Heterogeneous Internet of Medical Things (Het-IoMT) in Pervasive Healthcare to Enable Ambient Assisted Living – Research Article
Internet of medical things for smart
D3S to enable road safety
International Journal of Distributed
Sensor Networks
2019, Vol. 15(8)
Ó The Author(s) 2019
DOI: 10.1177/1550147719864883
journals.sagepub.com/home/dsn
Muhammad Ramzan1,2, Shahid Mahmood Awan1, Hamza Aldabbas3 ,
Adnan Abid1, Muhammad Farhan4 , Shehzad Khalid5 and
Rana M Amir Latif4
Abstract
It has been seen that most of the accidents occur due to driver’s fatigue. Drowsiness is a state of mind before the driver
falls asleep, which means the driver could not accomplish his actions, such as vehicular braking, controlling vehicular
motion, properly. We have built an Internet of things–based medical application to analyze driver’s drowsiness. An architecture has been proposed and a simulation of that scenario in NS3 WSN simulation tool has been done. This simulation
shows that the ratio of accidents can be majorly reduced. When drowsiness of drivers is captured, a message alert is
delivered to all other drivers of the vehicles that are near to the sleeping driver; for this, different sensor nodes are used.
Another unique feature of the sensor network used here is the collaborative effect of sensor nodes. So for measurement and analysis of applications on Google Play, a dataset of the medical applications category was scraped. The scraping was done with 550 applications of each category of medical applications. On each application on Google Play store,
almost 70 attributes for each category were scraped. It is envisioned that, in future, wireless sensor networks will be an
integral part of our lives, more so than the present-day personal computers.
Keywords
Driver’s drowsiness detection system (D3S), vehicular motion, Internet of medical things, wireless sensor networks,
Google play store, healthcare, scraping
Date received: 30 January 2019; accepted: 29 May 2019
Handling Editor: Suleman Khan
Introduction
1
Current breakthroughs in Wireless Body Area
Networks (WBANs) have resulted in various emerging
applications in pervasive healthcare.1 Using video sensors to transmit appropriate medical information
including electrocardiogram (ECG), electromyogram
(EMG), temperature, glucose level, blood pressure,
audio-visual clinical notes, x-ray, magnetic resonance
imaging (MRI), and others could potentially improve
patient care and improve Quality of Experience (QoE)
through real-time monitoring and diagnosis of threatening diseases.2,3 In recent years, many researchers
have started working on smart healthcare solutions to
Corresponding author:
Muhammad Farhan, Department of Computer Science, COMSATS
University Islamabad, Sahiwal Campus, COMSATS Road Off GT Road,
Sahiwal 57000, Pakistan.
Email: farhansajid@gmail.com
School of System and Technology, University of Management and
Technology, Lahore, Pakistan
2
Department of Computer Science & IT, University of Sargodha,
Sargodha, Pakistan
3
Prince Abdullah Bin Ghazi Faculty of Information and Technology, AlBalqa’ Applied University, Salt, Jordan
4
Department of Computer Science, COMSATS University Islamabad,
Sahiwal, Pakistan
5
Department of Computer Engineering, Bahria University, Islamabad,
Pakistan
Creative Commons CC BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 License
(http://www.creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without
further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/
open-access-at-sage).
2
provide better healthcare services to the community.
However, delivering sensed biomedical data over heterogeneous wireless networks,4,5 energy consumption
of sensor nodes, multimedia content delivery, Internet
of medical things (IoMT) data processing requirements, big medical data analytics, wireless link reliability, diverse communication environments, decision
support systems, and issues related to seamless data
streaming faced by the current and next generation of
wireless networks are of great concern.6
The number of road accidents has drastically
increased in Pakistan in the last two decades. This
causes enormous monetary and emotional misfortune.
The increased number of road accidents is because of
driver absentmindedness or driver drowsiness. The
driver’s sleepiness is caused carelessness situation.
These road accidents are fundamentally slippery to
human security. The side effects of driver weariness
fluctuate from driver to driver, and includes the following: loss of mindfulness, lethargy, yawning, torpid
responses, red eyes, microsleep, dreariness, eagerness,
missing street signs, trouble in keeping their head up,
switching to another lane, and so forth. Although the
reasons for driver weariness are various, researchers
have recognized a few elements.8
Vehicular-based
measures
are
non-invasive,
embedded in the vehicle to monitor the driver steering
wheel pattern, braking pattern, and lane departure for
drowsy detection. Steering wheel measurements are
known to be highly efficient and the most accurate
method for measuring drowsy driving, as compared
with all other methods of this category. Although
vehicular-based methods are cost-effective, they are not
considered as reliable methods because of driver’s
skills, road traffic, and the condition of the road.9,10
The third category consists of the driver’s behavior.
It includes eye blinking, yawning, eye closure, and head
positioning and other facial expression analyses.
Behavioral measures are considered as more effective
compared with vehicular-based measures because they
focus on the driver himself or herself than on the vehicle and because it is practically easier to measure. In
the behavioral measures method, information is gathered through video cameras and driver’s facial expressions are monitored. Nowadays, behavioral measures
have become a widely used technique due to the reliability. To obtain more reliability in results, researchers
are fusing physiological information into behavioral
measures to design a hybrid detection system.11
To avoid accidents, researchers suggest the drivers
take naps between long trips, intake coffee and energy
boosters, or drive with someone for company. It will
help them to stay awake and alert. In this research article, our focus is to compare different techniques of
behavioral measures and provide a systematic review of
the technique.
