ArticlePDF Available

Abstract and Figures

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.
Content may be subject to copyright.
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
http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2017.2789329, IEEE
Access
!
VOLUME XX, 2018 1
2169-3536 © 2018 IEEE. Translations and content mining are permitted for academic research only.
Personal use is also permitted, but republication/redistribution requires IEEE permission.
See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
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.
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
http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2017.2789329, IEEE
Access
!
VOLUME XX, 2018 9
“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
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
http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2017.2789329, IEEE
Access
!
VOLUME XX, 2018 9
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
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
http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2017.2789329, IEEE
Access
!
VOLUME XX, 2018 9
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.
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
http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2017.2789329, IEEE
Access
!
VOLUME XX, 2018 9
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
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
http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2017.2789329, IEEE
Access
!
VOLUME XX, 2018 9
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
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
http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2017.2789329, IEEE
Access
!
VOLUME XX, 2018 9
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.
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
http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2017.2789329, IEEE
Access
!
VOLUME XX, 2018 9
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
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
http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2017.2789329, IEEE
Access
!
VOLUME XX, 2018 9
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.
REFERENCES
[1] A. Al-Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari and M.
Ayyash, “Internet of things: A survey on enabling technologies,
protocols and applications”. IEEE Communications Surveys &
Tutorials, vol. 17, pp. 2347 2376, 2015.
[2] L. Atzori, A. Iera and G. Morabito, “The internet of things: A
survey”. Computer networks, vol. 54, pp. 27872805, 2010.
[3] S.H. Sutar, R. Koul and R. Suryavanshi, “Integration of Smart Phone
and IoT for development of smart public transportation system”.
International Conference on Internet of Things and Applications,
pp. 73-78, 2016.
[4] S.M.R. Islam, D. Kwak, H. Kabir, M. Hossain and K.S. Kwak, The
Internet of Things for Health Care: A Comprehensive Survey”. IEEE
Access, vol. 3, pp. 678-708, 2015.
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
http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2017.2789329, IEEE
Access
!
VOLUME XX, 2018 9
[5] S. Feng, P. Setoodeh and S. Haykin, Smart Home: Cognitive
Interactive People-Centric Internet of Things”. IEEE
Communications Magazine, vol. 55, pp. 34-39, 2017.
[6] I.E. Etim and J. Lota, Power control in cognitive radios, Internet-
of Things (IoT) for factories and industrial automation”. Annual
Conference of the IEEE Industrial Electronics Society, pp. 4701-
4705, 2016.
[7] X. Li, Q. Huang and D.Wu, “Distributed Large-scale Co-Simulation
for IoT-aided Smart Grid Control”. IEEE Access, 2017. In press.
[8] J.J.P.C. Rodrigues, S. Sendra and I. de la Torre, “e-Health Systems
Theory.” Advances and Technical Applications. Elsevier, 1st Edition,
296 pages, 2016.
[9] A.M. Alberti, G.D. Scarpioni, V.J. Magalhães, A.C.S. Júnior, J.J.P.C.
Rodrigues and R.R. Righi, “Advancing NovaGenesis Architecture
Towards Future Internet of Things”. IEEE Internet of Things
Journal, 2017.
[10] A.M. Elmisery, S. Rho and D. Botvich, “A fog based middleware for
automated compliance with OECD privacy principles in Internet of
healthcare things”. IEEE Access, vol. 4, pp. 8418-8441, 2016.
[11] A.M. Khairuddin, K.N.F.K. Azir and P.E. Kan, “Limitations and
future of electrocardiography devices: A review and the perspective
from the Internet of Things”. International Conference on Research
and Innovation in Information Systems, pp. 1-7, 2017.
[12] C. Li, X. HU and L. Zhang, The IoT-based heart disease monitoring
system for pervasive healthcare service”. Procedia Computer
Science, vol. 112, pp. 2328-2334, 2017.
[13] P.M. Vergara, E. Cal, J.R. Villar, V.M. González and J. Sedano, “An
IoT Platform for Epilepsy Monitoring and Supervising”. Journal of
Sensors, pp. 1-8, 2017.
[14] S. Deshkar, R.A. Thanseeh and V.G. Menon, “A Review on IoT
based m-Health Systems for Diabetes’. International Journal of
Computer Science and Telecommunications, vol. 8, pp. 13-18, 2017.
[15] L. Catarinucci, D. Donno, L. Mainetti, L. Palano, L. Patrono, M.L.
Stefanizzi and L. Tarr, “An IoT-Aware Architecture for Smart
Healthcare Systems”. IEEE Internet of Things Journal, vol. 2, pp.
515-526, 2015
[16] Y. Yin, Y. Zeng, X. Chen and Y. Fan, “The internet of things in
healthcare: an overview”. Journal of Industrial Information
Integration, vol. 1, pp. 313, 2016.
[17] M.W. Woo, J.H. Lee and K.H. Park,
“A reliable IoT system for Personal Healthcare Devices”. Future
Generation Computer Systems, 78, 326-640, 2018.
[18] B. Farahani, F. Firouzi, V. Chang, M. Badaroglu, N. Constant and K.
Mankodiya, “Towards fog-driven IoT eHealth: Promises and
challenges of IoT in medicine and healthcare”. Future Generation
Computer Systems, vol. 78, pp. 659-676, 2018.
[19] F. Firouzi, A.M. Rahmani, K. Mankodiya, M. Badaroglu, G.V.
Merrett, P. Wong and B. Farahani, “Internet-of-Things and big data
for smarter healthcare: from device to architecture, applications and
analytics”. Future Generation Computer Systems, vol. 78, pp. 583-
586, 2018.
[20] I.S.M. Riazul, D. Kwak, K.M.D. Humaun, M. Hossain, and K.
Kwak, “The Internet of Things for healthcare: a comprehensive
survey.” IEEE Access, vol. 5, pp 678-708, 2015.
[21] IEEE STANDARD. 802.15.4 - 2015 - IEEE Standard for Low-Rate
Wireless Networks, 2015.
[22] F.M. Machado, I.M. Koehler, M.S. Ferreira and M.A. Sovierzoski,
“An mHealth Remote Monitor System Approach Applied to MCC
Using ECG Signal in an Android Application. In: Rocha Á., Correia
A., Adeli H., Reis L., Mendonça Teixeira M. (Eds). New Advances in
Information Systems and Technologies: Advances in Intelligent
Systems and Computing, pp. 43-49, 2016.
[23] P. Maia, T. Batista, E. Cavalcante, A. Baffa, F.C. Delicato, P.F. Pires
and A. Zomaya, “A web platform for interconnecting body sensors
and improving health care”. Procedia Computer Science, vol. 40, pp.
135-142, 2014.
[24] E. Serafim, “Estrutura de rede baseada em tecnologia IoT para
atendimento médico em áreas urbanas e rurais”. Master’s thesis,
Programa de Mestrado em Ciência da Computação, Faculdade
Campo Limpo Paulista, 2014.
