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Abstract

In this study, a multisensory physiological measurement system was built with wireless transmission technology, using a DSPIC30F4011 as the master control center and equipped with physiological signal acquisition modules such as an electrocardiogram module, blood pressure module, blood oxygen concentration module, and respiratory rate module. The physiological data were transmitted wirelessly to Android-based mobile applications via the TCP/IP or Bluetooth seri-al ports of Wi-Fi. The Android applications displayed the acquired physiological signals in real time and performed a preliminary abnormity diagnosis based on the measured physiological data and built-in index diagnostic data provided by doc-tors, such as blood oxygen concentration, systolic pressure, and diastolic pressure. In addition, in this study, the R waves, which are the highest peaks of the PQRST waves in electrocardiograms, were analyzed and detected using the heart rate variability (HRV) for time-frequency analysis and calculation of the RR interval time series. In this study, the heart rate data in the same age group were collected, and the optimal value of the standard deviation of normal to normal (SDNN) during the time domain of a normal heartbeat was found using the particle swarm optimization (PSO) algorithm and set as the risk level of the SDNN time domain analysis. A spectral analysis on the activity of the autonomic nervous system (ANS) was performed and the preliminary analysis results were displayed on the Android handheld devices for comprehensive physiological data analysis and HRV time-frequency analysis. Healthcare needs are distributed at all levels, therefore user-friendly software interfaces have been written to meet the health-care needs at all ages.
Using Mobile Technology to Construct a Network Medical Health Care System
Sung-Jung Hsiao
1
and Wen-Tsai Sung
2
,*
1
Department of Information Technology, Takming University of Science and Technology, Taipei City, 11451, Taiwan
2
Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung, 411030, Taiwan
*Corresponding Author: Wen-Tsai Sung. Email: songchen@ncut.edu.tw
Received: 19 May 2021; Accepted: 20 June 2021
Abstract: In this study, a multisensory physiological measurement system was
built with wireless transmission technology, using a DSPIC30F4011 as the master
control center and equipped with physiological signal acquisition modules such as
an electrocardiogram module, blood pressure module, blood oxygen concentra-
tion module, and respiratory rate module. The physiological data were transmitted
wirelessly to Android-based mobile applications via the TCP/IP or Bluetooth seri-
al ports of Wi-Fi. The Android applications displayed the acquired physiological
signals in real time and performed a preliminary abnormity diagnosis based on the
measured physiological data and built-in index diagnostic data provided by doc-
tors, such as blood oxygen concentration, systolic pressure, and diastolic pressure.
In addition, in this study, the R waves, which are the highest peaks of the PQRST
waves in electrocardiograms, were analyzed and detected using the heart rate
variability (HRV) for time-frequency analysis and calculation of the RR interval
time series. In this study, the heart rate data in the same age group were collected,
and the optimal value of the standard deviation of normal to normal (SDNN) dur-
ing the time domain of a normal heartbeat was found using the particle swarm
optimization (PSO) algorithm and set as the risk level of the SDNN time domain
analysis. A spectral analysis on the activity of the autonomic nervous system
(ANS) was performed and the preliminary analysis results were displayed on
the Android handheld devices for comprehensive physiological data analysis
and HRV time-frequency analysis. Healthcare needs are distributed at all levels,
therefore user-friendly software interfaces have been written to meet the health-
care needs at all ages.
Keywords: Internet of things (IoT); particle swarm optimization (PSO); heart rate
variability (HRV); android; wireless sensing network; sensor
1 Introduction
In recent years, technology has developed rapidly. The robotics promoted by articial intelligence (AI)
research and the development and Germanys Industry 4.0 driven by IoT have made technology gradually
become the center of global economic growth. In such a highly competitive industrial situation, because
enterprises are under pressures such as the demand for product innovation, the efciency of solving
This work is licensed under a Creative Commons Attribution 4.0 International License, which
permits unrestricted use, distribution, and reproduction in any medium, provided the original
work is properly cited.
Intelligent Automation & Soft Computing
DOI:10.32604/iasc.2022.020332
Article
ech
T
PressScience
product problems, and customer returns after product use, it is more likely to create work stress and overwork
for employees. Hence, in this study, a portable networking healthcare system was designed for healthcare-
related employees that could allow users to carry out regular health examinations and observe changes in
their physiological data.
