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The Internet of Things (IoT) is one of the most promising technologies for the near future. Healthcare and well-being will receive great benefits with the evolution of this technology. This work presents a review of techniques based on IoT for healthcare and ambient assisted living, defined as the Internet of Health Things (IoHT), based on the most recent publications and products available in the market from industry for this segment. Also, this work identifies the technological advances made so far, analyzing the challenges to be overcome and provides an approach of future trends. Though selected works, it is possible notice that further studies are important to improve current techniques and that novel concept and technologies of Internet of Health Things are needed to overcome the identified challenges. The presented results aim to serve as a source of information for healthcare providers, researchers, technology specialists, and the general population to improve the Internet of Health Things.
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Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.
Digital Object Identifier 10.1109/ACCESS.2017.Doi Number
Enabling Technologies for the
Internet of Health Things
Joel J. P. C. Rodrigues1, Senior Member, IEEE, Dante B. R. Segundo2, Heres A. Junqueira2,
Murilo H. Sabino2, Rafael M. Prince2, Jalal Al-Muhtadi3, and Victor Hugo C. de Albuquerque4
1 National Institute of Telecommunications (Inatel), Santa Rita do Sapucaí, MG, Brazil; Instituto de Telecomunicações, Portugal;
ITMO University, Saint Petersburg, Russia; University of Fortaleza (UNIFOR), Fortaleza, CE, Brazil
2National Institute of Telecommunications (Inatel), Santa Rita do Sapucaí, MG, Brazil
3College of Computer and Information Sciences (CCIS), King Saud University, Riyadh 12372, Saudi Arabia
4Graduate Program in Applied Informatics, Laboratory of Bioinformatics, University of Fortaleza (UNIFOR), Fortaleza, CE, Brazil
Corresponding author: Joel J. P. C. Rodrigues (e-mail: joeljr@ieee.org).
This work was supported by the Research Center of College of Computer and Information Sciences, King Saud University; by the National Funding from
the FCT - Fundação para a Ciência e a Tecnologia through the UID/EEA/50008/2013 Project; by the Government of Russian Federation, Grant 074-U01;
by the Brazilian National Council for Research and Development (CNPq) via Grant No. 301928/2014-2, and by FINEP, with resources from Funttel, Grant
No. 01.14.0231.00, under the Centro de Referência em Radiocomunicações - CRR project of the Instituto Nacional de Telecomunicações (Inatel), Brazil.
The authors are grateful for this support.
ABSTRACT The Internet of Things (IoT) is one of the most promising technologies for the near future.
Healthcare and well-being will receive great benefits with the evolution of this technology. This work
presents a review of techniques based on IoT for healthcare and ambient assisted living, defined as the
Internet of Health Things (IoHT), based on the most recent publications and products available in the
market from industry for this segment. Also, this work identifies the technological advances made so far,
analyzing the challenges to be overcome and provides an approach of future trends. Though selected works,
it is possible notice that further studies are important to improve current techniques and that novel concept
and technologies of Internet of Health Things are needed to overcome the identified challenges. The
presented results aim to serve as a source of information for healthcare providers, researchers, technology
specialists, and the general population to improve the Internet of Health Things.
INDEX TERMS Ambient Assisted Living, Internet of Things, Internet of Health Things, Mobile Health,
Remote Healthcare Monitoring, Wearable.
I.
INTRODUCTION
A new network infrastructure is being planned and proposed
considering continuous increment of the number of
connected devices. The academy and industry proposed a
new vision of the Internet with the Internet of Things (IoT),
considering the next generation of the Internet. IoT offers
intelligence to objects by adding them the capacity to collect
and store data from different types of sensors, to perform
actions autonomously based on actuators, coordinate
functions, and share information considering the connectivity
among nodes, for example, [1, 2]. To illustrate the IoT
vision, one can imagine a home heating system being
controlled by a smartphone or a car driving its user to his/her
work autonomously. This scenario describes a simple remote
control and a machine-to-machine (M2M) communications
that also support IoT. These applications can be suited in
industry domains, such as transportation [3], healthcare [4],
smart home [5], industry automation [6], smart grid [7],
among others [8].
Many research companies present perspectives and trends
for the future of IoT as well as research community proposes
a Future Internet of Things [9]. International Data
Corporation (IDC) predicts that IoT will reach about a US$
1.7 trillion market by 2020. Gartner expects 25 billion
connected devices by 2020 while Cisco mentions about 50
billion. And Harvard Business Review expects 28 billion
“things” connected to the Internet [10].
Healthcare industry is among the fastest to embrace IoT-
based solutions. It is being considered one of the key industry
drivers and a special concept for it, considering the IoT
application on e-Health, aka Internet of Health Things
(IoHT). There is also a 2020 projection made for IoHT.
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“MarketsAndMarkets” predicts that IoHT will be worth US$
163.2B, commercial report claims a spending of $117B, and
McKinsey estimates an economic impact of more than US$
170B. All this caused by cost savings, quality of life
improvement for patients with chronic disease, and health
monitoring, which prevents disease complication [10]. It is a
fact that IoHT will create a big economy impact in the world.
Then, given this scenario, this review will present a detailed
study of the literature and relevant proposals from industry
on IoHT. The main contributions of this work are the
following: (i) review of the main available contributions
proposed by the research community and industry, (ii)
discussion about the stage of deployment of IoHT in the
world, and, (iii) identification of open issues to be explored
in further research works. Thus, this review has adopted
aspects related to the scientific fields of Internet of Things,
Biomedical Engineering, and Medicine as well as the real
needs in the daily life of a hospital, clinic, or homecare
environments, for example. Thus, due to the approach
adopted in this review, it can be considered by both a
beginner and experienced researchers, professors, specialists,
hobbyists, as well as health professionals who wishes to
know the latest and recent techniques studied in the IoHT.
The rest of the paper is organized as follows. Section 2
elaborates on Internet of Health Things and reviews the most
relevant proposals available in the literature. Section 3
presents the most relevant IoHT industry proposals. Section 4
presents a discussion considering both academic and industry
research proposals. Section 5 concludes the work and
suggests further research works to explore on the topic.
II. INTERNET OF HEALTH THINGS
Internet of Health Things (IoHT) is basically an IoT-based
solution that includes a network architecture that allows the
connection between a patient and healthcare facilities as, for
example, IoT-based e-Health systems for
electrocardiography [11], heart rate [12],
electroencephalogram [13], diabetes [14], and other different
kinds of monitoring of body (vital) signs based on
biomedical sensors. It includes pulse, oxygen in blood
(SPO2), airflow (breathing), body temperature, glucometer,
galvanic skin response, blood pressure, patient position
(accelerometer), and electromyography [15- 19].