[25] T.H. Laine, C. Lee and H. Suk, “Mobile gateway for ubiquitous
health care system using ZigBee and Bluetooth”. Eighth
International Conference on Innovative Mobile and Internet Services
in Ubiquitous Computing, 2014.
[26] R. Lima, C. Silva, C. Pereira and E. Aranha, “Wireless Sensor
Networks application on remote Healthcare Monitoring. Revista
Interdisciplinar de Tecnologias e Educação, vol. 2, pp. 1-5, 2016.
[27] E.C.S. Dantas, “Low Cost Embedded Hardware for Blood Pressure
Measurement based on Internet of Things”. Master's thesis,
Programa de Pós-Graduação em Engenharia Elétrica, Inst. Federal de
Educação, Ciência e Tecnologia da Paraíba 2016. [in portuguese]
[28] G. Matar, J. Lina, J. Carrier, A. Riley and G. Kaddoum, “Internet of
things in sleep monitoring: an application for posture recognition
using supervised learning”. 18th International Conference on e-
Health Networking, Applications and Services, 2016.
[29] R.S.H. Istepanian, S. Hu, N.Y. Philip and A. Sungoor, “The
potential of internet of m-health things m-IoT for non-invasive
glucose level sensing”. Annual International Conference of the IEEE
Engineering in Medicine and Biology Society, 2011.
[30] R. Senthilkumar, R.S. Ponmagal and K. Sujatha, “Efficient health
care monitoring and emergency management system using IoT”.
International Journal of Control Theory and Applications, vol. 9, pp.
137-145, 2016.
[31] Y. Msayib, P. Gaydecki, M. Callaghan, N. Dale and S. Ismail, “An
Intelligent Remote Monitoring System for Total Knee Arthroplasty
Patients”. Journal of Medical Systems, vol. 41(9), pp. 90, 2017.
[32] S. Kitsiou, M.M. Thomas, G.E. Marai, N. Maglaveras, G. Kondos,
R. Arena and B. Gerber, “Development of an innovative mHealth
platform for remote physical activity monitoring and health coaching
of cardiac rehabilitation Patients”. IEEE International Conference on
Biomedical & Health Informatics, 2017.
[33] S.F. Khan, “Health care monitoring system in Internet of Things
(loT) by using RFID”. International Conference On Industrial
Technology and Management, 2017.
[34] J. Qi, P. Yang, G. Min, O. Amft and L. Xu, Advanced internet of
things for personalised healthcare systems: A survey”. Pervasive and
Mobile Computing, vol. 41, pp. 132-149, 2017.
[35] N. Alshurafa, C. Sideris, M. Pourhomayoun, H. Kalantarian,
M. Sarrafzadeh and J.A. Eastwood,
Remote Health Monitoring Outcome Success Prediction Using
Baseline and First Month Intervention Data”. IEEE Journal of
Biomedical and Health Informatics, vol. 21, pp. 507-514, 2017
[36] J.H. Abawajy, M. Mohammad and M. Hassan, “Federated Internet of
Things and Cloud Computing Pervasive Patient
Health Monitoring System’. IEEE Communications Magazine, vol.
55, pp. 48-53, 2017.
[37] A. Ara, M. Al-Rodhaan, Y. Tian and A. Al-Dhelaan, “A Secure
Privacy-Preserving Data Aggregation Scheme Based on Bilinear
ElGamal Cryptosystem for Remote Health Monitoring Systems”.
IEEE Access, vol 5, pp. 12601-12617, 2017.
[38] L. Nkenyereye and J.W. Jang, “Performance Evaluation of Server-
side JavaScript for Healthcare Hub Server in Remote Healthcare
Monitoring System”. Procedia Computer Science, vol. 98, pp. 382-
387, 2016.
[39] K.A.A. Mamun, M. Alhussein, K. Sailunaz and M.S. Islam, “Cloud
based framework for Parkinson’s disease diagnosis
and monitoring system for remote healthcare applications”. Future
Generation Computer Systems, vol. 66, pp. 36-47, 2017.
[40] R. Ganapathy, A. Grewal and J.S. Castleman, “Remote monitoring of
blood pressure to reduce the risk of preeclampsia related
complications with an innovative use of mobile technology”.
Pregnancy Hypertension, vol. 6, pp. 263-265, 2016.
[41] B.M.C. Silva, J.J.P.C. Rodrigues, I. de la Torre Díez, M. López-
Coronado, K. Saleem, “Mobile-Health: A Review of Current State in
2015”. Journal of Biomedical Informatics, vol. 56, pp. 265-272,
2015.
[42] A. Murakami, L.O.M.A Kobayashi, U. Tachinardi, M.A. Gutierrez,
S.S. Furuie and F.A. Pires “Acesso a informações médicas através do
uso de sistemas de computação móvel. Congresso Brasileiro de
Informática na Saúde, 2004.
[43] T.M. Costa, P.L. Salomão, A.S. Martha, I.T. Pisa and D. Sigulem,
“The impact of short message service text messages sent as
appointment reminders to patients’ cell phones at outpatient clinics in
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
http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2017.2789329, IEEE
Access
!
VOLUME XX, 2018 9
Sao Paulo, Brazil”. Internacional Journal of Medical Informatics,
vol. 79, pp. 65-70, 2009.
[44] E.M. Shellington, T. Felfeli, R. Shigematsu, D.P. Gill and R.
Petrella, “HealtheBrain: An innovative smartphone application to
improve cognitive function in older adults”. MHealth, vol. 3, 2017.
[45] C. Crema, A. Depari, A. Flammini, E. Sisinni, A. Vezzoli and
P. Bellagente, “Virtual Respiratory Rate Sensors: An Example of
A Smartphone-Based Integrated and Multiparametric mHealth
Gateway”. IEEE Transactions on Instrumentation and Measurement,
vol. 66, pp. 2456-2463, 2017.
[46] D. Aranki; G. Kurillo; P. Yan; D.M. Liebovitz and R Bajcsy, Real-
Time Tele-Monitoring of Patients with Chronic Heart-Failure Using
a Smartphone: Lessons Learned”. IEEE Transactions on Affective
Computing, vol 7, pp. 206-219, 2016.
[47] P.M. Barrett and E.J. Topol, Smartphone Medicine”. IT
Professional, vol. 18, pp. 52-54, 2016
[48] P. Lorenzi, R. Rao, G. Romano, A. Kita and F Irrera, “Mobile
Devices for the Real-Time Detection of Specific Human Motion
Disorders”. IEEE Sensors Journal, vol. 16, pp. 8220-8227, 2016.
[49] Y. Ren, Y Chen, M.C. Chuah and J Yang, User Verification
Leveraging Gait Recognition for Smartphone Enabled
Mobile Healthcare Systems”, IEEE Transactions on Mobile
Computing, vol. 14, pp. 1961-1974, 2015.
[50] P. Silsupadol, K. Teja and V. Lugade, Reliability and validity of
a smartphone-based assessment of gait parameters across walking
speed and smartphone locations: body, bag, belt, hand, and pocket”.