International Journal of Distributed Sensor Networks
Smart-Phone applications
The Apple App Store was launched just a few years
back in 2008; however, the number of apps as of 2017
was around 2,200,000. In addition, in accordance with
Apple’s policy, program developers earned around
US$20 billion on their own. Although programs in the
Health and Physical Fitness and Medical categories
account for a relatively low percentage of just 4.87% of
the total apps within the app store, statistics indicate
that nearly 60% of smartphone users have now
installed at least one health-related program on their
mobiles. In all, the worth of the cellular wellness sector
equates to US$6.7 billion in 2012, and it is forecast to
increase to US$58.8 billion by 2020.12 The explosion of
programs available for people in the Apple Store and
Google Play store makes it harder to get M-health
application programmers to draw prospective end
users’ focus, bringing them their program, also then
motivating them to make use of their own programs
within an ongoing interval. After downloading, nearly
70% of users now utilize their programs rarely, 26%
use most of the downloaded programs just one time
and 74% have stopped using the apps completely.13
A great number of third-party applications in
Google Play store allow visitors. An incredible number
of end users use these apps on their tablets and on their
mobiles, and enroll themselves on their own private
devices. Several applications are uploaded by countless
programmers on a daily basis. The truth is that the articles uploaded online that users engage in are largely
unchecked, and countless end users download these
apps. However, there is really very little information
regarding the applications on Google play store. The
reason for this is the fact that there is a shortage of
scalable applications to assess and detect applications.14 The source code of third-party programmer
applications are not available with Google. It also does
not have any accessibility into the source code of even
those apps that are filed directly into the Google perform shop as compacted binary options. Google places
many procedures to avoid malfunctioning and also
engages in retail store information indexing. For
instance, Google play store dataset is bound by just
500 application detection owned by some kind of game
using almost any Internet search duration by simply
surfing the shop interface. In order to learn more about
the application from Google Play store material, we
have crawled info from Google play store using crawling and scrapping methods. Thus, depending on data
from Google Play store, we will quantify and study the
exact content of all Google Play store metadata and
also store the data. Subsequent to the resurrection of
countless of applications, we have identified a separate
application, including paid and completely free.15 We
plotted the data retrieved using Pie3D of R.
3
Ramzan et al.
pie3D scales the values in x so that they total 2*pi,
dropping zeros and missing values in the form of not
available values (NAs). It subsequently exhibits a
vacant storyline, computes the arrangement for drawing on the businesses and also requires draw.tilted.sector to attract just about every and every sector. When
tags are provided, it is going to telephone pie3D.label
to set those out each and every sector. When furnished,
the range of tags, tag places, and also business colors
needs to be equal to the range of values in x-ray. If the
event labels have been prolonged, then it may possibly
help reduce the radius of this pie or transform the
career, a common purpose for all R objects. For additional information regarding the graphics parameter
arguments. For uncomplicated scatter plots, plot.default has to be properly used. But, you will find storyline means of several items, for example, roles, frames,
data, density items, used techniques and so on.16
CIRCOS is an application program for visualizing
data and information. It visualizes information within a
circular layout, which tends to make CIRCOS ideal for
investigating connections between positions or objects.
There are other reasons why a circular layout is advantageous, not the least being the fact that it is attractive.
Circos is ideal for creating publication-quality infographics and illustrations with a high data-to-ink ratio,
richly layered data, and pleasant symmetries. You have
fine control each element in the figure to tailor its focus
points and detail to your audience.17
Because such calculation programs are complex and
highly technical, the demand for exceptionally scalable
and accurate system simulation engineering becomes
increasingly necessary. In spite of the development of
large-scale test beds for system investigation, simulation
plays a critical part in conditions of scalability (both in
measurement and also at the experimental rate), reproducibility, and quick design, along with instruction.
Together with simulation-based scientific tests, the procedure could be learned at length in varying scales, even
together with varying statistics tools, different subject
requirements, and also should lead to reproducible and
analyzable outcomes as discussed in the literature.18
Our system has an 85% accuracy of the accepted
level to detect drowsiness of drivers by monitoring eye
closure. We have built an Internet of things (IOT)based application to analyze the driver’s drowsiness.
The architecture has been simulated using the NS3
WSN simulation tool. The ratio of accidents will be
reduced after the simulation. We have used different
sensors, which will generate a notification that will be
sent to all those drivers who are nearer to that driver;
also theses sensors are fitted with sensor board and
work synchronously. These features have a wide range
of application for sensor networks such as health,
military, and security. We could possibly simulate the
architecture diagram through a wired channel.
However, this will be very costly, and in real life, we
cannot connect the vehicles with wires. So we have two
scenarios for connection: first is the true wireless connection and second is a false wireless connection
between vehicles. For simulation in NS3, we used 11
vehicles that can move freely in the given space. If any
of the drivers from the 11 vehicles are detected to be in
sleepy mood, an alert message will be generated in all
other vehicles. Moreover, in the true case when there is
no driver in sleepy mood, the message will not be generated, and so only Vehicle 1 will move, whereas other
vehicles will not move. We have just two types of applications: paid and free. We scraped thousands of health
applications from Google Play store by using the google-play-scraper. We scraped around 550 health applications in both free and paid categories with different
attributes, namely, rankings, in-app purchases (IAPs),
advertisement support, and installations. For analysis
and visualization of the health application, we have
used RStudio and CIRCOS, respectively.
The article is organized into the following sections.
The first section is the introduction, which gives an
overview of the study. The second section presents a
review of existing literature on the topic. The third section describes the smart D3S framework based on
IoMT, and the fourth section comprises the results and
discussion, which has two subsections. Each subsection
describes different types of health-related applications
in Google Play store. The last and fifth section concludes and discusses the way forward.
Literature review
Behavioral techniques display various constraints
because their operation will be influenced by light
requirements, camera motions, and also the framework
speed utilized to capture pictures of the driver’s encounter. A light variant could normally be removed using
infrared (IR) cameras. Blink detection from the filter is
directly obtained using electrooculography (EOG)
signs. A filter was employed for eradicating the ramble
from the EOG sign and for accentuating blink detection.19 Eyeblink switch from one individual to another
along with carbon filter eliminates completely speedy
eye motions. Possible interactions are discovered and
amplitude is expressed using a method of simulating
driver drowsiness with numerous eyelid movement features predicted with a combination of data using partial
least squares regression (PLSR). This was combined
with a method to handle the issue of sturdy collinear
connections (a method used for detecting embryo
4
International Journal of Distributed Sensor Networks
motion features), therefore forecasting the inclination
of drowsiness.20
Krogh et al.22 introduced a visual investigation of
attention condition and mind presence for steady
observation of endurance of the car driver. Most of the
present designs for discovery of non-alert driving are
dependent on close attention or nodding angles to
decide on driver drowsiness or diversion. An approach
to assessing the facial manifestation of the driver was
done using a hidden Markov model (HMM)-established lively modeling to assess drowsiness. The plan is
currently executed with a simulated driving arrangement.21 The accuracy and reliability of driver drowsiness detection using physiological signs are extremely
high in comparison with different procedures.
Nevertheless, the more straightforward character of
quantifying bodily signs remains a matter to be tackled.