The data input from patients can be collected through
sensors and processed by applications developed for a user
terminal, such as computers, smart phones, smart watches or,
even, a specific embedded device [20]. The user terminal is
connected to a gateway through short coverage
communication protocols, such as, Bluetooth low energy
(BLE), Bluetooth, or 6LoWPAN (IPv6 over Low Power
Wireless Personal Area Networks) over the IEEE 802.15.4
standard [21]. This gateway connects to a (clinical) server or
cloud services for data processing and storage. In the other
hand, patients’ data can be stored in a health information
system using electronic health records and, when the patient
visits a medical doctor, he/she can easily access the clinic
history of the patient. Figure 1 presents an illustration of an
IoHT-based solution.
FIGURE 1. Illustration of an IoHT-based solution architecture.
IoHT can support many medical conditions including care
for pediatric and elderly patients, the supervision of chronic
diseases, and the management of private health and fitness,
among others. For a better study of this extensive topic, this
review classifies the IoHT in four generic categories: i)
remote healthcare monitoring; ii) healthcare solutions based
on smartphones; iii) ambient assisted living; and iv) wearable
devices. Next sub-sections elaborate on each one.
A. REMOTE HEALTHCARE MONITORING
Remote health monitoring technologies are normally adopted
by homecare, clinicians, and hospitals environments to
remotely monitor the vital signs of an individual
communicating in real-time to patients, parents and physician
a possible abnormality, reducing the clinician time,
decreasing hospital costs, and improving quality of care.
Remote healthcare monitoring can be performed by
applications that acquire physiologic data from the patient to
be accessed remotely. Basically, these applications include a
user interface (smartphones, tablets, and computers), a data
collector (biosensors), and Internet connectivity. In this
regard, it can be performed with the integration of IoT,
mobile computing and cloud storage, and a data
communication infrastructure as suggested by Machado et
al., in [22]. This approach aims to capture, transmit, store,
and turn available the visualization of biomedical signals, in
real-time. In the same context, EcoHealth is a middleware
platform developed for IoT that connects patients, healthcare
providers, and devices [23]. It is a Web-based platform that
allows data management and aims to simplify and
standardize IoT applications development, addressing issues
like interoperability between different devices. Similar
approach was developed by Serafim, in [24], an IoT-based
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network to monitor patients in rural and low population
density areas. Healthcare providers may analyze the data
from patients in remote locations and request emergency
assistance if necessary. Another solution, called U-
Healthcare, is based on a mobile gateway for ubiquitous
healthcare systems that collects data, processes it and stores it
in the cloud for remote access [25].
An infrastructure composed by wireless body area
network, personal server using intelligent personal digital
assistant, and medical server tiers for remote healthcare
monitoring system is illustrated in Figure 2.
FIGURE 2. Illustration of an architecture for remote healthcare
monitoring system.
Some solutions are developed with low cost technology,
such as the prototype proposed by Lima et al., in [26].
Receiving pre-processed data, the supervisor system displays
it in a simple and cohesive way. The results show an
improvement of the individuals’ quality of life. An embedded
system able to measure blood with low cost technology is
described in [27]. Hosted by a device, a Webpage displays
the stored data in a useful way. Healthcare providers may
check the information simply by accessing the Internet.
More specific solutions are also being discussed. A remote
body pressure monitoring system is proposed by Matar et al.,
in [28]. Applicable in sleep studies, anesthetic surgical
procedures, and other areas that wish to determinate body
posture while lying down. A non-invasive glucose level
sensing is proposed by Istepanian et al., in [29], sending
patient data to healthcare professionals in real-time. Using a
humidity sensor and a heart rate sensor, the prototype
proposed by Senthilkumar et al., in [30], monitors patients
with Sjögren syndrome. A system that monitors knee flexion
on total knee arthroplasty patients is proposed by Msayib et
al., in [31]. The flexion angles data generated by exercise is
sent for remote analysis, preventing the need of going to the
hospital several times a week. Also, an mHealth platform
capable for patients monitoring that seek cardiac
rehabilitation is discussed in Kitsiou et al., in [32]. Data
collected is sent to the cloud for remote access by healthcare
providers. A body sensors platform is discussed by Khan, in
[33]. Sensors are directly connected to the users’ smartphone
to receive the collected data. The data is processed and stored
in the cloud to allow access and monitoring by the healthcare
providers.
Qi et al., in [34], published a review on advanced Internet
of things enabled personalized healthcare systems (PHS)
considering current works about IoT enabled PHS, and
enabling technologies, major IoT enabled applications and
successful case studies in healthcare, besides main challenges
and future perspectives about IoHT.
Alshurafa et al., in [35], developed a prediction system
(Wanda - cardiovascular disease, CVD) to aid peoples in
reducing detected CVD risk factors, in which the individuals
received instructions by six months of technology support
and reinforcement. A prediction tool can aid clinicians and
scientists in identifying participants who may optimally
benefit from the remote health monitoring (RHM) system,
once that presented results satisfactory (F-score of 0.92),
identifying the behavior (using activity and blood pressure
signals, as well as questionnaires) during the first month of
intervention that help determine outcome success.
Abawajy et al., in [36], presented a patient health
monitoring (PPHM) system integrated cloud computing and
Internet of Things technologies, and applied in the real-time
monitoring of a patient suffering from congestive heart
failure using ECG, showing be a flexible, scalable, and
energy-efficient remote patient health monitoring system
very promising.
Ara et al., in [37], proposed a secure privacy-preserving
data aggregation approach based on bilinear ElGamal
cryptosystem for RHM systems to improve data aggregation
efficiency and data privacy. Security analysis demonstrates
that the proposed method preserves data confidentiality, data
authenticity, and data privacy and also is resists passive
eavesdropping and replay attacks.
Nkenyereye and Jang [38] evaluated the performance of a
server-side JavaScript for healthcare hub server in RHM
System being 40 % faster than Apache Sling, presenting
high-performance, asynchronous, event-driven healthcare
hub server to handle an increasing number of concurrent
connections for RHM environment in remote places without
local medical aid to local medical care.
Mamun et al., in [39], developed a cloud system for
detecting and monitoring Parkinson patients that will enable
healthcare service in low resource setting.