Gait & Posture, vol. 58, pp. 516-522, 2017.
[51] L. Pepa, F. Verdini and L. Spalazzi, Gait parameter and event
estimation using smartphones, “Gait & Posture, vol. 57, pp. 217-223,
2017.
[52] V. Osmani, A. Gruenerbl, G. Bahle, C. Haring, P. Lukowicz and O.
Mayora Smartphones in Mental Health: Detecting Depressive and
Manic Episodes”. IEEE Pervasive Computing, vol. 14, pp. 10-13,
2015.
[53] N. Alshurafa, J.A. Eastwood, S. Nyamathi, J.J. Liu, W. Xu, H.
Ghasemzadeh, M. Pourhomayoun and M. Sarrafzadeh, Improving
Compliance in Remote Healthcare Systems
Through Smartphone Battery Optimization”. IEEE Journal of
Biomedical and Health Informatics, vol. 19, pp. 57-63, 2015.
[54] C. Seeger, K.V. Laerhoven, A. Buchmann, “MyHealthAssistant: An
Event-driven Middleware for Multiple Medical Applications on
a Smartphone-Mediated Body Sensor Network”. "IEEE Journal of
Biomedical and Health Informatics, vol. 19, pp.752-760, 2015.
[55] T.W.K. Poon and M.R. Friesen, “Algorithms for Size and Color
Detection of Smartphone Images of Chronic Wounds
for Healthcare Applications”. IEEE Access, vol. 3, pp. 1799-1808,
2015.
[56] L. Wang, P.C. Pedersen, D.M. Strong, B. Tulu, E. Agu and R.
Ignotz, Smartphone-Based Wound Assessment System for Patients
With Diabetes”. IEEE Transactions on Biomedical Engineering, vol.
62, pp. 477-488, 2015.
[57] M. Velikova, R.L. Smeets, J.T. van Scheltinga, P.J.F. Lucas and
M. Spaanderman, “Smartphone-based analysis of biochemical tests
for health monitoring support at home”. Healthcare Technology
Letters, vol. 1, pp. 92-97, 2014.
[58] J.P. Higgins, Smartphone Applications for Patients' Health and
Fitness”. The American Journal of Medicine, vol. 129, pp. 11-19,
2016.
[59] T. Matarazzo, M. Vazifeh, S. Pakzad, P. Santi and Carlo Ratti,
Smartphone data streams for bridge health monitoring”. Procedia
Engineering, vol. 199, pp. 966-971, 2017.
[60] J. Firth, J. Torous, J. Nicholas, R. Carney, S. Rosenbaum and J.
Sarris, Can smartphone mental health interventions reduce
symptoms of anxiety? A meta-analysis of randomized controlled
trials”. Journal of Affective Disorders, vol. 2017, pp. 15-22, 2017.
[61] E. Chao, C.K. Meenan and L.K. Ferris, Smartphone-Based
Applications for Skin Monitoring and Melanoma Detection”.
Dermatologic Clinics, vol. 35, pp. 551-557, 2017.
[62] L.M. Brayboy, A. Sepolen, T. Mezoian, L. Schultz, S. Benedict, L.
Mills, N. Spencer, C. Wheeler and M.A. Clark “Girl Talk: A
Smartphone Application to Teach Sexual Health Education to
Adolescent Girls”. Journal of Pediatric and Adolescent Gynecology,
vol. 30, pp. 23-28, 2017.
[63] S.E.P. Costa, J.J.P.C. Rodrigues, B.M.C. Silva, J.N. Isento and J.M.
Corchado, “Integration of Wearable Solutions in AAL Environments
with Mobility Support”. Journal of Medical Systems, vol. 39, Article
ID 184, 2015.
[64] N. Garcia and J.J.P.C. Rodrigues, “Ambient Assisted Living”, CRC
Press - Taylor & Francis Group, June, 1st Edition, 777 pages, 2015.
[65] A.J.J. Valera, M.A. Zamora and A.F.G. Skarmeta, “An architecture
based on Internet of Things to support mobility and security in
medical environments”. 7th IEEE Consumer Communications and
Networking Conference, 2010.
[66] B. Gomes, L. Muniz, F. Silva, L.T. Rios and M. Endler, “A
Comprehensive Cloud-based IoT Software Infrastructure for
Ambient Assisted Living, International Conference on Cloud
Computing Technologies and Applications, 2015
[67] P.P. Ray, “Home Health Hub Internet of Things (H3IoT): An
architectural framework for monitoring health of elderly people”.
International Conference on Science, Engineering and Management
Research, 2014.
[68] I.C. Lopes, B. Vaidya and J.J.P.C. Rodrigues, “Towards an
Autonomous Fall Detection and Alerting System on a Mobile and
Pervasive Environment”. Telecommunication Systems, vol. 52, pp.
2299-2310, 2013.
[69] E.T. Horta, I.C. Lopes and J.J.P.C. Rodrigues, “Ubiquitous mHealth
Approach for Biofeedback Monitoring with Falls Detection
Techniques and Falls Prevention Methodologies”, Mobile Health
(mHealth): The Technology Road Map, Springer Series in Bio-
/Neuroinformatics, vol. 5, pp. 43-75, 2015.
[70] L. Mano, M. Funes, T. Volpato and J. Neto, “Explorando tecnologias
de IoT no contexto de Health Smart Home: uma abordagem para
detecção de quedas em pessoas idosas”. Journal on Advances in
Theoretical and Applied Informatics, vol. 2, pp. 46-57, 2016. [in
Portuguese]
[71] L. Mainetti, L. Patrono, A. Secco and I. Sergi, I, “An IoT-aware
AAL System for Elderly People”. International Multidisciplinary
Conference on Computer and Energy Science, 2016.
[72] L.A. Silva, K.A.P. Costa, P.B. Ribeiro, D. Fernandes and J.P. Papa,
“On the Feasibility of Optimum-Path Forest in the Context of
Internet-of-Things-Based Applications”. Recent Patents on Signal
Processing, vol. 5, pp. 52-60, 2016.
[73] C.R. Pereira, R.Y.M. Nakamura, K.A.P. Costa and J.P. Papa,
An Optimum-Path Forest framework for intrusion detection in
computer networks”. Engineering Applications of Artificial
Intelligence, vol. 25, pp. 1226-1234, 2012.
[74] K.A.P. Costa, L.A.M. Pereira, R.Y.M. Nakamura, C.R. Pereira, J.P.
Papa and A.X. Falcão, A nature-inspired approach to speed
up optimum-path forest clustering and its application to intrusion
detection in computer networks”. Information Sciences, vol. 294, pp.
95-108, 2015.
[75] A.L. Bleda, F.J. Fernández-Luque, A. Rosa, J. Zapata and R.
Maestre, Smart Sensory Furniture Based on WSN
for Ambient Assisted Living”. IEEE Sensors Journal, vol. 17, pp.
5626-5636, 2017.