To overcome this, a wireless apparatus can be utilized
to quantify physical signs in a significantly less invasive
way by setting the electrodes in the system and receiving signs utilizing wireless systems such as Zigbee,
Bluetooth, and so forth.22
A few machine learning methods were utilized by
some researchers, which include things such as support
vector machines (SVMs), convolutional neural networks (CNNs), and HMMs from the drowsiness detection perspective. The support vector system procedure
could be the most frequently used strategy to find drowsiness; however, CNNs did a lot better compared with
both of the other methods.23 In general, diverse facial
functions such as eye finishing investigation, eye wind
speed, yawning, and facial manifestation are all examined employing these machine learning methods. SVMs
try to use a hyperplane that divides coaching data into
pre-defined categories. From the driver drowsiness perspective, SVMs are chiefly utilized to understand how
to classify various conditions of the driver using standard information. An amazing amount of effort has
expended to use the capacities of SVMs in the discovery
of drowsiness.24
Otoum et al.25 introduced a detection procedure utilizing machine learning methods; they detected the head
using the Viola–Jones object detection framework, utilizing cascade item sensor classifiers, after which SVM
and main element analysis are done. This machine
accomplished top precision in discovering drowsiness.
The famous Haar attribute algorithm has been used in
combination with unique machine learning processes
such as AdaBoost classifier using SVM. The accuracy
of this machine was quantified as roughly 99.1%.
HMM is a statistical method utilized to create forecasts
regarding concealed, and says predicated on detected
nations characterized by chances.25 Now, HMMs have
widespread usage in applications such as facial expression recognition, gene annotation, mimicking DNA
series glitches, and in computer virus erosion.26 The
authors have utilized an infrared (IR) digital camera to
get rid of light fluctuations and for simulating the daytime and nighttime conditions. CNNs are very similar
to a regular neural system that is additionally composed of neurons that contain weights. CNNs make use
of the levels of plasma convolutions that are ideal to
graphics and display powerful spatial correlations.
CNNs have shown powerful application in picture recognition, movie examination, and in many other fields.
While the application of CNNs in computer vision was
just discovered in 2012, research has now found profound benefits of using them in object recognition.27
These machines work even in the event the driver has
been shifting headset posture as shown in Table 1.
Lots of metrics, for example, deviations from lane
posture, movements of tires, hand gripping over the
tires, lane passing, and strain onto the speed pedal, are
always tracked and any alteration from those which
spans a predetermined threshold signals a considerably
increased likelihood of the driver being more fatigued.
Driver support methods have been utilized to track the
Table 1. Driver drowsiness detection using behavioral measures.
Author
Year
Journal / Conference
Measure
Classifier
Accuracy
Verma et al.20
2018
Eye state
Viola–Jones algorithm
97.5%
Memon et al.28
2017
Eye closure
Haar cascade object
detector with
OpenCV
82%
Juboori29
2017
Face detection and eye
closure
2016
Cascade object
detector with SVM
and PCA
Haar-like features and
AdaBoost Classifier
with an SVM classifier
Above then 90%
Ambekar et al.27
Ambient Communications
and Computer Systems
Conference on Signal
Processing and
Communication Systems
(ICSPCS)
International Research
Journal of Engineering and
Technology
International Journal of
Science Technology
Management & Research
PCA: principal component analysis; SVM: support vector machine.
Face and Eye state
99.1%
5
Ramzan et al.
car condition. These techniques offer assistance or support for the driver while driving and also function by
simply taking an input signal from sensors across the
automobile to calculate some sort of suggestions that
are subsequently utilized to help the driver of the automobile. Lane departure warning (LDW) always tracks
the place of the automobile onto the lane mark on both
sides. In case the automobile lane departure is only
slightly off the mark, the driver is advised and also a
corrective step can be carried out. Utilizing LDW, accidental lane death brought on by driver’s inattention,
distractions, and fatigue might be decreased. LDW systems catches the trail beforehand and the driver receives
a warning as soon as the automobile deviates and
reaches or approaches the lane marks. The caution
could be an audible tone or even a visual alarm or
vibrations within the tire or even the driver’s chair. In
case the driver is deliberately crossing the lane, then no
warning is generated.30
The lane discovery procedure is made of distinct
modules such as shooting video/image of the trail lane
indicating lane modeling, feature extraction, lane discovery and lane monitoring, and LDW methods. Lane
simulation can be utilized to get a statistical outline of
street lane marks. Distinct lane characteristics such as
the border, feel, and shade are pulled. Lane detection is
performed by matching the lane version with all the
other attributes, and lane adjustments are done using
the lane observation module. Throughout execution,
differently sophisticated predicaments such as lane type
S, road surface, daylight, ecological elements, shadow,
rain, types of lane marks, street surface area, lighting
variation, nighttime situations, and fog, along with the
sun are also believed to have a significant effect. Use
these factors, the lane detection endeavor is achieved
by edge detection and lane discovery.31
Personalized driver model (PDM) is additionally a
lane death procedure which discerns the faculties of a
person’s driving mode. PDM refers to the driver’s lanekeeping behavior and lane death behavior using a combined probability density function for Gaussian mixture
model (GMM) among auto rate, comparative yaw
angle, comparative yaw speed, and lateral displacement,
along with highway curvature. The latest analysis
attempted to check distinct steering processes so as to
describe the driver’s steering by monitoring hand position employing simulators and movement grids.32 But
just concentrating on hand position may not be enough.
The American Automobile Association reports that
besides improper steering behind the steering wheel,
tired driving takes liability for 12.5% of car or truck
accidents, requiring treatment for passengers and drivers. It would be very expensive to employ cable-less
detectors to quantify driver’s hand tilting routines and
heart rate simultaneously with hand gripping.33 These
devices can supply cheap but high-performance analytics. Real simulation for the research and clinical fields
are shown in Table 2.
Drowsiness can be discovered via heartbeat pace and
heartbeat, along with brain info. Heart speed fluctuates
greatly during different phases of drowsiness, for example, as for example fatigue and alertness. However, an
electroencephalogram (EEG) is the most frequently
used procedure as it reveals many frequency groups
such as delta, alpha, beta, and theta bands that match
different sleeping and comfort, endurance, and
Table 2. Drowsiness detection system using vehicular measures.
