optimized with ad-hoc network design methodologies [234, 235, 236], in
compliance with the electromagnetic compatibility issues [237]. Despite these
open issues, studies on technology adoption predict that the RFID will be
increasingly accepted and widespread in healthcare settings [238].

The main limitations of UHF RFID remain the limited read distance and the need
for a continuous remote power source to activate tags. The latter problem
could be mitigated through energy harvesting [239, 240], e.g., photovoltaic
cells for environmental and on-object tags [241], or piezoelectric [242],
vibration [243] and body-heat [244] powering for on-body sensors. Promising
opportunities would also arise by exploiting commodities signals to implement
backscattering communications among passive tags and routers, access points
and even smartphone and wearable devices. Finally, emerging 5G infrastructures
[245] will provide a further boost to improve interoperability, coverage
efficiency and bit-rate.

## Acknowledgments

The work was developed in the framework of the Dual-Skin project FISR 2020,
founded by MIUR, Italy.

## References

  * [1] M. Arons _et al._ , “Presymptomatic SARS-CoV-2 Infections and Transmission in a Skilled Nursing Facility,” _New England Journal of Medicine_ , vol. 382, April 2020.
  * [2] H. Ritchie _et al._ , “Coronavirus (COVID-19) Hospitalizations,” https://ourworldindata.org/covid-hospitalizations, 2021.
  * [3] M. Quian and J. Jiang, “COVID-19 and social distancing,” _Journal of Public Health: From Theory to Practice_ , vol. 2020, May 2020, doi: 10.1007/s10389-020-01321-z.
  * [4] S. Whitelaw, M. Mamas, E. Topol, and H. Van Spall, “Applications of digital technology in COVID-19 pandemic planning and response,” _The Lancet Digital Health_ , June 2020.
  * [5] World Health Organization, “COVID-19 vaccination: supply and logistics guidance,” https://www.who.int/publications/i/item/who-2019-ncov-vaccine-deployment-logistics-2021-1, 2021\.
  * [6] N. van Doremalen _et al._ , “Aerosol and surface stability of HCoV-19 (SARS-CoV-2) compared to SARS-CoV-1,” _medRxiv_ , 2020.
  * [7] P. de Man, S. Paltansing, D. Ong, N. Vaessen, G. Nielen, and J. Koeleman, “Outbreak of COVID-19 in a nursing home associated with aerosol transmission as a result of inadequate ventilation,” _Clinical infectious diseases: an official publication of the Infectious Diseases Society of America_ , August 2020.
  * [8] X. Ding _et al._ , “Wearable Sensing and Telehealth Technology with Potential Applications in the Coronavirus Pandemic,” _IEEE Reviews in Biomedical Engineering_ , vol. PP, pp. 1–1, May 2020.
  * [9] C. P. Adans-Dester _et al._ , “Can mHealth Technology Help Mitigate the Effects of the COVID-19 Pandemic? - Supplementary materials,” _IEEE Open Journal of Engineering in Medicine and Biology_ , vol. 1, pp. 243–248, 2020\.
  * [10] M. Nasajpour, S. Pouriyeh, R. Parizi, M. Dorodchi, M. Valero, and H. Arabnia, “Internet of Things for Current COVID-19 and Future Pandemics: an Exploratory Study,” _Journal of Healthcare Informatics Research_ , vol. 4, December 2020.
  * [11] Y. Dong and Y. Yao, “Iot platform for covid-19 prevention and control: A survey,” _IEEE Access_ , vol. 9, pp. 49 929–49 941, 2021.
  * [12] H. Habibzadeh, K. Dinesh, O. Rajabi Shishvan, A. Boggio-Dandry, G. Sharma, and T. Soyata, “A Survey of Healthcare Internet of Things (HIoT): A Clinical Perspective,” _IEEE Internet of Things Journal_ , vol. 7, no. 1, pp. 53–71, 2020.
  * [13] C. Occhiuzzi, S. Caizzone, and G. Marrocco, “Passive UHF RFID antennas for sensing applications: Principles, methods, and classifcations,” _IEEE Antennas and Propagation Magazine_ , vol. 55, no. 6, pp. 14–34, 2013\.
  * [14] Farsens, “Farsens endless opportunities: wireless battery free sensors,” http://www.farsens.com/en/, 2020.
  * [15] V. Mazzaracchio, L. Fiore, S. Nappi, G. Marrocco, and F. Arduini, “Medium-distance affordable, flexible and wireless epidermal sensor for pH monitoring in sweat,” _Talanta_ , vol. 222, p. 121502, August 2020.
  * [16] H. Yap, B. Ang, J. Lim, J. Goh, and C.-H. Yeow, “A fabric-regulated soft robotic glove with user intent detection using EMG and RFID for hand assistive application,” in _Proceedings - IEEE International Conference on Robotics and Automation_ , 2016, pp. 3537–3542.
  * [17] D. M. Dobkin, _The RF in RFID: passive UHF RFID in Practice_. Amsterdam: Elsevier, 2007.
  * [18] C. Occhiuzzi, S. Amendola, S. Nappi, N. D’Uva, and G. Marrocco, “RFID Technology for Industry 4.0: Architectures and Challenges,” in _2019 IEEE International Conference on RFID Technology and Applications (RFID-TA)_ , 2019, pp. 181–186.
  * [19] World Health Organization, “Rational use of personal protective equipment (PPE) for coronavirus disease (COVID-19),” https://www.who.int/publications/i/item/rational-use-of-personal-protective-equipment-for-coronavirus-disease-(covid-19)-and-considerations-during-severe-shortages, 2020\.
  * [20] S. Lockhart, J. Naidu, C. Badh, and L. Duggan, “Simulation as a tool for assessing and evolving your current personal protective equipment: lessons learned during the coronavirus disease (covid-19) pandemic,” _Canadian Journal of Anesthesia_ , vol. 67, no. 7, pp. 895–896, Mar 2020.
  * [21] J. Zamora, J. Murdoch, B. Simchison, and A. Day, “Contamination: A comparison of 2 personal protective systems,” _CMAJ_ , vol. 175, no. 3, pp. 249–254, Aug 2006.
  * [22] A. Mostaghimi _et al._ , “Regulatory and Safety Considerations in Deploying a Locally Fabricated, Reusable Face Shield in a Hospital Responding to the COVID-19 Pandemic,” _Clinical and Translational Resource and Technology Insights_ , vol. 1, no. 1, pp. 139–151, Dec 2020.
  * [23] D. Alexandre, M. Prieto, F. Beaumont, R. Taiar, and G. Polidori, “Wearing lead aprons in surgical operating rooms: ergonomic injuries evidenced by infrared thermography,” _Journal of Surgical Research_ , vol. 209, pp. 227–233, Mar 2017.
  * [24] N. Raabe Vanessa and B. Matthias, “Infection control during filoviral hemorrhagic fever outbreaks,” _Journal of Global Infectious Diseases_ , vol. 4, no. 1, pp. 69–74, Mar 2012.
  * [25] C. Occhiuzzi and G. Marrocco, “Constrained-design of passive UHF RFID sensor antennas,” _IEEE Transactions on Antennas and Propagation_ , vol. 61, no. 6, pp. 2972–2980, 2013.
  * [26] ——, “Precision and accuracy in UHF-RFID power measurements for passive sensing,” _IEEE Sensors Journal_ , vol. 16, no. 9, pp. 3091–3098, 2016.
  * [27] X. Qu, L. T. Simpson, and P. Stanfield, “A model for quantifying the value of RFID-enabled equipment tracking in hospitals,” _Advanced Engineering Informatics_ , vol. 25, no. 1, pp. 23 – 31, 2011.
  * [28] I. Mun, A. Kantrowitz, P. Carmel, K. Mason, and D. Engels, “Active RFID system augmented with 2D barcode for asset management in a hospital setting,” in _2007 IEEE International Conference on RFID, IEEE RFID 2007_ , Grapevine, TX, USA, Mar 2007, pp. 205–211.