Ganapathy, Grewal and Castleman, in [40], evaluated a
remote self-monitoring of blood pressure to detect raised
blood pressure in pregnancy (50 women), in which 45 of
them agreed that the remote monitoring adopted is easy to
use and 39 women prefer the proposed model of testing at
home.
B. HEALTHCARE SOLUTIONS USING SMARTPHONES
Many IoHT solutions use smartphones. Moreover,
nowadays, healthcare or ambient assisted living solutions
cannot be proposed and designed without considering mobile
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health support, in [41]. In this work, applications that support
diagnosis, clinical communication, providing drug references
or medical education are defined as healthcare solutions
using smartphones. It aims to create a connection between
sensors, smartphones, and the healthcare team. Since security
is one of the main issues on IoHT, a project concerning
healthcare data access is proposed by Murakami et al., in
[42], and aims to test mobile devices used to access data. In
Figure 3 is illustrated the mobile devices and apps for
smartphone healthcare.
FIGURE 3. Smartphone for healthcare system to store and process
electrophysiological data.
A simple and effective solution is proposed by Costa et al.,
in [43], to work as a medical check reminder. It automatically
sends a text message to the patient's phone one day before the
scheduled appointment with the physician. HealtheBrain is a
more specific application, proposed by Shellington et al., in
[44]. It focuses on allowing the square-stepping exercise to
be done by elderly people in their place of living without
cognitive loss.
Crema et al., in [45], proposed a local biosensor
virtualization based on a simplifying the wearables as, for
example, relying only on simple analog front ends and
communication interfaces, and exploiting the computational
capability of the smartphone, not only for implementing
gateway features but also for processing raw biosignals as
well, increasing the potentiality of mhealth applications. The
virtual sensor was evaluated in electrocardiogram signals
analyzing the hearth and respiratory rates.
Aranki et al., in [46], developed a smartphone-based
system for real-time tele-monitoring of vital signs and
general cardiovascular symptoms thought physical activity in
patients with heart disease, being the main challenge to apply
in diabetes and hypertension and other chronic disease.
Barret and Topol, in [47], investigated the possibilities in
which smartphones and the Internet of medical things can
improve medicine, believing that smartphones has a
direct influence on individual's everyday life, which can
bring great contributions to the healthcare field.
Lorenzi et al., in [48], proposed a m-health wearable
wireless sensing system for monitoring human motion
disorders as, for example, freezing of gait (FoG) in
Parkinsonian, and timely provide rhythmic auditory
stimulations to release the gait block, assisting the individual
during the daily activity. Ren et al., in [49], also proposed a
user verification system leveraging unique gait patterns
derived from acceleration readings (embedded within
smartphones) to detect possible user spoofing in m-
healthcare systems. Silsupadol, Teja and Lugade, in [50],
assessed in 34 healthy adults the performance of a
smartphone-based accelerometer in quantifying
spatiotemporal gait parameters when attached to the body or
in a bag, belt, hand, and pocket. Pepa, Verdini and Spalazzi,
in [51], used a smartphone app to acquire, process, and store
inertial sensor data and rotation matrices about device
position for gait parameters estimation during daily living.
Osmani et al., in [52], carried out a study with patients
diagnosed with bipolar disorder to detect episodes and
identify behavior changes using smartphones.
Alshurafa et al., in [53], developed a novel approach to
improve smartphone battery consumption and examine the
effects of smartphone battery lifetime on compliance for
RHM system. The same authors developed the WANDA
method, which remotely evaluate the risk of cardiovascular
disease using wearable smartphone for cardiac abnormality
recognition.
Seeger, Laerhoven and Buchmann, in [54], validated a
middleware targeted for medical applications on smartphone-
like platforms that relies on an event-based design to enable
flexible coupling with changing sets of wireless sensor units,
while posing only a minor overhead on the resources and
battery capacity of the interconnected device.
Poon and Friesen, in [55], proposed an automatic detection
system of chronic wounds (color and size features) for
healthcare environment using image analysis and processing
and machine learning techniques embedded in a mobile app
(m-health system). Wang et al., in [56], considered the same
approach developing a novel wound image analysis system
implemented solely on the Android smartphone.
Velikova et al., in [57], developed the StripTest reader, a
smartphone interpreter of biochemical tests based on paper-
based strip colour using image processing techniques, using
camera phone for acquisition, processing the images within
the phone and comparing them with gold standard.
Higgins, in [58], carried out a study on benefits of using
medical app evaluating smartphone applications for patients'
health and fitness, discussing limitations of apps and future
trends.
Matarazzo et al., in [59], discussed the confluence of
emerging technologies, which can provide regular
infrastructure data streams, within structural health
monitoring procedures for the immediate goal of system
identification and towards automated maintenance of bridges
from vibration data.
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Firth et al., in [60], published a review of all randomized
clinical trials reporting the effects of psychological
interventions delivered via smartphone on symptoms of
anxiety (sub-clinical or diagnosed anxiety disorders),
observing, among others, a significantly greater reduction in
total anxiety scores.
Chao, Meenan and Ferris, in [61], proposed and evaluated
the used of smartphones applied for skin monitoring and
automatic melanoma detection, showing that an early
intervention, through a pre-diagnosis at home, can
successfully treat this disease.
Brayboy et al., in [62], developed a free smartphone,
named Girl Talk, containing comprehensive sexual health
information, and determine the application’s desirability and
appeal among teenage girls.
C. AMBIENT ASSISTED LIVING
Ambient assisted living (AAL) is an IoT-based service that
supports care of elderly or incapacitated patients. These
solutions wish extend the independent life of the individuals
in their homes by providing more safety, in [63]. Connecting
users to smart objects, such as blood pressure sensors and
motion sensors, it is a common use of this service. AAL not
only provides a safer environment but also increases
autonomy and stimulates the user to have a more active life,
in [64]. In Figure 4 is presented an ambient assisted
living system overall physical architecture.
FIGURE 4. Illustration of an ambient assisted living system.
An IoT based architecture for AAL is proposed by Valera,
Zamora and Skarmeta, in [65]. It is developed to solve
problems, such as mobility and security in medical
environments. M-hub is another IoT software architecture,
which uses cloud for AAL, in [66]. It is a middleware used in
mobile devices that automatically finds and connects smart
objects. In this regard, H3IoT is an architectural framework
for monitoring health of elderly people, in [67]. It is
composed by five IoT based layers that include sensors
interconnectivity, microcontrollers, communication channels,
and several applications.