Author
Year
Journal
Measure
Classifier
Accuracy
Narote et al.30
2018
Pattern Recognition
Lane departure
87.88% in a day and
94.13% at night
Wang et al.33
2018
Wang et al.34
2018
European Journal of
Biomedical
Informatics
arXiv
The hand gripping
on the steering
wheel
Lane departure
Samuel et al.35
2014
Open Journal of
Safety Science and
Technology
Steering wheel
movements
LDWS, piecewise linear
stretching function
(PLSF), Grayscale, Hough
transform and Euclidean
distance with various
other detection
techniques.
Pressure sensor sheet is
used to measure the hand
gripping pattern.
Lane departure prediction
(LDP), TLC-PDM
method, Gaussian
mixture model (GMM),
HMM
Steering wheel
movements (SWM)
94%
Up to 100%
91%
LDWS: lane departure warning system; HMM: hidden Markov model; TLC-PDM: time to lane crossing-personalized driver model.
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International Journal of Distributed Sensor Networks
Table 3. Drowsiness detection system using physiological measures.
Author
Year
Journal
Measure
Classifier
Accuracy
Wang et al.34
Kartsch et al.36
2018
2018
Cognitive Neurodynamics
Information Fusion
2018
Chai et al.38
2017
Progress in Computing,
Analytics, and Networking
Journal of Biomedical and
Health Informatics
Dry EEG
Inertial measurement unit
(IMU) sensor with EEG
electrode
EEG with PCA, KNN,
CNN
EEG with AR, BNN
Nearly 100%
95.2%
Poorna et al.37
Physiological measure
Fusing physiological
information with behavior
information
Physiological measure
with eye blinks
Physiological measure
85%
88.2%
EEG: electroencephalogram; CNN: convolutional neural network; PCA: principal component analysis; AR: autoregression; KNN: k-nearest
neighbors; BNN: binarized neural networks.
exhausted conditions. EEG and EOG sensors are excellent since the sign-outs of EEG are assessed using signs
of EOG and bogus alarm speed declines of up to 5%.
Researchers state that the drowsiness of a driver can be
discovered by a person’s eye motion tracking along
with biosignal processing. They also suggest that warnings such as an enhanced driver assist module that
caters to computerized driver drowsiness detection and
detects driver downtime can lessen the range of these
deaths.34 Artificial-intelligence algorithms have been
utilized to analyze the visible information as a way to
discover, monitor, and examine the driver’s eyes and
face to directly calculate drowsiness and diversion indicators. These real-time procedures work throughout
nocturnal states using a consequence of the nearinfrared light procedure. Physiological step analytics
were combined with behavioral step analytics to construct a hybrid system. These devices provide top precision outcomes: the classifiers utilized in those systems
are EEG with diverse machine learning classifiers such
as principal component analysis (PCA), k-nearest
neighbors (KNNs), CNN, autoregression (AR), and
with binarized neural networks (BNN) as shown in
Table 3.
Since 2006, deep learning (DL) has come to be a
fast-growing search management, redefining innovative
performances within a broad variety of subjects such as
object recognition, image segmentation, language recognition, and machine interpretation. In present-day
fabrication procedures, information system health
tracking is gaining acceptance as a result of the widespread installation of cheap detectors and also their
relation on the web. Meanwhile, DL offers invaluable
tools to process and assess the vast information. The
primary aim of this article is to examine and outline the
emerging search job of DL with regard to system medical tracking. In addition, an experimental analysis of
the advantages of both these approaches was performed. Last but not the least, some new styles of DLbased system health tracking processes have been
discussed.39
This article introduces a hybrid sensor system platform, on the basis of an IoT-linked basic safety and
wellness tracking application. The machine is directed
toward improving protection at the exterior office.40
The suggested platform comprises a wearable human
body network system (WBAN) to get user info along
with a low-power wide-area system (LPWAN).41 The
wearable detectors while in the WBAN are used to
assess the environmental requirements around the area,
working with a risk-free node, to track the most critical
signals, and with an overall health node. A standalone
community host, which may approach the raw detector
signs, exhibits both the physiological and environmental info, and activates an alert when an unexpected
emergency scenario is discovered, is built into the projected system. To join the gateway with the World
Wide Web, an IoT cloud host has been used for better
broadcast of web tracking and cellular applications.42
IoT has emerged as a brand new paradigm now,
linking an assortment of virtual and physical elements
incorporated with electronics parts, detectors, actuators, and applications to successfully get and swap info.
The focus on IoT is growing for important research
issues in the medical industry overall and in specific
areas such as mental health. The principal goal of this
article is to demonstrate the primary IoT applications
and services in the area of mental health ailments. The
databases that were inspected include Google Scholar,
IEEE Xplore, PubMed, Science Immediate, and
Internet of Science, considering literature from 2008 to
date.43 Several research standards have been created
for IoT-based or Web of Things–based mental health
services. An overall number of 51 posts are entirely on
IoT-based applications and services from mental
health, of which 14 are determined to be important in
emotional wellness. A number of books (over 60%)
were observed to demonstrate the application formulated for tracking individuals with emotional illnesses
through detectors and networked apparatus. The addition of IoT technological innovations in well-being
brings benefits in the areas of diagnosis, fertility
7
Ramzan et al.
interventions, and supplying warn and advice products
and services. Mental health support is a critical aspect
that determines the overall individual life caliber and
efficacy of one’s health support system.44
The internet of Things (IoT) has empowered remote
sensing and communication with assorted apparatus. In
the field of healthcare, IoT has many positive aspects of
tracking and alerting sufferers. IoT healthcare is important in most medical devices such as ECG monitors,
sugar degree detection, and oxygen focus discovery.
The advanced level technology has eased its execution
although it has its unique problems.