  * [29] H. Hakim, R. Renouf, and J. Enderle, “Passive RFID asset monitoring system in hospital environments,” in _Bioengineering, Proceedings of the Northeast Conference_ , vol. 2006, Easton, PA, USA, Apr 2006, pp. 217–218.
  * [30] P. Jarritt, “Managing mobile medical devices using radiofrequency identification,” _British Journal of Health Care Management_ , vol. 20, no. 1, pp. 16–21, 2014.
  * [31] Y. Meiller, S. Bureau, W. Zhou, and S. Piramuthu, “RFID-embedded decision support for tracking surgical equipment,” in _Proceedings of the Annual Hawaii International Conference on System Sciences_ , Koloa, HI, United States, Jan 2011.
  * [32] T. Østbye _et al._ , “Evaluation of an infrared/radiofrequency equipment-tracking system in a tertiary care hospital,” _Journal of Medical Systems_ , vol. 27, no. 4, pp. 367–380, 2003.
  * [33] A. Shirehjini, A. Yassine, and S. Shirmohammadi, “Equipment location in hospitals using RFID-based positioning system,” _IEEE Transactions on Information Technology in Biomedicine_ , vol. 16, no. 6, pp. 1058–1069, 2012.
  * [34] M.-H. Tsai, C.-S. Pan, C.-W. Wang, J.-M. Chen, and C.-B. Kuo, “RFID medical equipment tracking system based on a location-based service technique,” _Journal of Medical and Biological Engineering_ , vol. 39, no. 1, pp. 163–169, 2019.
  * [35] S. Kumar, E. Swanson, and T. Tran, “RFID in the healthcare supply chain: usage and application,” _International Journal of Health Care Quality Assurance_ , vol. 22, no. 1, pp. 67–81, Apr 2008.
  * [36] A. Coustasse, S. Tomblin, and C. Slack, “Impact of radio-frequency identification (RFID) technologies on the hospital supply chain: a literature review,” _Perspectives in Health Information Management_ , pp. 1–17, Oct 2013.
  * [37] A. D. Smith, “RFID Applications in Healthcare Systems From an Operational Perspective,” _International Journal of Systems and Society_ , vol. 6, no. 2, pp. 1–28, Nov 2019.
  * [38] G. Borelli, P. Orrù, and F. Zedda, “Performance analysis of a healthcare supply chain. a RFID system implementation design,” in _Proceedings of the Summer School Francesco Turco_ , Senigallia, Italy, Sept 2013, pp. 42–47.
  * [39] E. Katsiri, K. Pramatari, A. Billiris, A. Kaiafas, A. Christodoulakis, and H. Karanikas, “RFcure: An RFID based blood bank/healthcare information management system,” in _IFMBE Proceedings_ , Paphos, Cyprus, Sept 2016, pp. 853–858.
  * [40] R. Kalra, P. Shetty, S. Mutalik, U. Nayak, M. Reddy, and N. Udupa, “Pharmaceutical applications of radio-frequency identification,” _Systematic Reviews in Pharmacy_ , vol. 3, no. 1, pp. 24–30, Feb 2012.
  * [41] P. H. Cole and D. C. Ranasinghe, _Networked RFID Systems and Lightweight Cryptography_ , Sept 2008.
  * [42] T. Inaba, _EPC system for a safe and secure supply chain and how it is applied_ , 2008.
  * [43] M. Safkhani, S. Rostampour, Y. Bendavid, and N. Bagheri, “IoT in medical and pharmaceutical: Designing lightweight RFID security protocols for ensuring supply chain integrity,” _Computer Networks_ , vol. 181, p. 107558, 2020\.
  * [44] G. Huang, Z. Qin, T. Qu, and Q. Dai, “RFID-enabled pharmaceutical regulatory traceability system,” in _Proceedings of 2010 IEEE International Conference on RFID-Technology and Applications, RFID-TA 2010_ , 2010, pp. 211–216.
  * [45] R. Raj, N. Rai, and S. Agarwal, “Anticounterfeiting in Pharmaceutical Supply Chain by establishing Proof of Ownership,” in _TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON)_ , Kochi, India, Oct 2019, pp. 1572–1577.
  * [46] K. Toyoda, P. T. Mathiopoulos, I. Sasase, and T. Ohtsuki, “A novel blockchain-based product ownership management system (poms) for anti-counterfeits in the post supply chain,” _IEEE Access_ , vol. 5, pp. 17 465–17 477, June 2017.
  * [47] S. Bauk, A. Schmeink, and J. Colomer, “An RFID model for improving workers’ safety at the seaport in transitional environment,” _Transport_ , vol. 33, no. 2, pp. 353–363, 2018.
  * [48] A. Kelm _et al._ , “Mobile passive radio frequency identification (RFID) portal for automated and rapid control of personal protective equipment (PPE) on construction sites,” _Automation in Construction_ , vol. 36, pp. 38 – 52, 2013.
  * [49] C. Musu, V. Popescu, and D. Giusto, “Workplace safety monitoring using RFID sensors,” in _2014 22nd Telecommunications Forum Telfor (TELFOR)_ , Belgrade, Serbia, Nov 2014, pp. 656–659.
  * [50] M. K. N. Mahmad _et al._ , “Applications of radio frequency identification (RFID) in mining industries,” in _International Conference on Innovative Research 2016_ , Iasi, Romania, May 2016.
  * [51] M. H. Hung, L. T. Lan, and H. S. Hong, “A Deep Learning-Based Method for Real-Time Personal Protective Equipment Detection,” _Journal of Science and Technique_ , vol. 199, no. 13, pp. 23–34, June 2019.
  * [52] R. D. W. Pradana _et al._ , “Identification system of personal protective equipment using convolutional neural network (cnn) method,” in _2019 International Symposium on Electronics and Smart Devices (ISESD)_ , Badung-Bali, Indonesia, Oct 2019, pp. 1–6.
  * [53] M. Helmus, A. Kelm, A. Meins-Becker, D. Platz, and K. M. Javad, “Life cycle data of PPE with RFID sensors - new research results,” in _11th European Seminar on Personal Protective Equipment_ , Saariselka, Finland, Jan 2012, pp. 1–5.
  * [54] M. Sole, C. Musu, F. Boi, D. Giusto, and V. Popescu, “RFID sensor network for workplace safety management,” in _2013 IEEE 18th Conference on Emerging Technologies Factory Automation (ETFA)_ , Cagliari, Italy, Sept 2013, pp. 1–4.
  * [55] S. Barro-Torres, T. M. Fernández-Caramés, H. J. Pérez-Iglesias, and C. J. Escudero, “Real-time personal protective equipment monitoring system,” _Computer Communications_ , vol. 36, no. 1, pp. 42 – 50, 2012.
  * [56] E. M. Mandar, W. Dachry, and B. Bensassi, “Toward a real-time personal protective equipment compliance control system based on RFID technology,” in _1st International Conference of Advanced Computing and Informatics_ , Casablanca, Morocco, Apr 2020, pp. 553–565.
  * [57] C. M. Cabreira _et al._ , “RFID applied to protective equipment inspection,” in _2015 IEEE Brasil RFID_ , São Paulo, Brasil, Oct 2015, pp. 1–4.
  * [58] J. Smith _et al._ , “RFID-based techniques for human-activity detection,” _Communications of the ACM_ , vol. 48, no. 9, pp. 39–44, 2005\.
  * [59] S. Dong, H. Li, and Q. Yin, “Building information modeling in combination with real time location systems and sensors for safety performance enhancement,” _Safety Science_ , vol. 102, pp. 226 – 237, 2018.
  * [60] G. M. Bianco and G. Marrocco, “Sensorized facemask with moisture-sensitive RFID antenna,” _IEEE Sensors Letters_ , vol. 5, no. 3, pp. 1–4, Feb. 2021, doi:10.1109/LSENS.2021.3059348.