Regarding patient’s location and mobility, a fall detection
system for elderly patients is shown in several contributions,
such as in Lopes et al., in [68], Horta et al., in [69], and Mano
et al., in [70]. It monitors and detects activities such as
walking, running, sitting or standing up, lying down, and
falling. Artificial intelligence integrated to IoT architecture is
used to categorize each activity, in [72], as well as for
intrusion detection in computer networks, in [73,74]. Similar
approach is described in Mainetti et al., in [71], a real-time
location monitoring system for elderly people.
Keeping in mind that drug compliance in AAL is one of
the most important issues, new devices can be regarding
patient safety to support diabetes therapy management, for
example. This solution controls patient’s personal data and
connects it to the healthcare professional.
Bleda et al., in [75], explained the smart sensory furniture
sensory layer (ambient assisted living system that allows
inferring a potential dangerous action of an elderly person
living alone at home) distributed signal processing technique
in a network of sensing objects massively distributed,
physically coupled, wirelessly networked, and energy
limited.
Liu, Shoji and Shinkuma, in [76], proposed a logical
correlation-based sleep scheduling mechanism (LCSSM) to
implement energy-efficient wireless sensor networks in
ambient-assisted homes (AAH). LCSSM analyzes sensory
data generated by different human behaviors to detect the
logical correlations between sensor nodes in an AAH.
Rafferty et al., in [77], introduced a new method to
implementing assistive smart homes based on an intention
recognition mechanism incorporated into an intelligent agent
architecture. The method was performed across three
scenarios: (i) considered a web interface, focusing on testing
the intention recognition mechanism, (ii) and (iii) involved
retrofitting a home with sensors and providing assistance
with physical activities.
Schwiegelshohn et al., in [78], evaluated the robots in
assisted living environments with different solutions for
combining robotics and home automation/smart home for use
in ambient assisted living.
Zdravevski et al., in [79], proposed a generic feature
engineering approach for selecting robust features from a
variety of sensors, which can be used for generating reliable
classification models, reducing reduce the cost of AAL
systems by facilitating execution of algorithms on devices
with limited resources and by using as few sensors.
Yuan, Tie and Song, in [80], modeled and analyzed a
multi-tier AAL applications, and aims to optimize resource
provisioning while meeting requests' response time
constraint, demonstrating achieve dynamic resource
provisioning while meeting the performance constraint.
Yao et al., in [81], analyzed several real-world
experiments conducted for AAL-related behaviors with
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various users, showing that the Big BangBig Crunch
interval type-2 fuzzy-logic-based systems (BB-BC IT2FLSs)
outperform the type-1 fuzzy logic system counterparts, as
well as other conventional nonfuzzy techniques, and the
performance improves when the number of subjects
increases.
Erden et al., in [82], published a survey on signal analysis
and processing techniques employed with different types of
sensors, such as pyro-electric infrared, and vibration sensors,
accelerometers, cameras, depth sensors, and microphones,
analyzing the increase of diseases and healthcare costs,
shortage of caregivers, and a rise in the number of
individuals unable to live independently.
Hossain et al., in [83], highlighted the AAL
communication from several message change perspectives
and developed a general tool of alert/response with
dependable RESTful communication throughout the message
trail within a local and cloud-based environment, offering a
lightweight option to address the variability of message
change in AAL.
Parada et al., in [84], considered the RFID intelligent
system to identify user-object interactions using machine
learning techniques to enable an AAL system in a retail store
with accuracy rate of 86 %.
Machado et al., in [85], analyzed a method to allow AAL
systems to identify and predict situations that may endanger
users in their living environment, proposing a
complementary and alternative to acquire comprehension of
a former person's behavioral providing this knowledge when
cognitive impairments will occur, improving to traditional
expert systems applied to AAL.
Zschippig and Kluss, in [86], suggested to extend the
concepts of AAL, active ageing and ageing in place to the
garden. They evaluated the motivations and possible benefits
of gardening, especially in regard to managing health and
well-being dynamically adapting AAL garden.
Wickramasinghe, Torres and Ranasinghe, in [87], used
heuristic and machine learning techniques to recognize ‘long-
lie’ situations, reducing the influence of outliers in the RFID
information from location of a fall on the smart carpet with
F-score of 0.93.
Garcés et al., in [88], provided a survey on quality models
(QM) and quality attributes (QA) that are important for the
AAL field, investigating how QM and QA are defined,
evaluated, and adopted in the studies, making available an
analysis of the maturity of the works selected.
Roy, Abidi and Abidi, in [89], proposed an activity patter
recognition method via possibilistic network-based classifiers
with uncertain observations, achieving with accuracy 79 % of
a proposed activity.
Demir et al., in [90], proposed an integrated system design
that allows collection, recording and transmission through
Internet of Things approach, where information from smart
things around us can be evaluated and transmitted over the
internet, of the data from different sensors placed in a house
of a person having dementia.
Dobbins, Rawassizadeh and Momeni, in [91], collected
information from tri-axial accelerometers and a heart-rate
monitor to distinguish different physical activity using
supervised machine learning algorithms.
D. WEARABLE DEVICES
Wearables are smart devices that can be attached, for
example, to the body, such as watches, shoes, or body
sensors, as showed in Figure 5. Those devices should be able
to connect to physiological transducers to display patient’s
signals, such as body temperature, heart rate, blood pressure,
and others, in [92].
FIGURE 5. Different types of wearable technology.
A common use of wearable devices is the use for
monitoring user’s physical activity, such as the system
proposed by Portocarrero et al., in [93]. Devices that can be
worn are also used to take care of elderly people. In this
context, a system developed for vital signs monitoring on
elderly people in rest homes is described in Rios and Bezerra,
in [94]. Sensors located on the patient clothes collect data
used to monitor their health parameters. Also, a ubiquitous
system for monitoring elderly patients with Alzhheimer’s is
described in Raad et al., in [95]. When needed, the patient
presses a button and sends important data, such as oxygen
saturation, blood pressure, and heart rate to the healthcare
professional for analysis.
More specific wearable devices are also found in the
literature, such as the system proposed by Chen et al., in [96].
It enables real-time data access in the cloud and monitors
blood pressure, temperature, electrocardiogram (ECG), and
oxygen saturation. Regarding another very specific use, UCC
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is a ubiquitous health monitoring system for cardiac
arrhythmias detection, in [97]. Data is captured using
wearable ECG sensors and it is sent to cardiologists for
analysis. More generic systems are also found, in which
sensors capture data to be stored and accessed in the cloud, in
real-time.