According to the Bureau of Labor Insurance of Taiwan, from 2010 to 2014, there were 358 overwork-
related cases veried by the Bureau of Labor Insurance, among which 156 people died. On average, one
person got sick from overworking every ve days, and one person died from overworking every 12 days,
as shown in Fig. 1. The people most at risk are employees with cardiovascular and cerebrovascular
diseases due to stress arising from the abuse of job responsibility systems in the technology industry,
security industry, intermediary industry, and medical industry [1,2].
2 Literature Review
2.1 Internet of Thing
The concept of IoT originated from the book The Road Ahead by Bill Gates in 1995. The technology
was not taken seriously at the time, however, because it was still limited by the development of equipment
and wireless networks. The information mentioned by Bill Gates in The Road Ahead has been veried
repeatedly. The book deeply analyzes the integration of information technology and business strategy in
future business and elaborates how to have predictive enterprises [3,4].
The Internet connects many subsystems, such as sensor systems, radio frequency identication (RFID)
systems [5], wireless communication systems, and global positioning systems, to build an internetwork with
the functions of sensing, communication, and monitoring. Machine to machine (M2M) communication is
established to transmit the sensed physical quantity to the management or monitoring system in the form
of data and texts for further analysis and processing [6,7]. As dened by the European
Telecommunications Standards Institute (ETSI) [8], according to different working procedures and
contents, IoT can be further subdivided into the layers: the perception layer, the network layer, and the
application layer. The IoT architecture is shown in Fig. 2. In addition, the gateway connecting the
network layer is composed of a Bluetooth module, a Wi-Fi module, and a passive RFID label.
A communications chip is the channel connecting the perception layer and the network layer and is used
to transmit the information obtained by the sensing devices to the network layer for the next stage of IoT [9].
Figure 1: Statistical chart of overworked persons
730 IASC, 2022, vol.31, no.2
Through the above two technologies of sensing and networking, human beings can enjoy applications
related to any object of interest around them at any time and in any place by any network access method. IoT
has been applied in elds such as smart rst aid kits for medical care [10,11], management and environmental
monitoring for power systems [12], smart learning services for educational resources [13], and smart green
buildings for construction projects [14]. Its application range covers food, clothing, residences,
transportation, education, and entertainment, as shown in Fig. 3. The application layer also includes an
application support layer, which mainly acts as a data and information analysis and processing platform
to connect various networks and provide application services. The application support layer can be
divided into three types: a cloud computing platform, a service support platform, and an information
disclosure platform [1517].
2.2 Amazon EC2
Amazon Elastic Compute Cloud (Amazon EC2), with its architecture based on Amazon Web Services
(AWS), is a cloud computing platform created by Amazon that provides numerous remote web services.
Figure 2: Proposed IoT system architecture
Figure 3: IoT architecture using mobile phones as the smart objects of IoT
IASC, 2022, vol.31, no.2 731
The Amazon S3 architecture is also on this platform. The architecture of the Amazon EC2 and AWS service
integration is shown in Fig. 4.
AWS provides various computing and web services that allow users to congure a virtual host server, set
arewall, congure network access, cut, and get exclusive routing IP addresses, and users can exibly
expand and increase (for example, network trafc, hard disk capacity, and CPU processing speed)
according to their own needs at any time [1820].
3 Medical Electronics
Aging and rapid increases in medical expenses have driven the growth of medical electronics. According
to the estimation by Evaluate Pharma, the medical device market grew to about US$ 780 billion in 2016,
mainly because of the increase in demand for chronic disease detection and monitoring. In 2016, the
United States Congress announced approval of the 21st Century Cure Act, which is a precision medical
policy. Driven by the development of medical big data analysis platforms and telemedicine, as well as
portable mobile medical development, it has grown at a compound annual rate of 6.2% and is expected to
grow to US$995 billion by 2020. The details are shown in Fig. 5.
Figure 4: AWS service integration architecture
732 IASC, 2022, vol.31, no.2
3.1 Electrocardiograms (ECG)
By placing electrodes on specic parts of the body and using the non-combination of electrodes, a
voltage record from 12 different angles can be produced, which is called the ECG lead. A 12-lead
electrocardiogram is generally used, which includes three standard leads, three augmented leads, and six
chest leads. The standard leads and augmented leads represent the frontal view of the heart, and the chest
leads represent the horizontal view of the heart [21,22].
3.1.1 Standard Leads
A standard lead, also known as a bipolar lead, contains two electrodes (a negative electrode and a
positive electrode). The records are comprised of Lead I, Lead II, and Lead III, and the potential
differences between each two limbs are recorded, as shown in Eqs. (1)(4). It is also known as
Einthovens Triangle, and the bodys bipolar leads are shown in Figs. 6a6c [23,24].