[76] W. Liu, Y. Shoji and R. Shinkuma, Logical Correlation-Based
Sleep Scheduling for WSNs in Ambient-Assisted Homes”. IEEE
Sensors Journal, vol. 17, pp. 3207-3218, 2017.
[77] J. Rafferty, C.D. Nugent, J. Liu and L. Chen, From Activity
Recognition to Intention Recognition for Assisted Living Within
Smart Homes, IEEE Transactions on Human-Machine Systems, vol.
47, pp. 368-379, 2017.
[78] F. Schwiegelshohn, M. Hubner, P. Wehner and D. Gohringer,
Tackling The New Health-Care Paradigm Through Service
Robotics: Unobtrusive, efficient, reliable, and modular solutions
for assisted-living environments”. IEEE Consumer Electronics
Magazine, vol. 6, pp. 34-41, 2017.
[79] E. Zdravevski, P. Lameski, V. Trajkovik, A. Kulakov, I. Chorbev, R.
Goleva, N. Pombo, N. Garcia, Improving Activity Recognition
Accuracy in Ambient-Assisted Living Systems by Automated
Feature Engineering”. IEEE Access, vol. 5, pp. 52625280, 2017.
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
http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2017.2789329, IEEE
Access
!
VOLUME XX, 2018 9
[80] J. Bi, H. Yuan, M. Tie and X. Song, “Heuristic virtual machine
allocation for multi-tier Ambient Assisted Living applications in a
cloud data center”. China Communications, vol. 13, pp. 56-65, 2016.
[81] B. Yao, H. Hagras, D. Alghazzawi and M.J. Alhaddad, A Big
BangBig Crunch Type-2 Fuzzy Logic System for Machine-Vision-
Based Event Detection and Summarization in Real-World Ambient-
Assisted Living”. IEEE Transactions on Fuzzy Systems, vol. 24,
1307-1319, 2016.
[82] F. Erden, S. Velipasalar, A.Z. Alkar and A.E. Cetin, “Sensors in
Assisted Living: A survey of signal and image processing methods”.
IEEE Signal Processing Magazine, vol. 33, pp. 36-44, 2016.
[83] M.A. Hossain, J. Parra, M. Rahman, A. Alamri, S. Ullah and H.T.
Mouftah, From Sensing to Alerting: a Pathway of RESTful
Messaging in Ambient Assisted Living”. IEEE Wireless
Communications, vol. 23, pp. 102-110, 2016.
[84] R. Parada, J. Melià-Seguí, M. Morenza-Cinos, A. Carreras and R.
Pous, Using RFID to Detect Interactions
in Ambient Assisted Living Environments”. IEEE Intelligent
Systems, vol. 30, pp. 16-22, 2015.
[85] A. Machado, V. Maran, I. Augustin, L.K. Wives and J.P.M. Oliveira,
Reactive, proactive, and extensible situation-awareness
in ambient assisted living”. Expert Systems with Applications, vol.
76, pp. 21-35, 2017.
[86] C. Zschippig and T. Kluss, Gardening in Ambient Assisted Living”.
Urban Forestry & Urban Greening, vol. 15, pp. 186-189, 2016.
[87] A. Wickramasinghe, R.L.S. Torres and D.C. Ranasinghe,
Recognition of falls using dense sensing in
an ambient assisted living environment”. Pervasive and Mobile
Computing, vol. 34, pp. 14-24, 2017.
[88] L. Garcés, A. Ampatzoglou, P. Avgeriou and E.Y. Nakagawa,
Quality attributes and quality models
for ambient assisted living software systems: A systematic mapping”.
Information and Software Technology, vol. 82, pp. 121-138, 2017.
[89] P.C. Roy, S.R. Abidi and S.S.R. Abidi, “Possibilistic activity
recognition with uncertain observations to support medication
adherence in an assisted ambient living setting”. Knowledge-Based
Systems, vol. 133, pp. 156-173, 2017.
[90] E. Demir, E. Köseoğlu, R. Sokullu and B. Şeker, Smart Home
Assistant for Ambient Assisted Living of Elderly People with
Dementia”. Procedia Computer Science, vol. 113, pp. 609-614,
2017.
[91] C. Dobbins, R. Rawassizadeh and E. Momeni, “Detecting physical
activity within lifelogs towards preventing obesity and
aiding ambient assisted living”. Neurocomputing, vol. 230, pp. 110-
132, 2017.
[92] G.A. Oliveira, “Um Modelo para Gerenciamento de Históricos de
Contextos Fisiológicos”. Master's thesis, Programa Integrado de
Pós-graduação em Computação Aplicada, Universidade do Vale do
Rio dos Sinos, 2016.
[93] J.M.T Portocarrero, W.L. Souza, M. Demarzo and A.F. Prado,
“Biblioteca Digital Brasileira da Computação”. X Workshop de
Informática Médica, 2010.
[94] T.S. Rios and R.M.S. Bezerra, “WHMS4: An integrated model for
health remote monitoring: A case study in nursing homes for the
elderly.” 10th Iberian Conference on Information Systems and
Technologies, 2015.
[95] M.W. Raad, T. Sheltami and E. Shakshuki, “Ubiquitous Tele-health
System for Elderly Patients with Alzheimer’s”. Procedia Computer
Science, vol. 52, pp. 685-689, 2015.
[96] M. Chen, Y. Ma, Y. Li, D. Wu, Y. Zhang and C. Youn, “Wearable
2.0: Enabling Human-Cloud Integration in Next Generation
Healthcare Systems”. IEEE Communications Magazine, vol. 55, pp.
54-61, 2017.
[97] J. Li, H. Zhou, K.M. Hou and C. De Vaulx, “Ubiquitous health
monitoring and real-time cardiac arrhythmias detection: A case
study”. Bio-Medical Materials and Engineering, vol. 24, pp. 1027-
1033, 2014.
[98] H. Thapliyal, V. Khalus and C. Labrado, Stress Detection and
Management: A Survey of Wearable Smart Health Devices”. IEEE
Consumer Electronics Magazine, vol. 6, pp. 64-69, 2017.
[99] E. Spanò, S.D. Pascoli and G. Iannaccone, Low-Power
Wearable ECG Monitoring System for Multiple-Patient Remote
Monitoring”. IEEE Sensors Journal, vol. 16, pp. 5452-5462, 2016.
[100] J. Liu and W. Sun, Smart Attacks against Intelligent Wearables in
People-Centric Internet of Things”. IEEE Communications
Magazine, vol. 54, pp. 44-49, 2016.
[101] H. Huang, J. Zhou, W. Li, J. Zhang, X. Zhang and G. Hou,
Wearable indoor localisation approach in Internet of Things”. IET
Networks, vol. 5, pp. 122-126, 2016.
[102] E. de la Guía, V.L. Camacho, L. Orozco-Barbosa, V.M.B.
Luján, V.M.R. Penichet and M.L. Pérez,
Introducing IoT and Wearable Technologies into Task-Based
Language Learning for Young Children”. IEEE Transactions on
Learning Technologies, vol. 9, pp. 366-378, 2016.