Thapliyal, Khalus and Labrado, in [98], published a survey
on Internet of Things and medical devices as alternative
technologies to recognize stress and manage using cloud-
based services, smartphone apps and wearable smart health
devices to aggregate and compute large data sets that track
stress behavior over long periods of time.
Spanò, Pascoli, and Iannaccone, in [99] proposed an
electrocardiogram remote monitoring system dedicated to
long-term residential health monitoring integrated with
Internet of Things.
Liu and Sun, in [100], published a survey on available
attack approaches on intelligent wearables, as well as the
corresponding countermeasures, from the perspectives of
data integrity, authenticity, and privacy, once that the
intelligent wearables are vulnerable to various cyber-attacks,
bringing up unprecedented security challenges in terms of
privacy leakage, financial loss, and even malicious invasion
of other connected IoT components and applications.
Huang et al., in [101] evaluated the wearable devices
embedded into individual to realize their displacement vector
in mobile environments, thus estimating their own locations
with main aim of walking prediction mechanism to increase
localisation accuracy, demonstrating that the used approach
achieves lower localisation error in various moving speeds.
Guía et al., in [102], investigated the benefits of using
wearable and Internet-of-Things technologies in streamlining
the creation of such realistic task-based language learning
scenarios, showing that the use of these approaches will
prove beneficial by freeing the instructors of having to keep
records of the tasks performed by each student during the
class session.
Cirani and Picone, in [103], analyzed the characteristics of
wearable applications for IoT scenarios and describe the
interaction patterns that should occur between wearable or
mobile devices and smart objects, as well as presented an
implementation of a wearable-based Web of Things
application used to evaluate the described interaction patterns
in a smart environment, deployed within their department's
IoT testbed.
Pasluosta et al., in [104] carried out a detailed review and
discuss the existing wearable devices and the Internet-of-
Things infrastructure used to Parkinson disease, prioritizing
how this technological tool may lead to a shift in paradigm in
terms of diagnostics and treatment.
Arias et al., in [105], analyzed the question of the
possibility and effects of combination between Internet of
Things and wearable devices on traditional design practices
and their implications for security (Google Nest Thermostat)
and privacy (Nike+ Fuelband) fields, approaching how
current industry practices of security as an afterthought or an
add-on affect the resulting device and the potential
consequences to the user's security and privacy.
Lomotey and Sriramoju, in [106], seeked out to streamline
the process by proposing a wearable IoT data streaming
infrastructure that offers traceability of data routes from the
originating source to the health information system. To
overcome the complexities of mapping and matching device
data to users, they used an enhanced Petri Nets, tracking and
the possible detection of medical data compromises.
Sood and Mahajan, in [107], proposed a method to detect
and monitor the outbreak of chikungunya virus based on IoT
and fog concepts in RHM system. To diagnose infected users
and generate emergency alerts from fog layer the Fuzzy-C
means algorithm is considered.
Cui et al., in [108], applied a connective and semantic
similarity clustering algorithm and a hierarchical
combinatorial test model based on finite state machine to
solve the problem of smart wearable systems due numerous
states usually lead to various unanticipated problems. The
FSM model of user manipulations is usually used to model
the system design specification of a smart phone for black-
box testing
III. IoHT INDUSTRY STATUS
The growing of IoHT is experiencing a great debate and
exploration. New start-ups, companies, and multinationals
corporations are taking a step towards that might be a giant
market and empowering products and advances. Table I
presents a compilation of these solutions for better
understanding the IoHT solutions available now. For each
identified solution, it is considered the provided service, the
company and the available product as well as a brief
description of its characteristics.
IV. DISCUSSION
Previous sections presented relevant works proposed by the
industry and the related literature for IoHT. However, it is
necessary to make sure that IoHT is not only a promise but
also a real solution. This section identifies the best works
searched among research publications performed by the
authors. A technological analysis is performed to consider
the industry needs.
The criteria used to select the considered best research
contributions/publications are the following: security
mechanisms, supported communication technologies, and
hardware/OS (operating system) platform. Table II presents
the best qualified works following those criteria. Moreover,
their importance is discussed. For each one, it is mentioned
the reference where it is published, its used mechanisms,
communication technologies, and hardware platform and/or
used operating system. Works that did not mention any
security and privacy mechanism were excluded from this
analysis since it is considered a major issue on e-Health
systems.
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Security and privacy mechanisms are important to be
considered in IoHT applications since they are working with
critical personal data. It is almost impossible that a
contribution may result in a real product if security and
privacy issues are not considered.
TABLE I
MORE RELEVANT KNOWN IOHT SOLUTIONS FROM INDUSTRY
Service
Company
Product
Brief description
Ambient
assisted living
Assisted Living
Technologies
Inc.
BeClose
Remote
Monitoring
System
BeClose Remote Monitoring System offers a
sense of comfort and independence to both the
caregiver and the individual that needs their
support.
Ambient
assisted living
Fade
Fade: Fall
Detector
(App)
Fade is an application for Android mobile
devices capable of detecting and sending an
alarm message when a person suffers a fall.
Healthcare
Solutions
Using
Smartphones
Mcare
Mcare (App)
Mcare is a bracelet that informs parents
whenever the child moves away.
Healthcare
Solutions
Using
Smartphones
Safe Heart
iOximeter
(App)
An oximeter for smartphones.
Healthcare
Solutions
Using
Smartphones
Medisafe
Medisafe
(App)
Medisafe is a product that acts as a medication
reminder.
Healthcare
Solutions
Using
Smartphones
OnTrack
OnTrack
Diabetes
(App)
OnTrack allows quickly and easily keep
tracking of everything needed to manage
diabetes.
Remote
Healthcare
Monitoring
EarlySense
EarlySense
All-in-One
Continuously monitors heart and respiratory
rate, fall prevention, early detection of patient
deterioration and pressure ulcer prevention.
Remote
Healthcare
Monitoring
NovaSom
AccuSom
Simple device used to monitor people
sleeping.
Remote
Healthcare
Monitoring
Hi
Technologies
Tele-ECG
System of records of electrocardiogram.
Remote
Healthcare
Monitoring
Proteus Digital
Health Inc.
Proteus
Discover
Proteus Digital Health consists of ingestible
sensors, a small portable sensor patch, an
application on a mobile device and a provider.
In this case, the pill dissolves in the stomach
and produces a small signal that is picked up
by a sensor used in the body, which again
relays the data (patient health information) to
a smartphone application.