Lead I ¼RA ðÞ to LA ðþÞ ðright arm left armÞ(1)
Lead II ¼RA ðÞ to RA ðþÞ ðright arm left legÞ(2)
Lead III ¼LA ðÞ to LL ðþÞ ðleft arm left legÞ(3)
Lead II ¼Lead I þLead III (4)
3.1.2 Augmented Leads
Augmented leads continue to use the electrodes of the standard leads (left hand, right hand and left foot)
but use different combinations to produce potential differences. An augmented lead is known as a unipolar
lead, as only one electrode (left hand, right hand, and left foot) is used to record voltages [25,26]. The human
Figure 5: Market growth estimation of major life science products in 2020
Figure 6: (a) Lead I; (b) Lead II; (c) Lead III
IASC, 2022, vol.31, no.2 733
lead diagram of the augmented leads is shown in Figs. 7a7c, and the equations are shown in Eq. (5)(7).
Lead aVR ¼RAðþÞ to ½RA &LL ðÞ (5)
Lead aVL ¼LA ðþÞ to ½RA &LL ðÞ (6)
Lead aVF ¼LL ðþÞ to ½RA &LA ðÞ (7)
3.2 Blood Oxygen Concentration
Red and infrared light emitting diodes and a light sensing transistor are put above and below a nger,
respectively. As shown in Fig. 8a, the light emitting diode is above the nger and the photosensitive transistor
is below, to receive the intensity of the light passing through the nger. The red and infrared light emitting
diodes are packaged in the same sensor and put below a nger, as shown in Fig. 8b, and a photosensitive
transistor is used to sense the intensity of the red and infrared light reected from the blood vessel.
4 Particle Swarm Optimization (PSO)
Particle swarm optimization (PSO) is a well-known swarm intelligence algorithm used to solve and
optimize problems. PSO was proposed by J. Kennedy and R. C. Eberhart who, inspired by the foraging
behavior of birds, based their study on bird ocks, sh schools, and human social systems to prove that
the information exchange among individuals in a group facilitates the evolution of an entire group, which
is the source of the core concept of the PSO algorithm [27]. The PSO ow chart is shown in Fig. 9.
The velocities and locations of all particles are updated using Eqs. (8) and (9).
Figure 7: (a) Augmented right-hand lead; (b) Augmented left hand lead; (c) Augmented left foot lead
Figure 8: Blood oxygen measurement method (a) Penetrative (b) Reecting
734 IASC, 2022, vol.31, no.2
Assume that a group comprised of Mparticles travels at a certain speed in a D-dimensional space; the
state of particle iat time twill be set as below:
Location: xt
i¼ðxt
i1;xt
i2;...;xt
idÞT
xt
id Ld;Ud;Ld;Udare the upper and lower limits of the search space, respectively
Velocity: vt
i¼ðvt
i1;vt
i2;...;vt
idÞT
vt
id vmin;d;vmax;d;vmin;vmax , are the minimum and maximum velocities, respectively;
Particle best location: pt
i¼ðpt
i1;pt
i2;...;pt
iDÞT;
Group best location: pt
g¼ðpt
g1;pt
g2;...;pt
gDÞT;
were, 1¼
,d¼
,D;1¼
,i¼
,M
The location of the particle at time t+1
vtþ1
id ¼xvt
id þc1r1ðpt
id xt
idÞþc2r2ðpt
gd xt
idÞ(8)
xtþ1
id ¼xt
id þvtþ1
id (9)
vt
id: Velocity of particle iat time t
xt
id: Location of particle iat time t
ω: Inertia weight
c
1
,c
2
: Learning factor
r
1
,r
2
: Random number
p
id
: The best location of particle iso far
p
gd
: The best locations of all particles so far
In the updated particle Eq. (10), the inertia weight and learning factor must be set. In this study, the
inertia weight ωwas set by referring to Wang et al. [28] After repeated experiments, the weight setting
method linearly decreasing from 0.9 to 0.4 was proposed in 2000. The linear decrease is shown in Eq. (10).
x¼xstart xstart xend
tmax
t(10)
Figure 9: PSO ow chart
Eberhart et al. (1999) analyzed limb tremors using the neural network of the PSO algorithm and
diagnosed normal tremors and Parkinsons disease [29,30]. The results show that the PSO algorithm can
accurately forecast the performance and effectively improve the accuracy of optimization [31,32].