  * [61] H. Liu and Z. Yao, “The research on recycling management model of medical waste based on RFID technology,” _Fresenius Environmental Bulletin_ , vol. 26, no. 8, pp. 5069–5081, Aug 2017.
  * [62] H. Liu, Z. Yao, F. Chang, and S. Meyer, “An RFID-based medical waste transportation management system: Assessment of a new model on a hospital in china,” _Fresenius Environmental Bulletin_ , vol. 29, no. 2, pp. 773–784, Feb 2020.
  * [63] E. Katsiri and K. Moschou, “A pervasive computing system for the remote management of hospital waste,” _Journal of Communications Software and Systems_ , vol. 12, no. 1, pp. 53–66, Mar 2016.
  * [64] P. Nolz, N. Absi, and D. Feillet, “A stochastic inventory routing problem for infectious medical waste collection,” _Networks_ , vol. 63, no. 1, pp. 82–95, Jan 2014.
  * [65] S. Sun, J. Hu, Y. Cao, and W. Zhou, “Discussion on the application of RFID technology in medical waste management,” in _Proceedings - 10th International Conference on Information Technology in Medicine and Education, ITME 2019_ , Qingdao, China, Aug 2019, pp. 120–124.
  * [66] I. Bose and S. Yan, “The green potential of RFID projects: A case-based analysis,” _IT Professional_ , vol. 13, no. 1, pp. 41 – 47, Jan. 2011.
  * [67] H. Liu and Z. Yao, “Research on the reverse logistics management of medical waste based on the RFID technology,” _Fresenius Environmental Bulletin_ , vol. 25, no. 12, pp. 8084–8092, Dec. 2017.
  * [68] F. Villa-Gonzalez, R. Bhattacharyya, and S. Sarma, “Single and bulk identification of plastics in the recycling chain using chipless RFID tags,” in _15th Annual Conference on RFID_ , 2021.
  * [69] L. Bonanni, E. Arroyo, C.-H. Lee, and T. Selker, “Smart sinks: Real-world opportunities for context-aware interaction,” in _Conference on Human Factors in Computing Systems_ , Portland, OR, USA, April 2005, pp. 1232–1235.
  * [70] E.-L. Do, “Technological interventions for hand hygiene adherence: Research and intervention for smart patient room,” in _Joining Languages, Cultures and Visions - CAADFutures 2009, Proceedings of the 13th International CAAD Futures Conference_ , Montréal, Canada, June 2009, pp. 303–313.
  * [71] K. Radhakrishna _et al._ , “Real-time feedback for improving compliance to hand sanitization among healthcare workers in an open layout icu using radiofrequency identification,” _Journal of Medical Systems_ , vol. 39, no. 6, 2015.
  * [72] A. Decker, G. Cipriano, G. Tsouri, and J. Lavigne, “Monitoring pharmacy student adherence to world health organization hand hygiene indications using radio frequency identification,” _American Journal of Pharmaceutical Education_ , vol. 80, no. 3, 2016.
  * [73] R. Johnson, G. Tsouri, and E. Walsh, “Continuous and automated measuring of compliance of hand-hygiene procedures using finite state machines and RFID,” _IEEE Instrumentation and Measurement Magazine_ , vol. 15, no. 2, pp. 8–12, 2012.
  * [74] M. Meydanci, C. Adali, M. Ertas, M. Dizbay, and A. Akan, “RFID based hand hygiene compliance monitoring station,” in _2013 IEEE International Conference on Control System, Computing and Engineering (ICCSCE 2013)_ , Penang, Malaysia, Nov 2013, pp. 573–576.
  * [75] L. Pineles _et al._ , “Accuracy of a radiofrequency identification (RFID) badge system to monitor hand hygiene behavior during routine clinical activities,” _American Journal of Infection Control_ , vol. 42, no. 2, pp. 144–147, 2014.
  * [76] A. Pleteršek, M. Sok, and J. Trontelj, “Monitoring, control and diagnostics using RFID infrastructure,” _Journal of Medical Systems_ , vol. 36, no. 6, pp. 3733–3739, 2012.
  * [77] Axzon, “RFM3300-E Magnus-S3 M3E passive sensor IC,” https://axzon.com/rfm3300-e-magnus-s3-m3e-passive-sensor-ic/.
  * [78] AMS, “SL900A EPC Gen2 Sensor Tag and Data Logger IC,” https://ams.com/documents/20143/36005/SL900A_DS000294_5-00.pdf/d399f354-b0b6-146f-6e98-b124826bd737.
  * [79] EM Microelectronic EM4325, https://www.emmicroelectronic.com/sites/default/files/products/datasheets/4325-ds_0.pdf.
  * [80] Asygn, “Asygn AS321X - Passive UHF RFID chip,” https://asygn.com/as321x/.
  * [81] S. Amendola, R. Lodato, S. Manzari, C. Occhiuzzi, and G. Marrocco, “RFID Technology for IoT-Based Personal Healthcare in Smart Spaces,” _IEEE Internet of Things Journal_ , vol. 1, no. 2, pp. 144–152, 2014.
  * [82] World Health Organization, “Personal protective equipment (PPE) for different healthcare activities,” https://www.who.int/bangladesh/emergencies/coronavirus-disease-(covid-19)-update/steps-to-put-on-personal-protective-equipment-(ppe), 2020\.
  * [83] M. Holland, D. J. Zaloga, and C. S. Friderici, “COVID-19 Personal Protective Equipment (PPE) for the emergency physician,” _Visual Journal of Emergency Medicine_ , vol. 19, 2020.
  * [84] M. Ippolito, C. Gregoriano, A. Cortegiani, and P. Iozzo, “Counterfeit filtering facepiece respirators are posing an additional risk to health care workers during COVID-19 pandemic,” _American Journal of Infection and Control_ , vol. 48, pp. 853–858, 2020.
  * [85] P. Pitts, “The spreading cancer of counterfeit drugs,” _Journal of Commercial Biotechnology_ , vol. 25, no. 3, pp. 20–14, 2020.
  * [86] T. M. Cook, “Personal protective equipment during the coronavirus disease (covid) 2019 pandemic - a narrative review,” _Anaesthesia_ , vol. 75, no. 7, pp. 920–927, Apr 2020.
  * [87] C. Houghton _et al._ , “Barriers and facilitators to healthcare workers’ adherence with infection prevention and control (IPC) guidelines for respiratory infectious diseases: a rapid qualitative evidence synthesis,” _Cochrane Database of Systematic Reviews_ , no. 4, 2020.
  * [88] S. Highnett, R. Welsh, and J. Banerjee, “Human factors issues of working in personal protective equipment during the COVID-19 pandemic,” _Anaesthesia_ , 2020.
  * [89] Q. Jiang _et al._ , “The prevalence, characteristics, and related factors of pressure injury in medical staff wearing personal protective equipment against covid-19 in china: A multicentre cross-sectional survey,” _International Wound Journal_ , vol. 17, no. 5, pp. 1300–1309, Apr 2020.
  * [90] S. Gao _et al._ , “Performance of N95 FFRs Against Combustion and NaCl Aerosols in Dry and Moderately Humid Air: Manikin-based Study,” _Annals of Occupational Hygiene_ , vol. 60, no. 6, pp. 748–760, Apr 2016\.
  * [91] A. Pacitto _et al._ , “Effectiveness of commercial face masks to reduce personal PM exposure,” _Science of Total Environment_ , vol. 650, no. 1, pp. 1592–1590, Feb. 2019.
  * [92] J. J. Klemes, Y. V. Fang, and P. Jian, “The energy and environmental footprints of COVID-19 fighting measures e PPE, disinfection, supply chains,” _Energy_ , vol. 211, August 2020.