Wearable
MC10
BioStampR
C
Body sensor so flexible and soft used to
collect data from the patient to help in
researches.
Wearable
Apple
Apple
Watch
The Apple Watch Series is a wearable that
allows the developers community to build an
infinite number of new applications for
Healthcare.
Wearable
Bittium
Enterprise
“Enterprise” provides customized, secure IoT
solutions and engineering services for
industrial, healthcare, and wearable sports
device manufacturers.
Wearable
Qardio
QardioCore
Device capable of tracking your complete
heart health on your smartphone and share
data with your doctor.
Wearable
Owlet
Smart Sock
2
Smart Sock 2 uses pulse oximetry to track
your infant’s heart rate and oxygen levels
while they sleep.
Wearable
Monica
Healthcare
Monica
AN24
Solution for home clinical management and
remote monitoring.
IoHT devices are associated both to near field and
worldwide communications systems through an extensive
plethora of MAC layer network technologies, such as Zigbee,
Z-Wave, LoRa, SigFox, Ingenu, Bluetooth, Bluetooth Low
Energy, WiFi, GSM, WiMax, and 3G/4G described in [109,
110]. Wireless channel attributes of these systems make
conventional wired security schemes less suitable.
Accordingly, it is hard to locate a complete security
convention that can treat both wired and wireless channel
qualities similarly, in [20].
An important aspect to consider on IoT is the scalability of
the solution since the quantity of IoHT devices will expand
continuously. More devices are getting associated with the
worldwide data arrange. In this manner, planning a
profoundly adaptable security plot without bargaining
security prerequisites turns it a testing assignment. Another
important aspect to be evaluated in the considered related
literature is the hardware platform. As shown in Table II, the
research works used hardware prototyping platforms, such as
Arduino and Raspberry Pi, indicating that the selected works
are in early stage of development.
TABLE II
TECHNOLOGY USED IN THE SELECTED RESEARCH PUBLICATIONS
Reference
Security
Mechanisms
Hardware Platform
and/or OS
Al-Taee et al. [121]
Cryptographic
Algorithm
Java
Gomes et al. [66]
SDDL
Android
Jara et al. [122]
Symmetric-key
encryption AES-
CBC
SkyeModule M2 and
Jennic JN5139
Machado et al. [22]
ECDH, SSL
Android and Arduino
Maia et al. [23]
JAAS
Arduino
Mainetti et al. [71]
Enterprise Service
Bus (ESB)
Raspberry Pi
Mano et al. [70]
SSL
Android, Raspberry Pi
Matar et al. [28]
SSL
Computer
Murakami et al. [42]
SSL
Computer
Ray [67]
BT SSP
MicaZ and Libelium
A. INDUSTRY STATUS DISCUSSION
An important goal of this survey is the analyzes of the IoHT
real status: who is using it? is it necessity? where is it used?
is it good in terms of usability? and, finally, what is the best
solution in each category? This sub-section discusses these
topics and elects the best solution on each category based on
those criteria.
The products presented for remote healthcare monitoring
are used by patients who need to be continuously monitored,
but not necessarily inside a hospital. They can be at home
using a device for monitoring their condition and send the
data to be analyzed by a health professional remotely. Being
able to be at somewhere else other than the hospital increases
patient’s comfort, reduce costs and hospital infection. As
proposed in Pourhomayoun et al., in [111], remote healthcare
monitoring frameworks may be an effective way to reduce
hospital readmission rates. Among the solutions selected by
the authors for remote healthcare monitoring, EarlySense is
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the one that offers the most complete solution, in [112]. It
monitors heart and respiratory rate, has a fall prevention
system, detects early patient deterioration, and prevents
pressure ulcer. The aspects taken into consideration were
security and how much the solutions explored the
possibilities within IoHT. Additionally, EarlySense publishes
their studies on the company’s Website; great results are
shown. However, the solution is not available worldwide and
only has offices in USA and Israel.
Fall is one of the main causes of fatal injury in elderly
people, turning these patients lives more dependent of care,
in [113]. The Fade App aims to solve the elderly falls
problem, in [114]. For being simple, easy to use, and
addressing such an important issue in ambient assisted living
environments, Fade is very effective in this topic. In 2010, in
USA, a study proved that 21.649 deaths were caused by fall
and 5.402 of them are associated with people with more than
65 years old, in [113]. The increasing number of elderly
population gives importance to the existence of Apps, such
as Fade and many other solutions for AAL environments.
According to the Global Health Observatory, in [115],
healthy life expectancy at birth was 63.1 years in 2015; more
than available mobile Apps it is necessary to give assistance
to this growing population. There are some devices
specifically built for the AAL market. BeClose monitoring
system is an example of a solution that combines several
sensors with artificial intelligence approaches providing more
freedom to elderly people, in [116].
When it comes to wearable devices, many possibilities
emerge. In USA, 15% of the healthcare consumers use
wearable devices, such as, smart watches and fitness devices.
It is predicted that 110 million of wearable devices will be
sold by 2018, in [117]. The goal is always to make the user
as much comfortable as possible to provide quality
healthcare. Therefore, the main aspects taken into
consideration to analyze the previously presented wearable
solutions were patient’s comfort, data security, and usability.
Although MC10 offers a state-of-the-art body sensor capable
of gathering complex physiological data, in [118], Bittium’s
smart watch takes the leading position in the context of this
paper, offering a solution that covers more users, in [119].
The smart watch is capable to measure vital signs,
monitoring patient location, detecting body positioning and
medicine dosage, and timing management. As security is
necessary in all IoHT solutions, Bittium also offers a solution
that ensures the secure data transfer between sensor devices
and cloud services, being a company specialized in the
development of reliable, secure communications, and
connectivity solutions. Although not elected as the best
solution in this work, it is also important to mention the
Apple Watch as a wearable development platform, in [120].
This device allows a great number of new Apps for
healthcare. Through it, it is possible to easily obtain the body
mass index (BMI), body surface area, estimated glomerular
filtration rate (eGFR), and several scores used in
cardiovascular diseases. An important data is that, only in
2016, 6 million devices were sold.
Healthcare solutions using smartphones has become
increasingly common these days. These solutions range from
a simple application to remind the patient to take a medicine
or an oximeter. According to Saúde Business, in [123], it is
estimated that there are more than a hundred million mHealth
Apps around the world. The number of people with diabetes
has risen from 108 million (in 1980) to 422 million in 2014.