5 Research Method and Analysis
The Fig. 10 shows the block diagram of the system completed in this study.
IASC, 2022, vol.31, no.2 735
5.1 Electrocardiogram Module
In this study, the main equipment included a physiological signal acquisition module, a wireless
transmission module, data display equipment, back-end data storage, and operational analysis, as shown
in Fig. 11. The development board used in this study was HUIOT-Basic, a product researched and
developed by HUAYU, as shown in Fig. 12. The electrocardiogram module used in this study is shown
in the Fig. 13.
Three leads were used as the inductions in this study, respectively labelled Lead I, Lead II, and Lead III,
mainly because the small module reduced the electrode patches and time at measurement. In addition, the
high-accuracy and high-reliability computation provided electrocardiogram data in real time. The block
diagram of the electrocardiogram module circuit ow is shown in Fig. 14.
Figure 10: Development block diagram of this study
Figure 11: System architecture
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5.2 Blood Pressure Module
The blood pressure module used in this study, shown in Fig. 15, measured blood pressure by the
resonance method, which is the most commonly-used noninvasive blood pressure measurement method.
The advantages of this method were its simplicity and convenience. The block diagram of the blood
pressure module circuit ow is shown in Fig. 16.
5.3 Respiratory Rate Module
The respiratory rate module used in this study operated based on the variation of the impedance
pneumography, as shown in Fig. 17. For air can be considered as a direct current insulator; the human
Figure 12: Development board HUIOT-Basic (Model: dsPIC30F4011-1-Mainboard)
Figure 13: Electrocardiogram module (Model: dsPIC30F4011-2-Electrocardiogram)
Figure 14: Block diagram of the electrocardiogram circuit ow
IASC, 2022, vol.31, no.2 737
lungs are like variable capacitors, and the capacitance increases when inhaling and decreases when exhaling.
The capacitance changes with inhalation and exhalation, and the tissue outside the chest is considered as a
resistance. The block diagram of the respiratory rate module circuit ow is shown in Fig. 18.
5.4 Blood Oxygen Concentration Module
The blood oxygen concentration module used in this study, as shown in Fig. 19, performed
measurements by the penetrative method. The block diagram of the blood oxygen concentration module
circuit ow is shown in Fig. 20.
Figure 15: Blood pressure module (Model: dsPIC30F4011-3-Blood pressure)
Figure 16: Block diagram of the blood pressure circuit ow
Figure 17: Respiratory rate module (Model: dsPIC30F4011-4-Respiratory rate)
738 IASC, 2022, vol.31, no.2
6 Experimental Simulation and Analysis
This study included a physiological data sensing module, a cloud server, and a smartphone application.
The physiological sensing module used HUIOT-Basic centered on dsPIC30F4011 as the master control and
Figure 18: Block diagram of the respiratory rate circuit ow
Figure 19: Blood oxygen concentration module (Model: dsPIC30F4011-5-Blood oxygen concentration)
Figure 20: Block diagram of the blood oxygen concentration circuit ow
IASC, 2022, vol.31, no.2 739
was equipped with four different physiological data sensing modules, including a heart rate module, a blood
pressure module, a respiratory rate module, and a blood oxygen concentration module, which were used to
acquire human data in a wired way and then achieve wireless physiological data transmission through Wi-Fi
and Bluetooth. The transmission control protocol architecture is shown in Fig. 21.
In this study, an Android Studio handheld device was mainly used to display the physiological data for
measurement and analysis. The HUIOT-Basic circuit board of HUAYU was used as the master control device
and was equipped with a physiological sensing module for physiological data acquisition. In order to clearly
observe changes in the R convex waves of the electrocardiogram, Lead II was used for the electrocardiogram
measurement to compute the changes in the R-R interval over a ve-minute measurement period and to
conduct the time frequency analysis of HRV theory. Fig. 22 shows the actual heart rate measurement. The
experiment proved that the pressure amplitude in the tourniquet was the maximum when the pressure in
the tourniquet was equal to the mean pressure. The Fig. 23 shows the actual blood pressure measurement.
Figure 21: Transmission control protocol architecture
Figure 22: Actual heart rate measurement
740 IASC, 2022, vol.31, no.2
In this study, a blood oxygen nger sensor was worn on the nger of the subject to acquire the blood
oxygen concentration. There is the red and infrared light with different wavelengths in the blood oxygen
nger sensor. The blood oxygen concentration module was mainly used to further compute the accuracy
of the measured values and get accurate blood oxygen concentration values. The Fig. 24 shows the actual
blood oxygen concentration measurement.