A health tracking system along with IoT will be a
more promising alternative to traditional healthcare
strategies.45,46 For the problem at hand, a small adaptive and acoustic real-time ECG tracking platform
incorporated on the T-shirt has been suggested.47 The
accumulated ECG statistics are sent through Bluetooth
low energy (BLE) into an end apparatus for the realtime exhibit. A computer’s graphical user interface
(GUI) along with a smartphone application is designed
for indoor and outdoor real-time observation. The
energy intake of this suggested wearable ECG tracking
system is as little as 5.2 mW. Powered with a a
240 mA h rechargeable battery, this will be able to
function for at least 110 h continuously. To lengthen
the duration of the battery life, an elastic solar power
harvester is additionally embraced.48
This study suggests a video transmission algorithm
to maximize medical quality of assistance with respect
to system metrics such as normal deviation, throughput, peak-to-mean ratio, delay, moderate delay, jitter
and normal jitter. The transmission of high-performance video monitoring along with retargeting at web
of multimedia matters along with Body Area Sensor
Networks (BSNs) are discussed. Experimental results
show that Mobile Quality of Service (M-QoS) is
enhanced for doing the job in advance transmission
within combined BSN and Internet of medical things
(IoMT). The example of such system is Telesurgery,
which is an emerging field of study and research.49
As indicated by the World Health Organization, in
developing nations, in excess of 1.3 million individuals
died in road accidents. A research study in Pakistan led
to quantify the effect of driver’s exhaustion in road
traffic crashes. Information was gathered from the
National Highways and Motorway Police (NHMP),
and measures of exhaustion-related mishaps were estimated by applying the Australian Transportation
Safety Bureau procedure. The examination comes
about to demonstrate that the normal level of
weariness-related road accidents were 28% and 10%
on Motorways and Highways, respectively. It likewise
portrays that exhaustion-related accidents prompt
deadly mishaps on Motorways (54%).50 Overall, now
the authorities are considering the counteractive actions
Figure 1. The percentage share of road crashes in Pakistan.
to prevent road accidents. In Pakistan, a large portion
of the mishaps happens because of driver’s tiredness.
We have gathered information from the Pakistan
Bureau of Statistics. Insights into of the road accidents
based on the class of road accidents and losses are
shown in Figure 1.
These incidents have driven specialists around the
globe to explore techniques for early cautioning, sluggishness identification, and cautioning. In addition,
numerous nations and government authorities are
focusing on the execution of answers for enhancing
driving security. There is no such framework yet in
Pakistan to distinguish a driver’s sleepiness. The following are the three major categories of measures to
detect the drowsiness of the driver:
1.
2.
3.
Behavioral measures
Vehicle-based measures
Physiological measures
In the first category, sleepiness is detected using different biomedical indicators, heart rate, skin color, and
electrical brain activity. These physiological features
change in the presence of fatigue and drowsiness. By
using various physiological indicators such as ECG,51
EOG,52
functional
near-infrared
spectroscopy
(FNIR),53 and electroencephalography (EEG),54 consistent results can be obtained for sleepiness detection.
Among all these indicators, EEG is considered as the
most reliable and commonly used technique. EEG is a
non-invasive technique that records the electric potential of the cortical neurons with the help of electrodes
placed on the scalp. EEG signals measure the potential
fluctuation of cortical neurons, and based on frequency
bands, the signals are classified as alpha signals (8–
13 Hz), beta signals (13–30 Hz), gamma signals (30–
50 Hz), and delta signals (1–4 Hz). We are interested in
the alpha wave band as it designates drowsiness. Some
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International Journal of Distributed Sensor Networks
Figure 2. Architectural view of smart D3S based on IoMT.
researchers combined the physiological parameters
with different kinds of sensor fusion techniques to
increase the competence of detection systems. It has
been proved that physiological techniques produce
satisfying results, but these techniques are not costeffective and also have some practical limitation of
setup complexity and non-portability of equipment.
The D3S system addresses many issues, which have
not been covered in the literature. Researchers such as
Verma et al.,20 Memon (2017),28 Juboori (2017),29 and
Ambekar et al.27 have used the machine learning classifiers and used face and eyes to detect the driver’s drowsiness. These researchers have used classical machine
learning methods for the static systems, but in case of
real-time situations, these classifiers and their related
methods will underperform. The proposed D3S system
can be used in the real-time situation and it functions
accurately.
architecture diagram and we are simulating that scenario using the NS3 WSN simulation tool. This simulation shows that the ratio of accidents can be majorly
reduced. When drowsiness of any driver is captured, a
message alert will be delivered to all other drivers of
the vehicles that are near to the sleeping driver; for this,
we are using different sensors nodes.55 Another unique
feature of the sensor network is the collaborative effort
of sensor nodes. Sensor nodes are fitted with an
onboard processor. The above-described features
ensure a wide range of applications for sensor networks. Some of the application areas are health, military, and security. For example, the physiological data
about a patient can be monitored remotely by a doctor.
We can build an Android application that could be
more useful for users as shown in Figure 2.
D3S
Smart D3S framework based on IoMT
Our system detects the drowsiness by monitoring the
eye closure state of the driver, and our system has a
level of 85% accuracy. To analyze the driver’s drowsiness, we can build an IoT-based medical application.
There are thousands of IoT-based medical applications
uploaded by developers that can assist and guide the
users of the applications. We are proposing an
The driver fatigue detection and alert system demands
precise measurements. It has two main measures. The
first one will be to coach the classifier to the facial skin
swelling, eye discovery, and assess whether the eyes are
closed or open. The discovery stages contain the selection of objective graphics and unwanted graphics. The
objective pictures, also known as favorable examples,
help with the aim of the system; the graphics that are
not useful are referred to as unwanted graphics.
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Ramzan et al.
Driver fague
detecon module
fague detecon score
Driver fague
score
High score
Image analysis
Low
score
alert
Fague acon system
Face and eyes
detecon
image
Machine classifier and analyzer
Image extracon
video stream
Video camera
monitoring driver
Figure 3. A module for Driver’s fatigue detection module.
Algorithm 1: Driver fatigue detection and alert generation
algorithm
Algorithm 2: Driver fatigue action management algorithm
Input: Video stream from the video camera, which is in front of
driver
Output: Driver fatigue score and its intensity
1. Get video stream from the video camera
2. Extract images one by one from the video
3. If face is found in the image Then check
4. If eye is detected from the face image
5.
check whether the eye is closed, then count fatigue score
6.
else no fatigue
7. end if
8. If the score is . threshold then generate alert
9. else end if
10. Store driver fatigue score and its intensity in the dataset
11. Go to step 3
Input: Driver fatigue score and its intensity
Output: Alarm generation and reporting to the traffic
management authorities
1. Get fatigue score from the driver fatigue detection module
and until the engine is ON
3. If level of fatigue is high Then
4. Generate alarm for the driver and
5. send alert message to warning management module
4. If alert count has reached a threshold Then
5.
Get the GPS coordinates
6.
Using connectivity module report to traffic management
authorities
7. end if
8. end if
11. Go to step 1
Numerous classifiers will need to get skilled independently for distinct objectives. Here aim denotes the
facial skin near the eyes and opened eyes or shut eyes.