According to the World Health Organization, this scenario is
likely to increase considerably. It requires some care to avoid
unpleasant aspects caused by diabetes. One very good
solution related to diabetes is On Track Diabetes, in [124].
OnTrack Diabetes is an App for people with type-2 diabetes.
It helps users better manage their conditions by performing
blood glucose, blood pressure, exercise, food, medication,
pulse and weight checks.
V. CONCLUSION AND FUTURE WORKS
Internet of Things is bringing innovations to many
segmentations of the industry. One of the fastest industries to
embrace this opportunity is healthcare, turning available a
new market based on IoHT. That fact led the authors to
develop a comprehensive survey to analyze the state of the
art on the topic. To accomplish this goal, the most recent
IoHT publications and products were identified, described,
and analyzed. It is possible to conclude that there are many
services and applications for IoHT, these solutions attend the
society needs, but are growing isolated. The discussion on
this paper may help developers and entrepreneurs to build
solutions that embrace all the society. Additionally, this
paper can be considered as a source of information for
healthcare providers, specialists, and the general population
interested in IoHT.
Nevertheless, this review does not provide a deep
understanding about some fundamental topics, including
topologies, architectures, and platforms for IoHT; security
requirements, challenges, and proposed models. There are
other technologies which are not explored in this review and
could be explored further, such as big data, augmented
reality, and cognitive systems. Finally, policies and
regulations are very important in the healthcare sector and
should be considered in future researches.
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2169-3536 (c) 2017 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See
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JOEL J. P. C. RODRIGUES [S’01, M’06,
SM’06] is a professor and senior researcher at
the National Institute of Telecommunications
(Inatel), Brazil and senior researcher at the
Instituto de Telecomunicações, Portugal. He has
been professor at the University of Beira
Interior (UBI), Portugal and visiting professor at
the University of Fortaleza (UNIFOR), Brazil.
Prof. Rodrigues is the leader of the Internet of
Things research group (CNPq), Member of the
IEEE ComSoc Board of Governors as Director
for Conference Development, IEEE ComSoc Distinguished Lecturer, the
President of the scientific council at ParkUrbis Covilhã Science and
Technology Park, the Past-Chair of the IEEE ComSoc Technical
Committee on eHealth, the Past-chair of the IEEE ComSoc Technical
Committee on Communications Software, Steering Committee member of
the IEEE Life Sciences Technical Community and Publications co-Chair,
and Member Representative of the IEEE Communications Society on the
IEEE Biometrics Council. He is the editor-in-chief of Three International
Journals and editorial board member of several high-reputed journals. He
has been general chair and TPC Chair of many international conferences,
including IEEE ICC, GLOBECOM, and HEALTHCOM. He is a member
of many international TPCs and participated in several international
conferences organization. He has authored or coauthored over 550 papers
in refereed international journals and conferences, 3 books, and 2 patents.
He had been awarded several Outstanding Leadership and Outstanding
Service Awards by IEEE Communications Society and several best papers
awards. Prof. Rodrigues is a licensed professional engineer (as senior
member), member of the Internet Society, and a senior member ACM and
IEEE.
DANTE BORGES DE REZENDE
SEGUNDO has a 5-year bachelor degree in
Biomedical Engineering from the National
Institute of Telecommunications (Inatel),
Brazil, 2017. He has experience as a Clinical
Engineering Intern at Antônio Moreira da
Costa Hospital. His fields of interest include
patient data security and availability, remote
healthcare, as well as ambient assisted living
and IoT for healthcare.
HERES ARANTES JUNQUEIRA has a 5-
year bachelor degree in Biomedical
Engineering from the National Institute of
Telecommunications (Inatel), Brazil, 2017.
He received scholarship to study abroad
sponsored by CAPES and CNPq at Temple
University and Illinois Institute of
Technology in 2014-2015. He was Assistant
at Inatel and worked in the projected
“Wheelchair Controlled by Voice” sponsored
by FAPEMIG. He has experience in Embedded Systems Programing,
Embedded Linux, Front-End Programing, Artificial Intelligence,
Biosignals Processing. He works at Ciena Communications, Brazil.
MURILO HENRIQUE SABINO has a 5-
year bachelor degree in Biomedical
Engineering from the National Institute of
Telecommunications (Inatel), Brazil, 2017.
He worked as a trainee at NCC
Certificações do Brasil Ltda, Bureau
Veritas group for one year. After, he got
the position of Certification Engineer and
Leader Auditor (ISO 9001/13485) in the
same company. His fields of interest
include new IEC standards, ISO 13485, Inmetro Ordinance No. 54, as well
as ambient assisted living and IoT for helthcare.
RAFAEL MACIEL PRINCE has a 5-year
bachelor degree in Biomedical Engineering
from the National Institute of
Telecommunications (Inatel), Brazil, 2017. He
has experience as an electronics technician with
an emphasis on medical equipment at Audiosonic
Equipamentos company. He is an expert of the
IEC 60601 standards series for medical products
development and safety. Quality Lead Auditor of
standard ISO 9001 with experience in quality
management system implementation integrated
with the standard ISO/IEC 13485 for medical
devices. He works at NCC Certificações do Brasil, a Bureau Veritas
Company, as a medical product certification engineer. His fields of interest
include quality management system, new iec standards, as well as remote
healthcare monitoring and IoT for healthcare.
JALAL F. AL-MUHTADI, PhD, is the
Director of the Center of Excellence in
Information Assurance (CoEIA) at King Saud
University. He is also an Assistant Professor at
the department of Computer Science at King
Saud University. Areas of expertise include
cybersecurity, information assurance, privacy,
and Internet of Things. He received his PhD and
MS degrees in Computer Science from the
University of Illinois at Urbana-Champaign,
USA. He has over 50 scientific publications in the areas of cybersecurity
and the Internet of Things.