  * [93] J. E. Bardram, A. Doryab, R. M. Jensen, P. M. Lange, K. L. G. Nielsen, and S. T. Petersen, “Phase recognition during surgical procedures using embedded and body-worn sensors,” in _2011 IEEE International Conference on Pervasive Computing and Communications (PerCom)_ , Seattle, WA, USA, Mar 2011, pp. 45–53.
  * [94] S. Denis, R. Berkvens, and M. Weyn, “A survey on detection, tracking and identification in radio frequency-based device-free localization,” _Sensors (Switzerland)_ , vol. 19, no. 23, 2019.
  * [95] L. Gholamhosseini, F. Sadoughi, and A. Safaei, “Hospital real-time location system (a practical approach in healthcare): A narrative review article,” _Iranian Journal of Public Health_ , vol. 48, no. 4, pp. 593–602, 2019.
  * [96] F. Vivaldi _et al._ , “A temperature-sensitive RFID tag for the identification of cold chain failures,” _Sensors and Actuators, A: Physical_ , vol. 313, pp. 1–18, 2020.
  * [97] L. Hu, C. Xiang, and C. Qi, “Research on traceability of cold chain logistics based on RFID and EPC,” in _IOP Conference Series: Materials Science and Engineering_ , vol. 790, no. 1, 2020, pp. 1–5.
  * [98] S. Caizzone, C. Occhiuzzi, and G. Marrocco, “Multi-chip RFID antenna integrating shape-memory alloys for detection of thermal thresholds,” _IEEE Transactions on Antennas and Propagation_ , vol. 59, no. 7, pp. 2488–2494, Jul 2011.
  * [99] E. Mahase, “Covid-19: What do we know about the late stage vaccine candidates?” _The BMJ_ , vol. 371, pp. 1–2, Nov 2020. [Online]. Available: https://www.bmj.com/content/371/bmj.m4576
  * [100] World Health Organization, “Advice on the use of masks in the context of COVID-19,” https://www.who.int/publications/i/item/advice-on-the-use-of-masks-in-the-community-during-home-care-and-in-healthcare-settings-in-the-context-of-the-novel-coronavirus-(2019-ncov)-outbreak, 2020\.
  * [101] M. C. Caccami, M. Y. S. Mulla, C. Di Natale, and G. Marrocco, “Wireless monitoring of breath by means of a graphene oxide-based radiofrequency identification wearable sensor,” in _2017 11th European Conference on Antennas and Propagation (EUCAP)_ , 2017, pp. 3394–3396.
  * [102] E. S. Windfeld and M. S.-L. Brooks, “Medical waste management - a review,” _Journal of Environmental Management_ , vol. 163, pp. 98–108, Nov 2015. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0301479715302176
  * [103] M. Hannan, M. Abdulla Al Mamun, A. Hussain, H. Basri, and R. Begum, “A review on technologies and their usage in solid waste monitoring and management systems: Issues and challenges,” _Waste Management_ , vol. 43, pp. 509–523, 2015.
  * [104] D. Wu, W. W. Y. Ng, P. P. K. Chan, H. Ding, B. Jing, and D. S. Yeung, “Access control by RFID and face recognition based on neural network,” in _2010 International Conference on Machine Learning and Cybernetics_ , vol. 2, 2010, pp. 675–680.
  * [105] X. Pan, “Research and Implementation of Access Control System Based on RFID and FNN-Face Recognition,” in _2012 Second International Conference on Intelligent System Design and Engineering Application_ , 2012, pp. 716–719.
  * [106] T. Louati, F. Ounnar, P. Pujo, and C. Pistoresi, “A simulation test bed of intelligent access control based on biometric and RFID,” in _2012 IEEE International Conference on RFID-Technologies and Applications (RFID-TA)_ , 2012, pp. 297–302.
  * [107] M. Kishwar Shafin _et al._ , “Development of an RFID based access control system in the context of Bangladesh,” in _2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS)_ , 2015, pp. 1–5.
  * [108] K. Bakht, A. U. Din, A. Shehzadi, and M. Aftab, “Design of an Efficient Authentication and Access Control System Using RFID,” in _2019 3rd International Conference on Energy Conservation and Efficiency (ICECE)_ , 2019, pp. 1–4.
  * [109] S. Amendola, C. Occhiuzzi, and G. Marrocco, “RFID sensing networks for critical infrastructure security: A real testbed in an energy smart grid,” in _2017 IEEE International Conference on RFID Technology Application (RFID-TA)_ , 2017, pp. 106–110.
  * [110] S. Manzari, S. Pettinari, and G. Marrocco, “Miniaturized and tunable wearable RFID tag for body-centric applications,” in _2012 IEEE International Conference on RFID-Technologies and Applications (RFID-TA)_ , 2012, pp. 239–243.
  * [111] A. Buffi, B. Tellini, A. Motroni, and P. Nepa, “A Phase-based Method for UHF RFID Gate Access Control,” in _2019 IEEE International Conference on RFID Technology and Applications (RFID-TA)_ , 2019, pp. 131–135.
  * [112] S. Wen, H. Heidari, A. Vilouras, and R. Dahiya, “A wearable fabric-based RFID skin temperature monitoring patch,” in _2016 IEEE SENSORS_ , 2016, pp. 1–3.
  * [113] S. Amendola, G. Bovesecchi, A. Palombi, P. Coppa, and G. Marrocco, “Design, Calibration and Experimentation of an Epidermal RFID Sensor for Remote Temperature Monitoring,” _IEEE Sensors Journal_ , vol. 16, pp. 1–1, October 2016.
  * [114] i-Tek - Body Temperature Monitoring System for Employees and Civil Services, https://infoteksoftware.com/rfid-solution/rfid-based-body-temperature-monitoring-for-workers.html?vertical=health-care-vertical.
  * [115] Y.-W. Chen _et al._ , “A RFID model of transferring and tracking trauma patients after a large disaster,” in _2009 IEEE/INFORMS International Conference on Service Operations, Logistics and Informatics, SOLI 2009_ , Chicago, IL, USA, Jul 2009, pp. 98–101.
  * [116] P.-J. Chen, Y.-F. Chen, S.-K. Chai, and Y.-F. Huang, “Implementation of an RFID-based management system for operation room,” in _2009 International Conference on Machine Learning and Cybernetics_ , vol. 5, Baoding, China, Jul 2009, pp. 2933–2938.
  * [117] B. Chowdhury and R. Khosla, “RFID-based hospital real-time patient management system,” in _Proceedings - 6th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2007; 1st IEEE/ACIS International Workshop on e-Activity, IWEA 2007_ , Melbourne, Australia, Jul 2007, pp. 363–368.
  * [118] T. Harry, M. Taylor, R. Fletcher, A. Mundt, and T. Pawlicki, “Passive tracking of linac clinical flow using radiofrequency identification technology,” _Practical Radiation Oncology_ , vol. 4, no. 1, pp. e85–e90, 2014.
  * [119] M. Pérez, C. Dafonte, and A. Gómez, “Traceability in patient healthcare through the integration of RFID technology in an ICU in a hospital,” _Sensors (Switzerland)_ , vol. 18, no. 5, 2018.
  * [120] Q. Cao, D. R. Jones, and H. Sheng, “Contained nomadic information environments: Technology, organization, and environment influences on adoption of hospital RFID patient tracking,” _Information and Management_ , vol. 51, no. 2, pp. 225 – 239, 2014.
  * [121] C.-H. Cheng and Y.-H. Kuo, “RFID analytics for hospital ward management,” _Flexible Services and Manufacturing Journal_ , vol. 28, no. 4, pp. 593–616, 2016.
  * [122] B. Chhetri, A. Alsadoon, P. Prasad, H. Venkata, and A. Elchouemi, “Enhanced weighted centroid localization in RFID technology: Patient movement tracking in hospital,” in _2019 5th International Conference on Advanced Computing and Communication Systems, ICACCS 2019_ , Coimbatore, India, Mar 2019, pp. 910–915.