The trained classifier is different for each specific individual, plus it might detect any individual eyes and face.
Second is the driver’s fatigue detection system. The
discovery procedure is attained by employing different
educated classifiers in the check graphics. A video camera has been directed at the drivers’ face in this procedure. A movie flow is got out of this camera; also the
video flow stipulates the evaluation graphics. A single
image will be taken in the video flow, and following the
investigation of the needed graphics, the machine will
automatically choose whether or not to build the information as shown in Algorithm 1. The decision around
the alarm is dependent on assessing the threshold value.
The conclusion of threshold depends upon the experiments. The entire procedure for driver fatigue detection
is displayed in Figure 3. Higher value of driver’s fatigue
score causes alert generation with the help of fatigue
action system module. Whereas the lower score is
ignored, and image extraction is performed to check
the fatigue value again.
The fatigue action system is intended to gauge the
crucial temperament of the driver’s drowsiness and
requires suitable actions. An alert will be sent to the
driver in the event that the degree of tiredness is significantly high as shown in Algorithm 2. The awakeness is
listed in from the device too. A particular relay of warnings activates the coverage system by means of a connectivity module into the authorities network together
with the driver’s latest GPS (latitude and longitude)
spot as exhibited in Figure 4.
The architectural version reflects the total operational procedure of this suggested system. The procedure for this suggested version starts by having a video
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International Journal of Distributed Sensor Networks
High
Driver fague detecon fague
level
module
Severity of Alert
alarm
alert
count
Connecvity module
using
Driver
Warning management
module
report
current locaon
Traffic management
authories
GPS locaon reporng
device
Figure 4. Driver’s fatigue action module.
camera that is active. The digital camera has been
mounted before the driver. The camera also carries an
online video flow that will be centered about the face
area. The driver’s fatigue detection procedure found
that the eyes and face employing the training of the
machine learning-based classifier. This technique decides the exhaustion and drowsiness of the driver. The
discovered fatigue worth is then passed into the tiredness activity technique. Cable alarm is made in case the
values are significantly less with a connectivity module
to the authorities, which notifies the cops in regard to
the tiredness of this driver together with the driver’s
present position as exhibited at Figure 5.
Drowsiness is a state of diminished awareness of
mind associated with the desire to fall asleep.
Drowsiness is the last stage before falling asleep.
Drowsiness may be due to physical work or long trips
without proper naps. Drowsiness detection system
monitors, processes, recognizes the drowsy state, and
gives results to the user; the user can then take actions
on it. Drowsiness can be detected by capturing a
sequence of images and constantly monitoring the eye
movement. When the body is in a drowsy state, many
physiological and behavioral changes occur, such as
excessive yawning, decreased eye blinking rate, and
slower heartbeat rate. It is believed that the drowsiness
state of the driver can be detected early enough to
avoid an accident. The detection system should be
designed such that it will not distract the driver, will
work in daytime and nighttime, and will give accurate
results. The objective of our proposed framework is to
provide an economical, non-intrusive, and efficient system to avoid accidents. All the processing of our detection system has been accomplished using MATLAB
image processing tool.
In this section, an overview of the system is explained
and then the proposed methodology is presented. The
video analysis
Driver’s fague detecon module
fague score
Fague acon module
connect
Connecvity module
locaon
informaon
Police reporng
module
alert
Driver alert module
alarm
Driver
Figure 5. Architectural model for driver’s fatigue management
system.
proposed detection system needs a wireless/wired webcam which is connected to a PC on which MATLAB is
installed. This first step is the acquisition of the video of
the driver and then this video is converted into the
frames. The second step is segmentation of the images.
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Ramzan et al.
Figure 6. Eye state measurements in D3S.
As we are concerned with behavioral measures, eye
state information is extracted from the images.
Detection of eyes has been completed using the Viola
and Jones algorithm using cascade object detection
classifier. To detect the drowsiness, a morphological
analysis is performed. Our system detects drowsiness by
analyzing the eye closure state of the driver. The proposed system has been shown in Figure 5. The major
stages are video acquisition, eye detection morphological analysis, and drowsiness detection as shown in
Figure 8. The main stages of the proposed framework
are demonstrated below:
objects along with its inside parts. Boundary values are
dependent upon the size of the objects.
(c) Morphological analysis
After eye extraction, the image is further morphologically processed for iris detection. First, the image is converted into the grayscale image and necessary masking
is done for morphological analysis. Then the complement of the grayscale image is taken and holes are filled
and the image is eroded using disk-shaped structuring
element. Using boundary value function iris boundary
is calculated, as shown in Figure 6.
(a) Video acquisition
(d) Drowsiness evaluation
The video acquisition is done using a camera installed
in front of the driver’s face. Video acquisition refers to
obtaining the live video of the driver. The live video
stream is necessary for the analysis of the driver’s
activeness during the driving process. The video acquisition can be good if proper lighting conditions are
present at the time of driving. An embedded system is
used to process the video acquired.
(b) Eye detection
As our main concern is to detect drowsiness using eye
state information, the eye pair is extracted from each
state using Viola and Jones Algorithm with cascade
object detector classifier in MATLAB as shown in
Figure 6. The images extracted from video stream are
converted to binary images on by one. The boundary
value function traces the exterior boundaries of objects,
as well as boundaries of holes inside these objects, in
the binary image. It also descends into the outermost
The boundary function values are used to calculate
the area of the iris. The round objects give the metric
values closer to 1. Therefore, a threshold value is set;
if the driver is in the alert state, then the value will be
approximately 1, as depicted in Figure 6. If the driver
is in a state of drowsiness and the metric value is lesser than 1, then drowsiness will be detected and an
alarm will be generated. The threshold value for the
drowsiness evaluation is 0.94 in our developed system.
The formula for drowsiness evaluation is as shown in
equation (1)
threshold\
4 3 p 3 area
perimeter2
ð1Þ
Results and discussion
The system proposed in this article has an acceptable
level of performance with an average accuracy of
84.4%. The accuracy of the system is calculated by the
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International Journal of Distributed Sensor Networks
Table 4. Results of the proposed system.
Feature
Total images
Eye pair detection
Drowsy faces truly detected
False detection
Accuracy
Eye closure
33
33
28
5
84.8%
Figure 7. Different time intervals of simulation in the wired connection among vehicles: (a) message sent, (b) acknowledgment
sent, (c) information sent, and (d) other extra information sent.
number of truly detected faces and the number of false
detections. The drowsiness detection is accomplished
by setting a threshold value when the object is in the
normal state and eyes are open during which the iris
detection values will be approximately equal to 1.