VICTOR HUGO C. DE ALBUQUERQUE
[M17] has a PhD in Mechanical Engineering with
emphasis on Materials from the Federal
University of Paraíba (UFPB, 2010), an MSc in
Teleinformatics Engineering from the Federal
University of Ceará (UFC, 2007), and he
graduated in Mechatronics Technology at the
Federal Center of Technological Education of
Ceará (CEFETCE, 2006). He is currently
Assistant VI Professor of the Graduate Program in
Applied Informatics, and coordinator of the
Laboratory of Bioinformatics at the University of Fortaleza (UNIFOR). He
has experience in Computer Systems, mainly in the research fields of:
Applied Computing, Intelligent Systems, Visualization and Interaction,
with specific interest in Pattern Recognition, Artificial Intelligence, Image
Processing and Analysis, as well as Automation with respect to biological
signal/image processing, image segmentation, biomedical circuits and
human/brain-machine interaction, including Augmented and Virtual
Reality Simulation Modeling for animals and humans. Prof. Victor is the
leader of Computational Methods in Bioinformatics Research Group. He is
editorial board member of the IEEE Access, Computational Intelligence
and Neuroscience, Journal of Nanomedicine and Nanotechnology
Research, and Journal of Mechatronics Engineering, and he has been Lead
Guest Editor of several high-reputed journals, and TPC member of many
international conferences. He has authored or coauthored over 160 papers
in refereed international journals, conferences, 4 book chapters, and 4
patents.
... The variety of available transducing mechanisms provides wearable biosensors worn on the head, neck, torso, legs, feet, arms, hands, and fingers. Figure 2A shows the body locations for which the multiple wearable form factors have been reported [33][34][35]. The wearable market and reported research results include a broad spectrum of device designs from smart helmets to skin patches (Figure 2A), for which user adoption and accuracy are the critical factors to present a sustainable and user-friendly technology. ...
... Following the conventional classification, from now on, we will classify the biosensors depending on their transducers. [33][34][35]. The variety of the transducing mechanisms and the miniaturization of electronics provide wearables that can be worn on the head, neck, torso, legs, feet, arms, and hands. ...
... accessed on 27 April 2022, (B) Adapted from the data shown in ref. [36]. [33][34][35]. The variety of the transducing mechanisms and the miniaturization of electronics provide wearables that can be worn on the head, neck, torso, legs, feet, arms, and hands. ...
Article
Full-text available
The development of new biosensor technologies and their active use as wearable devices have offered mobility and flexibility to conventional western medicine and personal fitness tracking. In the development of biosensors, transducers stand out as the main elements converting the signals sourced from a biological event into a detectable output. Combined with the suitable bio-receptors and the miniaturization of readout electronics, the functionality and design of the transducers play a key role in the construction of wearable devices for personal health control. Ever-growing research and industrial interest in new transducer technologies for point-of-care (POC) and wearable bio-detection have gained tremendous acceleration by the pandemic-induced digital health transformation. In this article, we provide a comprehensive review of transducers for biosensors and their wearable applications that empower users for the active tracking of biomarkers and personal health parameters.
... Healthcare services can now be provided outside the hospital facility with the employment of IoT technology. Remote health monitoring, telemedicine, the ambient-assisted living (ALL) of elderly or disabled people and supervision of chronic diseases are some crucial applications that can benefit healthcare [3,19,20]. Specifically, they can improve the effectiveness and accessibility of health services and help alleviate the pressure on hospital resources. Ref. [20] analyzed recent proposals for Internet of Health Things, ambient assisted living and remote healthcare monitoring systems. ...
... Specifically, they can improve the effectiveness and accessibility of health services and help alleviate the pressure on hospital resources. Ref. [20] analyzed recent proposals for Internet of Health Things, ambient assisted living and remote healthcare monitoring systems. Furthermore, it provided illustrations of relative architectures. ...
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Health 4.0 is a new promising addition to the healthcare industry that innovatively includes the Internet of Things (IoT) and its heterogeneous devices and sensors. The result is the creation of numerous smart health applications that can be more effective, reliable, scalable and cost-efficient while facilitating people with their everyday life and health conditions. Nevertheless, without proper guidance, the employment of IoT-based health systems can be complicated, especially with regard to security challenges such susceptible application displays. An appropriate comprehension of the structure and the security demands of IoT-based multi-sensor systems and healthcare infrastructures must first be achieved. Furthermore, new architectures that provide lightweight, easily implementable and efficient approaches must be introduced. In this paper, an overview of IoT integration within the healthcare domain as well as a methodical analysis of efficient smart health frameworks, which mainly employ multiple resource and energy-constrained devices and sensors, will be presented. An additional concern of this paper will be the security requirements of these key IoT components and especially of their wireless communications. As a solution, a lightweight-based security scheme, which utilizes the lightweight cryptographic primitive LEAIoT, will be introduced. The proposed hardware-based design displays exceptional results compared to the original CPU-based implementation, with a 99.9% increase in key generation speed and 96.2% increase in encryption/decryption speed. Finally, because of its lightweight and flexible implementation and high-speed keys’ setup, it can compete with other common hardware-based cryptography architectures, where it achieves lower hardware utilization up to 87.9% with the lowest frequency and average throughput.
... For this reason, this concept of an ecosystem of interconnected devices has had, in recent years, a significant impact on the consolidation of, e.g., telemedicine and electronic health services [3], industry 4.0 [4] or smart spaces such as Smart Cities, Smart Homes, etc. [5,6]. In addition, it has facilitated the creation of new market contexts such as toys and child development (the Internet of Toys) [7], the automobile industry (the Internet of Vehicles) [8], or even health (the Internet of Health Things) [9]. ...
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Under the Internet of Things paradigm, the emergence and use of a wide variety of connected devices and personalized telematics services have proliferated recently. As a result, along with the penetration of these devices in our daily lives, the users’ security and privacy have been compromised due to some weaknesses in connected devices and underlying applications. This article focuses on analyzing the security and privacy of such devices to promote safe Internet use, especially by young people. First, the connected devices most used by the target group are classified, and an exhaustive analysis of the vulnerabilities that concern the user is performed. As a result, a set of differentiated security and privacy issues existing in the devices is identified. The study reveals that many of these vulnerabilities are related to the fact that device manufacturers often prioritize functionalities and services, leaving security aspects in the background. These companies even exploit the data linked to the use of these devices for various purposes, ignoring users’ privacy rights. This research aims to raise awareness of severe vulnerabilities in devices and to encourage users to use them correctly. Our results help other researchers address these issues with a more global perspective.
... Healthcare 4.0 aims to create a comprehensive, well connected set of smart services for the healthcare industry and support the various needs of quality healthcare services [1][3][10] [11] [12]. Healthcare 4.0 is enabled by several recent and innovative software, hardware, and communication technologies like IoT [13], medical Cyber-Physical Systems (MCPS) [4], health fogs [14][15], health clouds [16], and mobile communications [17]. Big data analytics provide cutting-edge instruments to recognize healthcare trends and relationships, patterns, and insights [18] leading to improved healthcare services, decision making processes, and strategic planning [19]. ...
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