[103] S. Cirani and M. Picone, Wearable Computing for the Internet of
Things”. IT Professional, vol. 17, pp. 35-41, 2015.
[104] C.F. Pasluosta, H. Gassner, J. Winkler, J. Klucken and B.M.
Eskofier, “An Emerging Era in the Management of Parkinson's
Disease: Wearable Technologies and the Internet of Things”. IEEE
Journal of Biomedical and Health Informatics, vol. 19, pp. 1873-
1881, 2015.
[105] O. Arias, J. Wurm, K. Hoang and Y. Jin, “Privacy and Security in
Internet of Things and Wearable Devices”. IEEE Transactions on
Multi-Scale Computing Systems, vol. 1, pp. 99-109, 2015.
[106] R.K. Lomotey, J. Pry and S. Sriramoju, “Wearable IoT data stream
traceability in a distributed health information system”. Pervasive
and Mobile Computing, vol. 40, pp. 692-707, 2017.
[107] S.K. Sood and I. Mahajan, “Wearable IoT sensor based healthcare
system for identifying and controlling chikungunya virus”.
Computers in Industry, vol. 91, pp. 33-44, 2017.
[108] K. Cui, K. Zhou, T. Qiu, M. Li and L. Yan, “A hierarchical
combinatorial testing method for smart phone software
in wearable IoT systems”. Computers & Electrical Engineering, vol.
61, pp. 250-265, 2017.
[109] Z. Sheng, S. Yang, Y. Yu, A.Vasilakos, J. Mccann and K. Leung “A
survey on the IETF protocol suite for the internet of things:
Standards, challenges, and opportunities”. IEEE Wireless
Communications, vol. 20, pp. 91-98, 2013.
[110] J.C. Silva, J.J.P.C. Rodrigues, A.M. Alberti, P. Solic and L.L.A.
Aquino, “LoRaWAN - A Low Power WAN Protocol for Internet of
Things: a Review and Opportunities”. International
Multidisciplinary Conference on Computer and Energy Science, pp.
143-148, 2017.
[111] M. Pourhomayoun, N. Alshurafa, F. Dabiri, E. Ardestani, A.
Samiee, H. Ghasemzadeh and M. Sarrafzadeh, “Why do we need a
remote health monitoring system? A study on predictive analytics for
heart failure patients”. 11th International Conference on Body Area
Networks, 2017.
[112] EarlySense “Early Sense One”. Retrieved from
http://www.earlysense.com/earlysense-one/, 2017.
[113] J.A. Stevens and R.A. Rudd, “Circumstances and contributing
causes of fall deaths among persons aged 65 and older”. Journal of
the American Geriatrics Society, vol. 62, pp. 470-475, 2014.
[114] Fade, “Fade: Fall Detector”. Retrieved from: http://fade.iter.es/,
2017.
[115] World Health Organization, “Global Health Observatory”. 2017.
[116] Assisted Living Technologies, “BeClose Remote Monitoring
System”. Retrieved from:
http://www.assistedlivingtechnologies.com/remote-monitoring-
elderly/11-beclose.html, 2017.
[117] L. Piwek, D.A. Ellis, S. Andrew and A. Joinson, “The rise of
consumer health wearables: promises and barriers”. PloS Medicine,
vol. 13, pp. e1001953, 2016.
[118] MC10, “BioStampMD”. Retrieved from
https://www.mc10inc.com/our-products#BioStampMD, 2017.
[119] Bittium, “IoT and Wearable Solutions”. Retrieved from
https://www.bittium.com/products__services/iot_and_wearable_solut
ions/healthcare_market#concept_examples, 2017.
[120] Apple, “Apple Watch Series 2”. Retrieved from:
https://www.apple.com/br/watch/, 2017.
[121] M.A. Al-Taee, W. Al-Nuaimy, A. Al-Ataby, Z.J. Muhsin and S.N.
Abood, “Mobile health platform for Diabetes management based on
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
http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2017.2789329, IEEE
Access
!
VOLUME XX, 2018 9
the Internet-of-things”. IEEE Jordan Conference on Applied
Electrical Engineering and Computing Technologies, 2015.
[122] A.J. Jara, M.A. Zamora and A.F.G. Skarmeta, “An Internet of
Things-based personal device for Diabetes therapy management in
Ambient Assisted Living”. Personal and Ubiquituos Computing, vol.
15, pp. 431-440, 2011.
[123] Saúde Business, “O que é Mobile Health”.
http://saudebusiness.com/noticias/o-que-e-mobile-health-
infografico/, 2017.
[124] OnTrack, “OnTrack Diabetes App”. Retrieved from
https://www.ontrack.org.au/diabetes/, 2017.
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.
... DICOM, the widely adopted standard for medical pictures, ensures secure processing and retrieval across healthcare systems. The integration of IoT in healthcare has expanded remote monitoring and device integration capabilities, necessitating robust network security measures [14]. Implementing integrity encryption is paramount to safeguard patient information across facilities to ensure reliable communication and overcome privacy concerns. ...
... The histogram analysis solely yields visual graphical perspectives. The chi-square test ( (2) is an essential test for examining the consistency of the encrypted pixel distribution, which is mathematically calculated by Eq. 2, [14]. ...
Article
Full-text available
One of the largest and fastest-growing industries globally is e-healthcare. Digital medical imaging constantly evolves, advancing from standard computed tomography to powerful multi-dimensional imaging. This medical imaging forms patients’ real-time electronic health records (EHR), making it a prime target for hackers. To counter cipher-attacks, it is necessary to secure medical imaging. This research thoroughly analyses the properties of DNA chaos-based digital medical image encryption and reviews various algorithms. Researchers have performed several statistical and differential metrics, conducting simulation experiments to determine which methodology provides the best security outcome. The paper concludes by analysing significant advances in medical image encryption and emphasising future challenges. This paper discusses the importance of securing medical images, the potential breaches, and the security needs, providing a comprehensive review of DNA-based chaos cryptography techniques.
... For reusability or updating, it can access services remotely from any place, and at any time, installation costs are reduced, and less technical knowledge is required to interact with the devices. Due to all these benefits, IoT has rapidly grown in almost all application areas, including healthcare [11,12], military, agriculture [13,14], industry [15], transportation [16,17], mining, manufacturing, smart cities [18,19], smart grids [20,21], remote environmental monitoring [22], search and rescue applications [23], fire detection [24], underwater [25], and satellite communications [26]. The advances in various technologies make IoT a breakthrough technology for a new level of novel applications ranging from improved security to higher energy efficiency and personalized experiences. ...