Otherwise, the values will vary from 0.2 to 0.5 according to the drowsiness level of the driver. The accuracy
of the system can be enhanced by fusing this behavioral
measures information with some sensors, that is, inertial measurement unit (IMU) sensor or pressure measuring sensor as shown in Table 4.
Real-time simulation of D3S
Wired connection among stationary IoT devices
This study uses simulation using a wire channel. Wires
are very costly and it is impossible to connect vehicles
with each other through a wire. For demonstration of
this scenario, a simulation of a vehicle connected with
wires is shown. Vehicle 1 is sending a message to
Vehicle 2 when the driver is sleeping. This can be
clearly seen in Figure 7. The label of X-axis and Y-axis
are the distances measured in xx,yy format i.e., 13.5,
0.0 m/sec, which means 13.5 meters per second in Xaxis and 0.0 meters per second speed of the vehicle.
True wireless connection among vehicles
The mobility nodes will move like vehicles. For simulation, we take 11 vehicles that can move freely in a given
range. When a driver of any vehicle is found sleeping,
an alert message will be generated and sent to all other
vehicles. Therefore, other vehicles will take any safety
precautions. In the simulation, the driver of the first
vehicle is found sleeping. Vehicle 1 will send a message
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Ramzan et al.
Figure 8. Different time intervals of simulation in True wireless connection among vehicles: (a) vehicle in position with true
wireless connection, (b) sleeping driver’s vehicle sent message to others, (c) vehicles changed their routes, and (d) vehicle
repositioned themselves.
to all other vehicles that Vehicle 1’s driver is found
sleeping during driving. Other vehicles will also get the
position of Vehicle 1, which will help out other vehicles
to take suitable action. Figure 8 shows the other
vehicles reacting once they receive a message from
Vehicle 1. The label of X-axis and Y-axis are the distances measured in xx,yy format i.e., 16.1, 0.0 m/sec,
which means 16.1 meters per second in X-axis and 0.0
meters per second speed of the vehicle.
False wireless connection among vehicles
For more clarity, we also make another simulation for
the normal case. For differentiating the case when the
driver is not sleeping, no message will be generated, so
only Vehicle 1 will move and other vehicles will not
move, as shown in Figure 9. The label of X-axis and
Y-axis are the distances measured in xx,yy format i.e.,
91.0, 0.0 m/sec, which means 91.0 meters per second in
X-axis and 0.0 meters per second speed of the vehicle.
Google Play store use case
On Google Play store, a range of applications are
found. These apps are of quite a few types, for example, matches, motion pictures, instruction and music,
and movie. The applications are of just two main types:
paid and free. In this article, we have made a Google
Play store dataset with categories of health application
using the Google-play-scraper. By employing this scraper, we scraped 550 use cases of every single type of
health application, both free and paid categories. From
every single app on Google Play store, we were able to
scrap almost 70 features, some of which used in this
article are rankings, IAPs, marketing service, and
installations. An evaluation of the dataset is performed.
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International Journal of Distributed Sensor Networks
Figure 9. Different time intervals of simulation in the false wireless connection among vehicles: (a) vehicle in position with false
wireless connection, (b) sleeping driver’s vehicle do not send message to others, (c) vehicles do not change their routes, and (d)
vehicle do not reposition themselves.
Figure 10. Sample screenshot of medical applications dataset.
15
Ramzan et al.
Using Google-Play-Scraper
Dataset of
Google Play
applicaons
Scrape Medical Applicaon
Free applicaons
Applicaons Type
Paid applicaons
Aributes Selecon
for Analysis
●
●
Analysis in R-Studio
●
●
Aributes
Rangs
InApplicaonPurcahses
Adversements
support
Installs
CIRCOS Visualizaon
Figure 11. Methodology diagram of Google Play store use
case.
Investigation of dataset is completed in RStudio.
Visualization of our analysis is shown in CIRCOS as in
Figures 10 and 11. The title in the figures represents the
name of the applications.
On Google Play store, thousands of applications
are uploaded by the developers of Android application. These applications are downloaded by the users
without any check and also without any analysis
about the application, whether the application is suitable for the user or not. The fake or malicious applications can grab the personal information from the
user and can damage the user’s trust on Google Play
store. The legitimacy of the application cannot be
measured because there is little information provided
by the developers on the front end of the application.
Even the Google Play store has no information about
the source code of the applications. In this research
work, we have visualized the IAP, percentage of
advertisement support, rating, and installations in the
representation of histograms of free and paid applications of medical category from Google Play store. We
have visualized the relationship of different attributes
in CIRCOS.
(a) Free medical offering IAP
In the IAP, a number of medical applications in Google
Play store offer credits, products, and so on; these services can be availed by a user by performing some task
or activity which is not relevant to that specific medical
application. For example, some medical applications
give coins by just buying through credit card, by filling
survey form, by seeing adds or videos, and so on. Free
applications mostly offer IAP because at the time of
installation, free applications do not charge any credit
from users of those applications. For visualizing the
percentage of the defined attributes, we used Pie3D
charts. The percentage of these medical applications
shows that that 15% free medical apps offer IAP and
85% free medical apps do not offer IAP, as shown in
Figure 12(a).
Figure 12. Different medical applications: (a) free IAP, (b) paid IAP, (c) free ads support, and (d) paid ads support.
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International Journal of Distributed Sensor Networks
Figure 13. Free medical application rating trends.
(b) Paid medical offering IAPs
Paid applications charge money at the time of installations, which is why these applications provide more
comfort and satisfaction to their users. In the paid
application, the ratio of IAP purchases is less. We can
visualize the statistical information of paid applications
and IAPs using Pie3D. It visualizes that 5% paid medical apps offer IAPs, whereas just 95% paid medical
apps did not offer IAPs, as shown in Figure 12(b).
(c) Free medical advertisements
Advertisement plays an essential role in the success of
the companies, brands, and so on. In the Google Play
store, many applications contain the advertisement of
different companies, which sometimes frustrates the
users and sometimes gives helpful information. Free
applications do not charge during installation, which is
why most of the applications offer advertisements. The
Pie3D charts visualize that 23% of free medical apps
offer advertisements and just 77% free medical apps
did not offer advertisements, as shown in Figure 12(c).