  * [123] E. Iadanza, F. Dori, R. Miniati, and R. Bonaiuti, “Patients tracking and identifying inside hospital: A multilayer method to plan an RFId solution,” in _Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS’08 - "Personalized Healthcare through Technology"_ , 2008, pp. 1462–1465.
  * [124] M. Magliulo, L. Cella, and R. Pacelli, “Novel technique radio frequency identification (RFID) based to manage patient flow in a radiotherapy department,” in _Proceedings of 2012 IEEE-EMBS International Conference on Biomedical and Health Informatics_ , 2012, pp. 972–975.
  * [125] L. Mapa and K. Saha, “Application of RFID technology in patient management system,” in _Proceedings of 122nd ASEE Annual Conference and Exposition_ , Seattle, WA, USA, June 2015, pp. 2–17.
  * [126] P. Najera, J. Lopez, and R. Roman, “Real-time location and inpatient care systems based on passive RFID,” _Journal of Network and Computer Applications_ , vol. 34, no. 3, pp. 980–989, 2011.
  * [127] D.-S. Kim, J. Kim, S.-H. Kim, and S. Yoo, “Design of RFID based the patient management and tracking system in hospital,” in _Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS’08 - "Personalized Healthcare through Technology"_ , 2008, pp. 1459–1461.
  * [128] P. Newman-Casey, J. Musser, L. Niziol, K. Shedden, D. Burke, and A. Cohn, “Designing and validating a low-cost real time locating system to continuously assess patient wait times,” _Journal of Biomedical Informatics_ , vol. 106, 2020.
  * [129] A. Kapoor _et al._ , “Estimating physical work-load on ed clinicians and staff using real-time location systems,” in _15th Annual Conference on RFID_ , Apr. 2021.
  * [130] M. Pérez, G. González, and C. Dafonte, “The development of an RFID solution to facilitate the traceability of patient and pharmaceutical data,” _Sensors (Switzerland)_ , vol. 17, no. 10, 2017.
  * [131] S. Bahri, “Enhancing quality of data through automated SARS contact tracing method using RFID technology,” _International Journal of Networking and Virtual Organisations_ , vol. 4, no. 2, pp. 145–162, May 2007.
  * [132] Y.-T. Chang, S. Syed-Abdul, C.-Y. Tsai, and Y.-C. Li, “A novel method for inferring RFID tag reader recordings into clinical events,” _International Journal of Medical Informatics_ , vol. 80, no. 12, pp. 872–880, Dec. 2011.
  * [133] L. Garg, E. Chukwu, N. Nasser, C. Chakraborty, and G. Garg, “Anonymity preserving iot-based covid-19 and other infectious disease contact tracing model,” _IEEE Access_ , vol. 8, pp. 159 402–159 414, Aug. 2020.
  * [134] D. Lowery-North _et al._ , “Measuring social contacts in the emergency department,” _PLoS ONE_ , vol. 8, no. 8, Aug. 2013, art. no. e70854.
  * [135] C. Cattuto, W. van den Broeck, A. Barrat, V. Colizza, J.-F. Pinton, and A. Vespignani, “Dynamics of person-to-person interactions from distributed RFID sensor networks,” _PLoS ONE_ , vol. 5, no. 7, 2010, art. no. e11596.
  * [136] J. Stehlé _et al._ , “Simulation of an SEIR infectious disease model on the dynamic contact network of conference attendees,” _BMC Medicine_ , vol. 9, 2011, art. no. 87.
  * [137] T. Smieszek, S. Castell, A. Barrat, C. Cattuto, P. White, and G. Krause, “Contact diaries versus wearable proximity sensors in measuring contact patterns at a conference: Method comparison and participants’ attitudes,” _BMC Infectious Diseases_ , vol. 16, no. 1, 2016, art. no. 341.
  * [138] P. Nikitin, S. Ramamurthy, R. Martinez, and K. Rao, “Passive tag-to-tag communication,” in _2012 IEEE International Conference on RFID, RFID 2012_ , 2012, pp. 177–184.
  * [139] G. Marrocco and S. Caizzone, “Electromagnetic models for passive tag-to-tag communications,” _IEEE Transactions on Antennas and Propagation_ , vol. 60, no. 11, pp. 5381–5389, 2012.
  * [140] Y. Karimi, A. Athalye, S. Das, P. Djuric, and M. Stanacevic, “Design of a backscatter-based tag-to-tag system,” in _2017 IEEE International Conference on RFID, RFID 2017_ , 2017, pp. 6–12.
  * [141] D. De Donno, F. Ricciato, L. Catarinucci, and L. Tarricone, “Design and applications of a software-defined listener for UHF RFID systems,” in _IEEE MTT-S International Microwave Symposium Digest_ , 2011.
  * [142] D. Piumwardane, C. Rohner, and T. Voigt, “Reliable flooding in dense backscatter-based tag-to-tag networks,” in _15th Annual Conference on RFID_ , 2021.
  * [143] A. Borisenko, M. Bolic, and M. Rostamian, “Intercepting UHF RFID signals through synchronous detection,” _Eurasip Journal on Wireless Communications and Networking_ , vol. 2013, no. 1, 2013.
  * [144] M. Bolic, M. Rostamian, and P. Djuric, “Proximity detection with RFID: A step toward the internet of things,” _IEEE Pervasive Computing_ , vol. 14, no. 2, pp. 70–76, 2015.
  * [145] L. Isella _et al._ , “Close encounters in a pediatric ward: Measuring face-to-face proximity and mixing patterns with wearable sensors,” _PLoS ONE_ , vol. 6, no. 2, 2011.
  * [146] S. Saab and H. Msheik, “Novel RFID-based pose estimation using single stationary antenna,” _IEEE Transactions on Industrial Electronics_ , vol. 63, no. 3, pp. 1842–1852, 2016.
  * [147] G. Alvarez-Narciandi, J. Laviada, M. Pino, and F. Las-Heras, “Attitude estimation based on arrays of passive RFID tags,” _IEEE Transactions on Antennas and Propagation_ , vol. 66, no. 5, pp. 2534–2544, 2018.
  * [148] R. Krigslund, P. Popovski, G. Pedersen, and K. Bank, “Potential of RFID systems to detect object orientation,” in _IEEE International Conference on Communications_ , 2011.
  * [149] N. Barbot, O. Rance, and E. Perret, “Angle sensor based on chipless RFID tag,” _IEEE Antennas and Wireless Propagation Letters_ , vol. 19, no. 2, pp. 233–237, 2020.
  * [150] M. Akbar, D. Taylor, and G. Durgin, “Planar position and orientation estimation using a 5.8 GHz RFID system,” in _2019 IEEE International Conference on RFID Technology and Applications, RFID-TA 2019_ , 2019, pp. 252–257.
  * [151] F. Xiao, Z. Wang, N. Ye, R. Wang, and X.-Y. Li, “One more tag enables fine-grained RFID localization and tracking,” _IEEE/ACM Transactions on Networking_ , vol. 26, no. 1, pp. 161–174, 2018.
  * [152] N. Cho, S.-J. Song, S. Kim, S. Kim, and H.-J. Yoo, “A 5.1-/spl mu/W UHF RFID tag chip integrated with sensors for wireless environmental monitoring,” in _Proceedings of the 31st European Solid-State Circuits Conference, 2005. ESSCIRC 2005._ , 2005, pp. 279–282.
  * [153] Z. Qi, Y. Zhuang, X. Li, W. Liu, Y. Du, and B. Wang, “Full passive UHF RFID Tag with an ultra-low power, small area, high resolution temperature sensor suitable for environment monitoring,” _Microelectronics Journal_ , vol. 45, no. 1, pp. 126–131, 2014.
  * [154] C. Occhiuzzi, S. Amendola, S. Manzari, S. Caizzone, and G. Marrocco, “Configurable RFID Sensing Antenna Breadboard for Industrial IoT,” _Electronics Letters_ , vol. 53, December 2016.