Article
Full-text available
The Internet of Things (IoT) has orchestrated various domains in numerous applications, contributing significantlyto the growth of the smart world, even in regions with low literacy rates, boosting socio-economic development.This study provides valuable insights into optimizing wireless communication, paving the way for a more connectedand productive future in the mining industry. The IoT revolution is advancing across industries, but harshgeometric environments, including open-pit mines, pose unique challenges for reliable communication. The adventof IoT in the mining industry has significantly improved communication for critical operations through theuse of Radio Frequency (RF) protocols such as Bluetooth, Wi-Fi, GSM/GPRS, Narrow Band (NB)-IoT, SigFox,ZigBee, and Long Range Wireless Area Network (LoRaWAN). This study addresses the optimization of networkimplementations by comparing two leading free-spreading IoT-based RF protocols such as ZigBee and LoRaWAN.Intensive field tests are conducted in various opencast mines to investigate coverage potential and signal attenuation.ZigBee is tested in the Tadicherla open-cast coal mine in India. Similarly, LoRaWAN field tests are conducted atone of the associated cement companies (ACC) in the limestone mine in Bargarh, India, covering both Indoor-to-Outdoor (I2O) and Outdoor-to-Outdoor (O2O) environments. A robust framework of path-loss models, referredto as Free space, Egli, Okumura-Hata, Cost231-Hata and Ericsson models, combined with key performance metrics,is employed to evaluate the patterns of signal attenuation. Extensive field testing and careful data analysis revealedthat the Egli model is the most consistent path-loss model for the ZigBee protocol in an I2O environment, witha coefficient of determination (R2) of 0.907, balanced error metrics such as Normalized Root Mean Square Error(NRMSE) of 0.030, Mean Square Error (MSE) of 4.950, Mean Absolute Percentage Error (MAPE) of 0.249 andScatter Index (SI) of 2.723. In the O2O scenario, the Ericsson model showed superior performance, with the highestR2 value of 0.959, supported by strong correlation metrics: NRMSE of 0.026, MSE of 8.685, MAPE of 0.685, MeanAbsolute Deviation (MAD) of 20.839 and SI of 2.194. For the LoRaWAN protocol, the Cost-231 model achievedthe highest R2 value of 0.921 in the I2O scenario, complemented by the lowest metrics: NRMSE of 0.018, MSE of1.324, MAPE of 0.217, MAD of 9.218 and SI of 1.238. In the O2O environment, the Okumura-Hata model achievedthe highest R2 value of 0.978, indicating a strong fit with metrics NRMSE of 0.047, MSE of 27.807, MAPE of 27.494,MAD of 37.287 and SI of 3.927. This advancement in reliable communication networks promises to transform theopencast landscape into networked signal attenuation. These results support decision-making for mining needs and ensure reliable communications even in the face of formidable obstacles
... Technology for Integration Suitable for Use with Heterogeneous Substrates (HIT) It contained a description of a number of different technologies, such as injection-modeled troops, micro-fluidic systems, flexible multi-channels, and precision micro-components, to name just a few. These technologies were utilized in order to achieve the intended applications in sensor monitoring and healthcare diagnostics in order to attain a smaller product size while keeping the cost to a bare minimum [90][91][92][93][94][95]. The terms "ultra-high frequency," "radio frequency identification," "global system for mobile communications" (GSM), and "smart mobile" all refer to radio frequency technologies. ...
Article
Full-text available
AI and IoT should work together to enhance their respective businesses in the future. The "Internet of Things" includes various technologies. The Internet of Things (IoT) connects all physical objects via the internet. IoT devices have endless data in their processors and sensors, thus they can empower people in many areas of their life by tapping into the large quantity of data in the massive number of linked devices. Using Internet of Things data can do this. Both. "Artificial intelligence and machine learning algorithms can control them. AI is the practical approach to controlling the Internet of Things' linked components (IoT). The biggest challenge is understanding and managing endless data from IoT devices. To enhance operations, businesses are using machine learning, a successful area of artificial intelligence (ML). Machine learning on IoT data lets smart systems make accurate predictions". AI applications for IoT help firms improve risk management, efficiency, product development, and downtime. Healthcare, autonomous cars, smart homes, agriculture, and marketing applications employ this combination most. This survey article examines healthcare AI and IoT advances. These applications have several flavors. It also discusses AI-based Internet of Things methods that can be used in medical to predict diseases sooner. This can improve doctor-patient care. INTRODUCTION The extension of preexisting internet services is what is known as the Internet of Things (IoT). It provides a definition of the network of physical objects or items that are present in the modern world. [Citation needed] The Internet of Things is dependent on application programming interfaces (APIs), sensors, mobile phones, and actuators in order to connect objects to one another or exchange data via the internet. It enables the connection of all of the devices to the internet as well as the connection of those devices to those that are already connected. It is envisaged that in the future, inanimate things will be able to recognise themselves and converse with one another independently, without the need for any assistance from humans [1-3]. In point of fact, the strength of the Internet of Things enables it to produce tremendous effects on a range of "aspects of everyday life and the behaviours of potential users". These effects can have a significant impact on the development of new businesses and industries. The most important uses for it may be found in contemporary computing methods such as pervasive computing and ubiquitous computing, as well as in communication technologies, embedded devices, sensor networks, and internet protocols. According to the results of the survey, it is projected that the value of the Internet of Things market on a global scale would be larger than one billion dollars by 2017. This projection is based on the fact that it is expected that the market will exist by 2017. This indicates the rapid growth that is taking place in the quantity as well as the variety of the data that is being gathered. As a direct consequence of this, systems that are founded on the Internet of Things generate a substantial amount of data, which is also referred to as "big data." The data cannot be processed using the standard methods or algorithms that are often used for data processing. These days, "Artificial Intelligence (AI) is the most important factor in imitating human tasks such as industrial applications, healthcare applications, business monitoring, research and development, product development, business process, share market prediction, social network analysis, and environmental control [4-9]. Examples of these tasks include industrial applications, healthcare applications, business monitoring, research and development, product development" and environmental control. It is now capable of doing tasks that were hitherto reserved solely for the intellectual capacity of humans. Artificial intelligence has the capabilities of being aware of its surroundings, being immersed in its environment, being customised and predictive. The Internet of Things (IoT) and artificial intelligence (AI) are going to be extremely important in the future in a variety of different contexts. "It is largely necessary for the participation of enterprises, governments, scientists, engineers, and technicians in order to put it into practise in a number of different situations. When combined in servers, artificial intelligence and the Internet of Things present a multitude of potential benefits and opportunities. For example, artificial intelligence (AI) is made up of machine learning, which analyses data in order to make projections about future events. [10-12]These projections can include the need to place replacement orders in the marketing department or the need to repair equipment in the manufacturing department. In addition to this, artificial intelligence may employ machine learning in order to develop a "smart home" atmosphere. [13-15]. It is quite possible that AI techniques paired with IoT might be utilised to recognise human behaviour through the use of sensors and Bluetooth signals in order to make the required modifications to the room's lighting and temperature.
... Additionally, cutting-edge technologies, especially in healthcare services and applications, are emerging rapidly. In-home patient and elderly monitoring is becoming a viable and low-cost alternative, especially during pandemics or when hospitals are crowded (da Costa et al. 2018;Rodrigues et al. 2018). In this type of monitoring, patients wear wearable sensors to measure vital signs (VSs) and send the data to the hospital's medical staff via wireless communication. ...