(d) Paid medical advertisements
The Pie3D chart shows that just 3% paid medical apps
offer advertisements, and 97% paid medical apps did
not offer advertisements, as shown in Figure 12(d),
because the paid applications charge at the time of
installation. Advertisements sometimes frustrate the
user of the application, but the paid application users
make sure the they are comfortable with the application. In paid applications, the advertisement ratio is
less as compared with free applications.
Figure 14. Paid medical application rating trends.
have their own experience related to that application.
On the basis of this experience, the user gives a rating
to that application. In Google Play store, the ratio of
the rating has been set from 0 to 5, where 0 indicates
that this application is very bad and 5 indicates that
this application is too good in the sense of usability.
The rating attribute is visualized in the form of a histogram that shows how people rate free medical apps.
Most of the users of the medicals applications give 4.2
ratings to free applications, which is visualized in the
histogram shown in Figure 13.
Paid medical rating value
A histogram shows how people rate the paid medical
applications (Figure 14). It can be visualized that most
people give 5 ratings to paid medical applications.
However, as compared with free medical applications,
users do not hesitate to give 0 ratings for paid medical
applications if they are unsatisfied because they buy the
apps.
Free and paid medical application installations
The number of installations of each application plays an
important role in showing the popularity of the application. Figure 15 shows some installations of free medical
applications in different categories. As we can see, the
free category has a higher number of installations when
compared with the paid category. If, in Google Play
store, we can find a category in which installations are
highest, we can guide developers to try their skills in that
category. The following diagram shows some installations of paid medical applications in different categories.
As we can see, the free category has a higher number of
installations compared with other categories.
Free medical rating value
CIRCOS visualization for free and paid medical
applications
Thousands of the users download the applications from
Google Play store for specific purposes. These users
We used CIRCOS for visualization and analysis of
attributes of free and paid applications; we have four
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Ramzan et al.
Figure 15. The number of installations for free and paid medical applications.
Figure 16. CIRCOS free medical application visualization.
attributes: ratings, IAPs, advertisement support, and
installations. CIRCOS is a tool in which multiple
dependent and independent variables could be easily
identified. The position of the object can be easily identified in the circular layout of the data. We used the
CIRCOS package for the visualization of the information. CIRCOS illustrates the link ratio between different attributes of free and paid medical Android
applications, which identifies the symmetries among
the values, as shown in Figures 16 and 17 respectively.
The plot is drawn against the number of installations
and with the rating of the applications related to free
and paid medical applications on Google Play. Some
of the applications offer IAPs and support of ads. The
representation of IAPs and the support of ads is very
limited in the figures because these two values are very
rare in the applications. In the configuration of the
Figure 17. CIRCOS paid medical application visualization.
visualization in CIRCOS, many libraries are included
for different purposes and these libraries have their
own functionalities. The percentile of the colors shows
the percentage of the color density of each observation
in statistics. The components drawn can change the
orientation, color, and scale. The color configuration
can show every type of color and its usage.
(a) Configurations
In the visualization using CIRCOS, there are always
many libraries with unique functions. The meaning of
these colors exhibits the proportion of this coloration
density of every point in the number scale. The color
components could transform the orientation and along
with settings may demonstrate every kind of coloring,
which along with every type may take advantage of
these settings.
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International Journal of Distributed Sensor Networks
(b) Percentile configuration
This library indicates the feasibility of color usage that
is just a step employed in numbers suggesting the significance under a certain proportion.
(c) Components scaling configuration
In this section, we can correct the components’ scale,
shade, and orientation. Finding the ideogram design
correctly is essential. Generating all elements seem like
precisely the same size may highlight the contrast
between comparative components in the CIRCOS
graph.
(d) Colors configuration
integral part of our lives, more so than the presentday personal computers.
In the future, the system will be combined with physiological measures or with vehicular measures to yield
more accurate results. The warning system can also be
improved in such a way that when the system detects
that the driver is drowsy, it should either stop the car
or slow down the car to avoid accidents. To analyze the
driver’s drowsiness, we can build an IoT-based medical
application. There are thousands of IoT-based medical
applications uploaded by developers that can assist and
guide the users of the applications. We can build an
Android application that could be more useful for
users. For this, we should have an understanding of
which category of IoT-based medical application is
suitable for this research. The delay factor will be added
in the network simulation so that a realistic scenario
can be emulated.
Even the \colors. block comprises all of the coloring
definitions. CIRCOS has a substantial number of
default coloring definitions (pure colors and colors,
Brewer palettes, UCSC chromosome color schemes,
luminance-normalized colors, and so forth). You
always need to incorporate them on your settings in the
‘‘.conf’’ files, which is available to you with the name of
‘‘CIRCOS.conf.’’ Importing this document can specify
fonts, colors, and designs, utilizing the proper blockstructure.
The author(s) declared no potential conflicts of interest with
respect to the research, authorship, and/or publication of this
article.
Conclusion and future work
ORCID iDs
In the research study, a driver’s drowsiness detection
system is developed, which can help to avoid or reduce
the number of accidents in Pakistan. D3S is efficient
and cost-effective, and requires a high-quality webcam
for capturing the drowsiness of the driver and a processing system. The testing of D3S is performed using the
MATLAB environment. The system measures the drowsiness based on eye closure state of the driver and gives
an accuracy of about 85%. When drowsiness of any
driver is captured, a message alert will be delivered to all
other vehicle drivers in the vicinity of the sleepy driver;
for this, different sensor nodes are used. Another unique
feature of the sensor network is the cooperative effort of
sensor nodes. Sensor nodes are fitted with an onboard
processor. Some of the application areas are health, military, and security. For example, the physiological data
about a patient can be monitored remotely by a doctor.
So, for measurement and analysis, we build a Google
Play store dataset with all free and paid applications of
the medical category by using the Google-play-scraper.
By using this scraper, we scraped at least 550 applications of each category of medical applications in free
and paid categories. On each application on Google
Play store, we scraped almost 70 attributes, but we
used only four attributes for our analysis. We envision
that, in the future, wireless sensor networks will be an
Hamza Aldabbas
https://orcid.org/0000-0002-4141-0079
Muhammad Farhan
https://orcid.org/0000-0002-36495717
Declaration of conflicting interests
Funding
The author(s) received no financial support for the research,
authorship, and/or publication of this article.
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