  * [155] I. A. Oprea, N. Barsan, U. Weimar, M.-L. Bauersfeld, D. Ebling, and J. Wöllenstein, “Capacitive Humidity Sensors on Flexible RFID Labels,” _Sensors and Actuators B: Chemical_ , vol. 132, pp. 404–410, June 2008.
  * [156] J. Siden, J. Gao, and B. Neubauer, “Microstrip antennas for remote moisture sensing using passive RFID,” in _2009 Asia Pacific Microwave Conference_ , 2009, pp. 2375–2378.
  * [157] J. Virtanen, L. Ukkonen, T. Bjorninen, A. Z. Elsherbeni, and L. Sydänheimo, “Inkjet-Printed Humidity Sensor for Passive UHF RFID Systems,” _IEEE Transactions on Instrumentation and Measurement_ , vol. 60, no. 8, pp. 2768–2777, 2011.
  * [158] S. Manzari, C. Occhiuzzi, S. Nawale, A. Catini, C. Di Natale, and G. Marrocco, “Humidity Sensing by Polymer-Loaded UHF RFID Antennas,” _IEEE Sensors Journal_ , vol. 12, no. 9, pp. 2851–2858, 2012.
  * [159] Y. Amin, Y. Feng, Q. Chen, L.-R. Zheng, and H. Tenhunen, “RFID antenna humidity sensor co-design for USN applications,” _IEICE Electronics Express_ , vol. 10, pp. 20 130 003–20 130 003, January 2013.
  * [160] S. Manzari, A. Catini, C. Di Natale, and G. Marrocco, “Ambient sensing by chemical-loaded UHF-RFIDs,” in _2013 7th European Conference on Antennas and Propagation (EuCAP)_ , 2013, pp. 1718–1720.
  * [161] S. Manzari and G. Marrocco, “Modeling and Applications of a Chemical-Loaded UHF RFID Sensing Antenna With Tuning Capability,” _IEEE Transactions on Antennas and Propagation_ , vol. 62, no. 1, pp. 94–101, 2014.
  * [162] R. Colella, L. Catarinucci, and L. Tarricone, “Improved Battery-Less Augmented RFID Tag: Application on Ambient Sensing and Control,” _IEEE Sensors Journal_ , vol. 16, no. 10, pp. 3484–3485, 2016.
  * [163] World Health Organization, “Hand Hygiene: Why, How & When?” https://www.who.int/gpsc/5may/Hand_Hygiene_Why_How_and_When_Brochure.pdf, 2020\.
  * [164] C. Dawson and J. Mackrill, “Review of technologies available to improve hand hygiene compliance - are they fit for purpose?” _Journal of Infection Prevention_ , vol. 15, no. 6, pp. 222–228, 2014.
  * [165] S. Amendola, C. Occhiuzzi, S. Manzari, and G. Marrocco, “RFID-based multi-level sensing network for industrial internet of things,” in _Studies in Computational Intelligence_ , June 2018, pp. 1–24.
  * [166] M. E. Kretzschmar, G. Rozhnova, M. C. J. Bootsma, M. van Boven, J. H. H. M. van de Wijgert, and M. J. M. Bonten, “Impact of delays on effectiveness of contact tracing strategies for COVID-19: a modelling study,” _The Lancet Public Health_ , vol. 5, no. 8, pp. e452–e459, 2020.
  * [167] D. Mareiniss, “The impending storm: Covid-19, pandemics and our overwhelmed emergency departments,” _American Journal of Emergency Medicine_ , vol. 38, no. 6, pp. 1293–1294, 2020.
  * [168] C. Maringe _et al._ , “The impact of the COVID-19 pandemic on cancer deaths due to delays in diagnosis in england, uk: a national, population-based, modelling study,” _The Lancet Oncology_ , vol. 21, no. 8, pp. 1023–1034, 2020.
  * [169] M. Makoni, “Covid-19 worsens zimbabwe’s health crisis,” _Lancet (London, England)_ , vol. 396, no. 10249, p. 457, 2020.
  * [170] A. Spena, L. Palombi, M. Corcione, M. Carestia, and V. Spena, “On the Optimal Indoor Air Conditions for SARS-CoV-2 Inactivation. An Enthalpy-Based Approach,” _International Journal of Environmental Research and Public Health_ , vol. 17, p. 6083, August 2020.
  * [171] S. A. Quraishi, L. Berra, and A. Nozari, “Indoor temperature and relative humidity in hospitals: workplace considerations during the novel coronavirus pandemic,” _Occupational and Environmental Medicine_ , vol. 77, no. 7, pp. 508–508, 2020.
  * [172] “COVID-19 Indoor Capacity Limitations: Instructions and Resources,” https://www.sccgov.org/sites/covid19/Pages/covid-capacity.aspx, 2021.
  * [173] Centers for Disease Control and Prevention, “Definitions of Symptoms for Reportable Illnesses,” https://www.cdc.gov/quarantine/air/reporting-deaths-illness/definitions-symptoms-reportable-illnesses.html, 2020\.
  * [174] C. Occhiuzzi, S. Amendola, S. Manzari, and G. Marrocco, “Industrial RFID sensing networks for critical infrastructure security,” in _2016 46th European Microwave Conference (EuMC)_ , 2016, pp. 1335–1338.
  * [175] S. Merilampi, Han He, L. Sydänheimo, L. Ukkonen, and J. Virkki, “The possibilities of passive UHF RFID textile tags as comfortable wearable sweat rate sensors,” in _2016 Progress in Electromagnetic Research Symposium (PIERS)_ , 2016, pp. 3984–3987.
  * [176] T. Bjorninen, J. Virkki, L. Sydanheimo, and L. Ukkonen, “Manufacturing of antennas for passive UHF RFID tags by direct write dispensing of copper and silver inks on textiles,” in _2015 International Conference on Electromagnetics in Advanced Applications (ICEAA)_ , 2015, pp. 589–592.
  * [177] S. Amendola _et al._ , “UHF epidermal sensors: Technology and applications,” 11 2020, pp. 133–161.
  * [178] S.-W. Wang, W.-H. Chen, C.-S. Ong, L. Liu, and Y.-W. Chuang, “RFID Application in Hospitals: A Case Study on a Demonstration RFID Project in a Taiwan Hospital,” in _Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS’06)_ , vol. 8, 2006, pp. 184a–184a.
  * [179] J. Van Praet, B. Claeys, A.-S. Coene, K. Floré, and M. Reynders, “Prevention of nosocomial COVID-19: another challenge of the pandemic,” _Infection Control and Hospital Epidemiology_ , vol. 41, no. 11, pp. 1355–1356, Nov. 2020.
  * [180] European centre for disese prevention and control, https://www.ecdc.europa.eu/sites/default/files/documents/covid-19-contact-tracing-public-health-management-third-update.pdf, 2020\.
  * [181] F. Camera, C. Miozzi, F. Amato, C. Occhiuzzi, and G. Marrocco, “Experimental Assessment of Wireless Monitoring of Axilla Temperature by Means of Epidermal Battery-Less RFID Sensors,” _IEEE Sensors Letters_ , vol. 4, no. 11, pp. 1–4, 2020.
  * [182] C. Occhiuzzi, S. Parrella, F. Camera, S. Nappi, and G. Marrocco, “RFID-based Dual-Chip Epidermal Sensing Platform for Human Skin Monitoring,” _IEEE Sensors Letters_ , 2020.
  * [183] C. Miozzi, F. Amato, and G. Marrocco, “Performance and Durability of Thread Antennas as Stretchable Epidermal UHF RFID Tags,” _IEEE Journal of Radio Frequency Identification_ , vol. 4, no. 4, pp. 398–405, 2020.
  * [184] F. Camera _et al._ , “Monitoring of temperature stress during firefighters training by means of RFID epidermal sensors,” in _2019 IEEE International Conference on RFID Technology and Applications (RFID-TA)_ , 2019, pp. 499–504.