Article
Full-text available
This paper introduces a new protocol named MEDCO for eMErgency Detection and COmpression, designed to minimize data transmission and optimize sensor energy usage in wireless body sensor networks. MEDCO operates in two stages. The first stage assesses the patient’s condition based on vital signs and compares it with the previous state to determine if the data should be transmitted to medical staff. Data is only sent if a change in the patient’s situation is detected. The second stage focuses on compressing the identified data using two algorithms: range and changed vital signs methods. The range method classifies patient readings into ranges based on the current health situation before compressing them. At the same time, the changed vital signs algorithm considers both current and previous situations during compression. Through simulations using actual patient data, we demonstrated the effectiveness of our protocol in reducing data transmission by 97% while maintaining a high level of accuracy in the transmitted information. The range method outperforms by achieving an additional data reduction of 34.6% compared to the selected protocol from state of the art, and the changed vital signs method achieves a reduction of 6.4%.
Article
Full-text available
Smart Health Care provides efficient, sustainable, and real-time human services, with its development rooted in the idea of an improved Internet of Things (IoT). However, the integration of these IoT-centric devices with other organizations raises security concerns, as unauthorized users may exploit the available data. Given that alterations in data values from sensors can impact the diagnostic process, potentially leading to severe health issues, ensuring data security and privacy emerges as a paramount concern in the healthcare industry. The Internet of Medical Things (IoMT) signifies a set of healthcare technology that allow care providers, doctors, and medical testing establishments to store and share health data automatically. Despite this, guaranteeing data privacy protection during IoMT transmission is a difficult challenge. To solve these issues, the combination of blockchain (BC) and IoMT provides resilience against multiple attacks while maintaining anonymity. Blockchain successfully enables peer-to-peer data transmission, detecting a large number of potential dangers. The creation of blockchain networking based on the TDES-EKMC addresses the limitations of blockchain networking in the IoMT domain. For user mutual authentication, the proposed framework employs the ECMQV-MAC protocol, the DC-PC for data storage module, and the DM-BGC for key generation. This protocol assures that users have the legal right to use blockchain networking (BCN), while also avoiding superfluous data storage in the blockchain. The key generation process strengthens data privacy defenses against both internal and external attacks. Finally, authorized users can contribute healthcare data to the blockchain, and TDES-EKMC provides secure data transmission in blockchain networking. The suggested framework beats the traditional state-of-the-art technique in terms of throughput, Packet Delivery Ratio (PDR) value, privacy leakage, and the additional time necessary to build new blocks, according to test findings.
Chapter
The Internet of Things (IoT) and Artificial Intelligence (AI) have gained widespread applications in various sectors as a result of recent advancements in technology and communication. One industry where IoT and AI, either separately or in combination, have significant impacts is healthcare, which is always under pressure to reduce costs while caring for a constantly growing ailing population. The potential of AI made the field of IoT healthcare aims to conceptualise about how IoT and AI might improve the proactive, continuous, and coordinated nature of our current secondary and tertiary healthcare system, as well as make preventative public health services more widely available. Furthermore, IoT and AI promote responsibility and fulfilment by motivating patients to collaborate more closely with their doctors. Aside from this, IoT offers numerous benefits for optimising and improving healthcare delivery, allowing for the accurate prediction of health problems as well as the diagnosis, treatment, and follow-up of patients both within and outside of hospitals.
Chapter
Wearable electronics have garnered significant attention recently due to their potential applications in health monitoring, fitness tracking, and personalized communication devices. However, developing robust, flexible, and lightweight materials for wearable electronics remains challenging. Carbon nanotube-polymer nanocomposites have emerged as a promising solution to address these requirements. This chapter comprehensively examines the latest advancements in carbon nanotube-polymer nanocomposites for wearable electronics. The chapter highlights these nanocomposites’ unique mechanical, electrical, and thermal properties, making them suitable for wearable electronic applications. These nanocomposites have the ability to improve the performance and durability of electronic devices integrated into clothing and accessories, making them ideal for applications in the rapidly growing field of wearable technology. This chapter discusses recent advancements in synthesizing and characterizing carbon nanotube-polymer nanocomposites specifically designed for wearable electronics. We also highlight the challenges and future prospects of utilizing these materials in commercial wearable technology products. This typical chapter also aims to provide valuable insights into the potential of carbon nanotube-polymer nanocomposites as a key enabling material for the next generation of wearable electronic devices.
Chapter
Full-text available
Due to Industry 4.0 and its crucial supporting technologies for information and communication, the development and service industries are seeing a radical shift. Big data, the internet of things (IoT), cloud computing, and fog computing are revolutionising the eHealth ecosystem and driving it towards Healthcare 4.0. The health industry, in particular, is experiencing a rebirth of eHealth. As a set of technological procedures bridging the gap between the digital, biological, and physical realms, Healthcare 4.0 has just evolved. Health process efficiency and reliability are the goals of these procedures, which also establish norms for the data-to-information-usage and -access transition. But there's a constantly evolving problem that needs fixing before we can operationalize and qualitatively describe Industry 4.0's contributions to the healthcare industry. This chapter examines the health sector in depth in light of Industry 4.0 and all of its characteristics.
Article
Full-text available
The Internet of Things (IoT) is a paradigm in which smart objects actively collaborate among them and with other physical and virtual objects available in the Web in order to perform high-level tasks for the benefit of end-users. In the e-health scenario, these communicating smart objects can be body sensors that enable a continuous real-time monitoring of vital signs of patients. Data produced by such sensors can be used for several purposes and by different actors, such as doctors, patients, relatives, and health care centers, in order to provide remote assistance to users. However, major challenges arise mainly in terms of the interoperabil-ity among several heterogeneous devices from a variety of manufacturers. In this context, we introduce EcoHealth (Ecosystem of Health Care Devices), a Web middleware platform for connecting doctors and patients using attached body sensors, thus aiming to provide improved health monitoring and diagnosis for patients. This platform is able to integrate information obtained from heterogeneous sensors in order to provide mechanisms to monitor, process, visualize, store, and send notifications regarding patients' conditions and vital signs at real-time by using Internet standards. In this paper, we present blueprints of our proposal to EcoHealth and its logical architecture and implementation, as well as an e-health motivational scenario where such a platform would be useful.
Article
Full-text available
It is the nature of life that with advancing age human brain cells are damaged and brain functions degrade, which is manifested by memory loss, lack of concentration, reduced judgement abilities as well as speaking problems and even noticeable changes in personality and behavior. When a number of these symptoms collectively detected in a person, he usually diagnosed with dementia. These people have problems in managing their daily activities and shortly become unable to live by themselves. Ways to prolong the independent life of elderly people have drawn the attention of a large scientific community. In this paper, we present an integrated system design that allows collection, recording and transmission through a cloud application of the data from different sensors placed in a house of a person having dementia. The concept of this work is a part of the so-called Internet of Things (IoT) where information from smart things around us can be evaluated and transmitted over the internet.