  * [185] C. Miozzi, S. Nappi, S. Amendola, C. Occhiuzzi, and G. Marrocco, “A General-Purpose Configurable RFID Epidermal Board With a Two-Way Discrete Impedance Tuning,” _IEEE Antennas and Wireless Propagation Letters_ , vol. 18, no. 4, pp. 684–687, 2019.
  * [186] C. Miozzi, S. Nappi, S. Amendola, and G. Marrocco, “A General-Purpose Small RFID Epidermal Datalogger for Continuous Human Skin Monitoring in Mobility,” in _2018 IEEE/MTT-S International Microwave Symposium - IMS_ , 2018, pp. 371–373.
  * [187] C. Miozzi, S. Amendola, A. Bergamini, and G. Marrocco, “Clinical Trial of Wireless Epidermal Temperature Sensors: preliminary results,” in _Nordic-Baltic Conference on Biomedical Engineering and Medical Physics_ , June 2018, pp. 1041–1044.
  * [188] C. Miozzi, S. Amendola, A. Bergamini, and G. Marrocco, “Reliability of a re-usable wireless Epidermal temperature sensor in real conditions,” in _2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN)_ , 2017, pp. 95–98.
  * [189] T. Adame, A. Bel, A. Carreras, J. Melia-Segui, M. Oliver, and R. Pous, “CUIDATS: An RFID-WSN hybrid monitoring system for smart health care environments,” _Future Generation Computer Systems_ , vol. 78, December 2016\.
  * [190] S. Milici, S. Amendola, A. Bianco, and G. Marrocco, “Epidermal RFID Passive Sensor for Body Temperature Measurements,” in _2014 IEEE RFID Technology and Applications Conference, RFID-TA 2014_ , October 2014, pp. 140–144.
  * [191] X. Chang, J. Dai, Z. Zhang, K. Zhu, and G. Xing, “RF-RVM: Continuous Respiratory Volume Monitoring With COTS RFID Tags,” _IEEE Internet of Things Journal_ , pp. 1–1, 2021.
  * [192] C. Miozzi, G. Stendardo, G. M. Bianco, F. Montecchia, and G. Marrocco, “Dual-chip RFID on-skin tag for bilateral breath monitoring,” in _15th Annual Conference on RFID_ , Apr. 2021.
  * [193] V. Oliveira, L. Duarte, G. Costa, M. Macedo, and T. Silveira, “Automation System for Six-minute Walk Test Using RFID Technology,” in _International Symposium on Networks, Computers and Communications_ , 2020\.
  * [194] Y. Wang and Y. Zheng, “TagBreathe: Monitor Breathing with Commodity RFID Systems,” _IEEE Transactions on Mobile Computing_ , vol. PP, pp. 1–1, February 2019.
  * [195] Z. Hussain, S. Sagar, W. E. Zhang, and Q. Sheng, “A cost-effective and non-invasive system for sleep and vital signs monitoring using passive RFID tags,” in _MobiQuitous ’19: Proceedings of the 16th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services_ , November 2019, pp. 153–161.
  * [196] M. C. Caccami, Y. Mulla, C. Occhiuzzi, C. Natale, and G. Marrocco, “Design and Experimentation of a Batteryless On-skin RFID Graphene-Oxide Sensor for the Monitoring and Discrimination of Breath Anomalies,” _IEEE Sensors Journal_ , vol. PP, pp. 1–1, August 2018.
  * [197] C. Occhiuzzi, C. Caccami, S. Amendola, and G. Marrocco, “Breath-monitoring by means of Epidermal Temperature RFID Sensors,” in _2018 3rd International Conference on Smart and Sustainable Technologies (SpliTech)_ , 2018, pp. 1–4.
  * [198] M. C. Caccami, M. Y. S. Mulla, C. Di Natale, and G. Marrocco, “Graphene oxide-based radiofrequency identification wearable sensor for breath monitoring,” _IET Microwaves, Antennas Propagation_ , vol. 12, no. 4, pp. 467–471, 2018.
  * [199] J. Araujo _et al._ , “Passive RFID tag for respiratory frequency monitoring,” in _3rd International Symposium on Instrumentation Systems, Circuits and Transducers_ , August 2018, pp. 1–5.
  * [200] X. Hui and E. Kan, “Monitoring vital signs over multiplexed radio by near-field coherent sensing,” _Nature Electronics_ , vol. 1, January 2018\.
  * [201] P. Sharma and E. C. Kan, “Sleep Scoring with a UHF RFID Tag by Near Field Coherent Sensing,” in _2018 IEEE/MTT-S International Microwave Symposium - IMS_ , 2018, pp. 1419–1422.
  * [202] M. C. Caccami, C. Miozzi, M. Y. S. Mulla, C. Di Natale, and G. Marrocco, “An epidermal graphene oxide-based RFID sensor for the wireless analysis of human breath,” in _2017 IEEE International Conference on RFID Technology Application (RFID-TA)_ , 2017, pp. 191–195.
  * [203] Y. Hou, Y. Wang, and Y. Zheng, “TagBreathe: Monitor Breathing with Commodity RFID Systems,” in _2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)_ , 2017, pp. 404–413.
  * [204] X. Hu, K. Naya, P. Li, T. Miyazaki, and K. Wang, “Non-invasive sleep monitoring based on RFID,” in _2017 IEEE 19th International Conference on e-Health Networking, Applications and Services (Healthcom)_ , 2017, pp. 1–3.
  * [205] Y. Wang, “Design of the Pulse Oximetry Measurement Circuit and Its Sensing System Based On CMOS,” _IOSR Journal of Electrical and Electronics Engineering_ , vol. 12, pp. 64–70, March 2017.
  * [206] R. Horne, J. Batchelor, P. Taylor, E. Balaban, and A. Casson, “Ultra-Low Power on Skin ECG using RFID Communication,” in _IEEE International Conference on Flexible and Printable Sensors and Systems_ , 2020.
  * [207] C. Wang, L. Xie, W. Wang, Y. Chen, Y. Bu, and S. Lu, “RF-ECG: Heart Rate Variability Assessment Based on COTS RFID Tag Array,” _Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies_ , vol. 2, pp. 1–26, July 2018.
  * [208] S. Agezo, Y. Zhang, Z. Ye, S. Chopra, S. Vora, and T. Kurzweg, “Battery-free RFID heart rate monitoring system,” in _2016 IEEE Wireless Health (WH)_ , 2016, pp. 1–7.
  * [209] S. Vora, K. Dandekar, and T. Kurzweg, “Passive RFID tag based heart rate monitoring from an ECG signal,” in _Annual International Conference of the IEEE Engineering in Medicine and Biology Society_ , vol. 2015, August 2015, pp. 4403–4406.
  * [210] C.-R. Wang, S.-Y. Lee, and W.-C. Lai, “An RFID tag system-on-chip with wireless ECG monitoring for intelligent healthcare systems,” _Conference proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference_ , vol. 2013, pp. 5489–5492, July 2013.
  * [211] J. S. Besnoff, T. Deyle, R. R. Harrison, and M. S. Reynolds, “Battery-free multichannel digital ECG biotelemetry using UHF RFID techniques,” in _2013 IEEE International Conference on RFID (RFID)_ , 2013, pp. 16–22.
  * [212] S. Nappi, V. Mazzaracchio, L. Fiore, F. Arduini, and G. Marrocco, “Flexible pH Sensor for Wireless Monitoring of the Human Skin from the Medimun Distances,” in _2019 IEEE International Conference on Flexible and Printable Sensors and Systems_ , July 2019, pp. 1–3.
  * [213] C. Occhiuzzi, C. Vallese, S. Amendola, S. Manzari, and G. Marrocco, “Multi-channel processing of RFID backscattering for monitoring of overnight living,” in _2014 XXXIth URSI General Assembly and Scientific Symposium (URSI GASS)_ , 2014, pp. 1–4.