Article
Full-text available
An important goal of smart grid is to leverage modern digital communication infrastructure to help control power systems more effectively. As more and more Internet of Things (IoT) devices with measurement and/or control capability are designed and deployed for a more stable and efficient power system, the role of communication network has become more important. To evaluate the performance of control algorithms for inter-dependent power grid and communication network, a testbed that could simulate inter-dependent power grid and communication network is desirable. In this paper, we demonstrate design and implementation of a novel co-simulator which would effectively evaluate IoT-aided algorithms for scheduling the jobs of electrical appliances. There are three major features of our cosimulator: 1) large-scale test is achieved by distributed modules that are designed based on a Turing-indistinguishable approach; 2) remote servers or test devices are controlled by local graphical user interface (we only need to configure the simulator on a local server); 3) a Software Virtual Network (SVN) approach is employed to emulate real networks, which significantly reduces the cost of real-world testbeds. To evaluate our co-simulator, two energy consumption scheduling algorithms are implemented. Experimental results show that our co-simulator could effectively evaluate these methods. Thus our co-simulator is a powerful tool for utility companies and policy makers to commission novel IoT devices or methods in future smart grid infrastructure.
Article
Full-text available
Knowledge on the dynamic properties of bridges in a city can improve condition assessments, maintenance scheduling, and emergency planning to better serve the public. Currently, bridge vibration data is obtained primarily by researchers through the use of a sophisticated sensor network that is composed of fixed sensor nodes. Recent studies have supported the alternative of mobile sensor networks, which are capable of delivering important structural information, e.g., modal properties, requiring less setup efforts and using fewer sensors. Simultaneously, digital technology has spawned data initiatives such as crowdsensing, in which individuals can collectively sense the urban environment. The prevalence of smartphones, which contain various advanced sensors, is rapidly restructuring researchers’ perceptions of data collection. This paper discusses the confluence of these emerging technologies, which can provide regular infrastructure data streams, within structural health monitoring (SHM) procedures for the immediate goal of system identification (SID) and towards automated maintenance of bridges. Will researchers continue to install sensor networks and collect their own data or will they start to source resident smartphone data? One of the objectives of this ongoing work is to quantify expected smartphone data stream volumes that would be applicable to SHM processes. As an example, the number of smartphones that traverse the Harvard bridge in a month is quantified.
Article
Full-text available
In China, most of heart attack results in death before the patients get any treatment. Because the traditional healthcare mode is passive, by which patients call the healthcare service by themselves. Consequently, they usually fail to call the service if they are unconscious when the heart disease attacks. The Internet of Things (IoT) techniques have overwhelming superiority in solving the problem of heart diseases patients care as they can change the service mode into a pervasive way, and trigger the healthcare service based on patients’ physical status rather than their feelings. In order to realize the pervasive healthcare service, a remote monitoring system is essential. In this paper, we proposed a pervasive monitoring system that can send patients’ physical signs to remote medical applications in real time. The system is mainly composed of two parts: the data acquisition part and the data transmission part. The monitoring scheme (monitoring parameters and frequency for each parameter) is the key point of the data acquisition part, and we designed it based on interviews to medical experts. Multiple physical signs (blood pressure, ECG, SpO2, heart rate, pulse rate, blood fat and blood glucose) as well as an environmental indicator (patients’ location) are designed to be sampled at different rates continuously. Four data transmission modes are presented taking patients’ risk, medical analysis needs, demands for communication and computing resources into consideration. Finally, a sample prototype is implemented to present an overview of the system.
Conference Paper
Full-text available
The Internet of Things (IoT) vision requires increasingly more sensor nodes interconnected and a network solution that may accommodate these requirements accordingly. In wireless sensor networks, there are energy-limited devices; therefore techniques to save energy have become a significant research trend. Other issues such as latency, range coverage, and bandwidth are important aspects in IoT. It is considering the massive number of expected nodes connected to the Internet. The LoRaWAN (Low Power WAN Protocol for Internet of Things), a data-link layer with long range, low power, and low bit rate, appeared as a promising solution for IoT in which, end-devices use LoRa to communicate with gateways through a single hop. While proprietary LPWAN (Low Power Wide Area Network) technologies are already hitting a large market, this paper addresses the LoRa architecture and the LoRaWAN protocol that is expected to solve the connectivity problem of tens of billions of devices in the next decade. Use cases are considered to illustrate its application alongside with a discussion about open issues and research opportunities.
Article
In today's world, many people feel stressed out from school, work, or other life events. Therefore, it is important to detect stress and manage it to reduce the risk of damage to an individual's well being. With the emergence of the Internet of Things (IoT), devices can be made to detect stress and manage it effectively by using cloudbased services and smartphone apps to aggregate and compute large data sets that track stress behavior over long periods of time. Additionally, there is added convenience via the connectivity and portability of these IoT devices. They allow individuals to seek intervention prior to elevated health risks and achieve a less stressful life.
Article
The assessment of spatiotemporal gait parameters is a useful clinical indicator of health status. Unfortunately, most assessment tools require controlled laboratory environments which can be expensive and time consuming. As smartphones with embedded sensors are becoming ubiquitous, this technology can provide a cost-effective, easily deployable method for assessing gait. Therefore, the purpose of this study was to assess the reliability and validity of a smartphone-based accelerometer in quantifying spatiotemporal gait parameters when attached to the body or in a bag, belt, hand, and pocket. Thirty-four healthy adults were asked to walk at self-selected comfortable, slow, and fast speeds over a 10-m walkway while carrying a smartphone. Step length, step time, gait velocity, and cadence were computed from smartphone-based accelerometers and validated with GAITRite. Across all walking speeds, smartphone data had excellent reliability (ICC2,1≥0.90) for the body and belt locations, with bag, hand, and pocket locations having good to excellent reliability (ICC2,1≥0.69). Correlations between the smartphone-based and GAITRite-based systems were very high for the body (r=0.89, 0.98, 0.96, and 0.87 for step length, step time, gait velocity, and cadence, respectively). Similarly, Bland-Altman analysis demonstrated that the bias approached zero, particularly in the body, bag, and belt conditions under comfortable and fast speeds. Thus, smartphone-based assessments of gait are most valid when placed on the body, in a bag, or on a belt. The use of a smartphone to assess gait can provide relevant data to clinicians without encumbering the user and allow for data collection in the free-living environment.
Article
With the advancement of mobile technologies, smartphone applications (apps) have become widely available and gained increasing attention as a novel tool to deliver dermatologic care. This article presents a review of various apps for skin monitoring and melanoma detection and a discussion of current limitations in the field of dermatology. Concerns regarding quality, transparency, and reliability have emerged because there are currently no established quality standards or regulatory oversight of mobile medical apps. Only a few apps have been evaluated clinically. Further research is needed to evaluate the utility and efficacy of smartphone apps in skin cancer screening and early melanoma detection.