  * [214] C. Occhiuzzi, C. Vallese, S. Amendola, S. Manzari, and G. Marrocco, “NIGHT-Care: A Passive RFID System for Remote Monitoring and Control of Overnight Living Environment,” _Procedia Computer Science_ , vol. 32, pp. 190–197, December 2014.
  * [215] C. Occhiuzzi and G. Marrocco, “The RFID Technology for Neurosciences: Feasibility of Limbs’ Monitoring in Sleep Diseases,” _IEEE Transactions on Information Technology in Biomedicine_ , vol. 14, no. 1, pp. 37–43, 2010.
  * [216] USA Today, https://eu.usatoday.com/in-depth/graphics/2020/10/18/improving-indoor-air-quality-prevent-covid-19/3566978001/, 2020\.
  * [217] Centers for Disease Control and Prevention, “Symptoms of Coronavirus,” https://www.cdc.gov/coronavirus/2019-ncov/symptoms-testing/symptoms.html, 2020\.
  * [218] C. Petrilli _et al._ , “Factors associated with hospital admission and critical illness among 5279 people with coronavirus disease 2019 in New York City: Prospective cohort study,” _BMJ_ , vol. 369, p. m1966, May 2020.
  * [219] J. Hasell, “Testing early, testing late: four countries’ approaches to COVID-19 testing compared,” https://ourworldindata.org/covid-testing-us-uk-korea-italy, May 2020.
  * [220] Q. Sun, H. Qiu, M. Huang, and Y. Yang, “Lower mortality of COVID-19 by early recognition and intervention: experience from Jiangsu Province,” _Annals of Intensive Care_ , vol. 10, 2020.
  * [221] M. C. Caccami, C. Miozzi, V. Greco, and G. Marrocco, “Epidermal radio-sensors for wireless detection of physiological parameters and sense augmentation,” in _12th European Conference on Antennas and Propagation (EuCAP 2018)_ , 2018, pp. 1–5.
  * [222] N. Panunzio, G. M. Bianco, C. Occhiuzzi, and G. Marrocco, “RFID Sensors for the Monitoring of Body Temperature and Respiratory Function: a Pandemic Prospect,” in _2021 6th International Conference on Smart and Sustainable Technologies (SpliTech)_ , 2021, pp. 1–5.
  * [223] W.-J. Guan _et al._ , “Clinical Characteristics of Coronavirus Disease 2019 in China,” _New England Journal of Medicine_ , vol. 382, February 2020\.
  * [224] N. Panunzio and G. Marrocco, “SECONDSKIN Project: BioIntegrated Wireless Sensors for the Epidermal Monitoring and Restoring of Sensorial Injuries,” in _2021 IEEE International Conference on RFID Technology and Applications (RFID-TA) (IEEE RFID-TA 2021)_ , October 2021.
  * [225] G. M. Bianco, N. Panunzio, and G. Marrocco, “RFID research against COVID-19 – Sensorized face masks,” in _11th IEEE Int. Conf. RFID Technol. Appl._ , 2021, to be published.
  * [226] C. Massaroni, A. Nicolo, D. Presti, M. Sacchetti, S. Silvestri, and E. Schena, “Contact-Based Methods for Measuring Respiratory Rate,” _Sensors_ , vol. 19, p. 908, February 2019.
  * [227] M. Passavanti, A. Argentieri, D. M. Barbieri, B. Lou, K. Wijayaratna, A. S. Foroutan Mirhosseini, F. Wang, S. Naseri, I. Qamhia, M. TangerÃ¥s, M. Pelliciari, and C.-H. Ho, “The psychological impact of COVID-19 and restrictive measures in the world,” _Journal of Affective Disorders_ , vol. 283, pp. 36–51, 2021.
  * [228] H. Daanen _et al._ , “COVID-19 and thermoregulation-related problems: Practical recommendations,” _Temperature_ , August 2020.
  * [229] Z. S. and, “The microfluidics of the eccrine sweat gland, including biomarker partitioning, transport, and biosensing implications.” _Biomicrofluidics_ , vol. 93, p. 031301, 2015.
  * [230] R. Buyya and S. Srirama, _Fog and edge computing: Principles and paradigms_ , 2019.
  * [231] B. Nordlinger, C. Villani, and D. Rus, _Healthcare and artificial intelligence_ , 2020.
  * [232] C. Tarantini, P. Brouqui, R. Wilson, K. Griffiths, P. Patouraux, and P. Peretti-Watel, “Healthcare workers’ attitudes towards hand-hygiene monitoring technology,” _Journal of Hospital Infection_ , vol. 102, no. 4, pp. 413–418, 2019.
  * [233] J. Lee and K. El-Khatib, “A privacy-enabled architecture for an RFID-based location monitoring system,” in _Proc. 7th ACM Int. Symp. Mobility Manage. Wireless Access_ , Tenerife, Spain, October 2009, pp. 128–131.
  * [234] A. Oztekin, F. Pajouh, D. Delen, and L. Swim, “An RFID network design methodology for asset tracking in healthcare,” _Decision Support Systems_ , vol. 49, no. 1, pp. 100–109, 2010.
  * [235] A. Elewe, K. Hasnan, and A. Nawawi, “Review of RFID optimal tag coverage algorithms,” _ARPN Journal of Engineering and Applied Sciences_ , vol. 11, no. 12, pp. 7706–7711, 2016.
  * [236] N. Panunzio, C. Occhiuzzi, and G. Marrocco, “Propagation modeling inside the International Space Station for the automatic monitoring of Astronauts by means of Epidermal UHF-RFID Sensors,” _IEEE Journal of Radio Frequency Identification_ , pp. 1–1, 2021.
  * [237] R. Van der Togt, P. Bakker, and M. Jaspers, “A framework for performance and data quality assessment of radio frequency identification (RFID) systems in health care settings,” _Journal of Biomedical Informatics_ , vol. 44, no. 2, pp. 372–383, 2011.
  * [238] A. Yee-Loong Chong, M. Liu, J. Luo, and O. Keng-Boon, “Predicting RFID adoption in healthcare supply chain from the perspectives of users,” _International Journal of Production Economics_ , vol. 159, pp. 66–75, 2015\.
  * [239] C. Huang, S. Zhou, J. Xu, Z. Niu, R. Zhang, and S. Cui, _Energy harvesting wireless communications_ , 2018.
  * [240] A. Georgiadis, “Energy harvesting for autonomous wireless sensors and RFID’s,” in _2014 XXXIth URSI General Assembly and Scientific Symposium (URSI GASS)_ , 2014, pp. 1–5.
  * [241] A. Abdelnour, A. Hallet, S. Dkhil, P. Pierron, D. Kaddour, and S. Tedjini, “Energy harvesting based on printed organic photovoltaic cells for RFID applications,” in _2019 IEEE International Conference on RFID Technology and Applications, RFID-TA 2019_ , 2019, pp. 110–112.
  * [242] P. Shieh, N. Azana, T. Santos, A. Martins, N. Dias, and J. Xavier, A.L., “Methodology for choosing piezoelectric devices using piezoelectric energy harvesting to feed massive use of RFID tags,” in _2014 IEEE Brasil RFID_ , 2014, pp. 46–49.
  * [243] B. Khemmanee, K. Tattiwong, and C. Bunlaksananusorn, “Experimental study of a vibration based energy harvesting system for an RFID module,” in _ECTI-CON 2018 - 15th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology_ , 2019, pp. 465–468.
  * [244] I. Jauregi _et al._ , “UHF RFID temperature sensor assisted with body-heat dissipation energy harvesting,” _IEEE Sensors Journal_ , vol. 17, no. 5, pp. 1471–1478, 2017.
  * [245] S. Li, L. D. Xu, and S. Zhao, “5G Internet of Things: A survey,” _Journal of Industrial Information Integration_ , vol. 10, pp. 1–9, 2018\.