7 Heart Rate Analysis of HRV Theory
In this experiment, the HRV was obtained based on the heart rate measured by the electrocardiogram,
and programs were used to obtain the HRV of the measured R waves of the electrocardiogram within the
measured time series. The HRV ow chart is shown in Fig. 25. The R-R interval of the heartbeat actually
observed by a mobile phone is shown in Fig. 26.
7.1 HRV Value Time Domain Analysis
The HRV standard deviation was mainly computed as part of the electrocardiogram time domain
analysis. In this study, the main parameters of the index analysis were the SDNN analysis values, and the
index values were analyzed and computed by using an algorithm, using the reference Eqs. shown in
(11)(14). The algorithm calculation was performed on the heart rate variability acquired by the
electrocardiogram during a short period of time using the smartphone application [33].
Figure 23: Actual blood pressure measurement
Figure 24: Actual blood oxygen concentration measurement
IASC, 2022, vol.31, no.2 741
Total heart beats computed by the program:
THB ðTotal heart beatsÞ¼N(11)
Time series of mean R-R interval: Mean
MeanRR ¼1
NX
N
n¼1
RRiðnÞ(12)
Standard deviation of NN intervals: SDNN
In this study, 200 items of physiological data from each age group were obtained using the Physio Net
database, as shown in Fig. 27, in which the green bar represents the range of human values. The SDNN
relation equation and PSO algorithm were used to nd the optimal SDNN value of the physiological data
in HRV, which changes with age [34,35]. The X-axis represents age and the Y-axis represents the range
of SDNN analysis data. The optimal value of the physiological data was used to develop the index
diagram of the physiological data risk levels, as shown in Fig. 28.
7.2 HRV Value Frequency Domain Analysis
The HRV frequency domain analysis was mainly computed using discrete Fourier transform. The HRV
frequency domain conversion is shown in Eqs. (13) and (15). The ECG signal is γ(t), and its autocorrelation
function is γ(t):
gðtÞ¼ X
s¼1
s¼1
xðtÞxðtþsÞds(13)
Figure 26: R-R interval of an electrocardiogram measured by a mobile phone
Figure 25: HRV ow chart
742 IASC, 2022, vol.31, no.2
Its spectral power is W(f):
WðfÞ¼ X
t¼1
t¼1
cðtÞei2pftds(14)
7.3 Comparison of Various Measurements
In this study, the blood pressure sensing module and heart rate sensing module in the physiological
sensing module platform were compared with the sphygmomanometer (model BC30) developed by
Beurer, a German company, as shown in Fig. 29. The mean absolute percentage error (MAPE) was used
to determine the error rate of the system, as shown in Eq. (16).
MAPE ¼100
nX
n
t¼1
AB
C
(15)
A: Data measured by commercially available instrument
B: Data measured by the module of this study
C: Data measured by commercially available instruments
Figure 27: Optimal values of the relationship between SDNN autonomic nerve values and age groups
Figure 28: Index diagram of SDNN autonomic nerve values and risk levels of age groups
IASC, 2022, vol.31, no.2 743
The Fig. 30 shows the comparison between this research module and the commercially available blood
pressure meter to measure heart rate. The Fig. 31 shows the average absolute error rate of the heart rate. The
Fig. 32 shows the comparison between this research module and the commercially available blood pressure
meter to measure systolic blood pressure. The Fig. 33 shows the mean absolute error rate of systolic blood
pressure. The Fig. 34 shows the comparison between this research module and the commercially available
blood pressure meter to measure diastolic blood pressure. The Fig. 35 shows the mean absolute error rate of
diastolic blood pressure [36].
Figure 29: Comparison in measurement between the module of this study and the commercially available
sphygmomanometer
Figure 30: Heart rate comparison
Figure 31: Heart rate mean absolute percentage error
744 IASC, 2022, vol.31, no.2
Figure 32: Systolic pressure comparison
Figure 34: Diastolic pressure comparison
Figure 33: Systolic pressure means absolute percentage error
Figure 35: Diastolic pressure means absolute percentage error
IASC, 2022, vol.31, no.2 745
8 Conclusion
According to the statistical analysis, the total number of sudden cardiac deaths is increasing every year.
On average, three people have a heart attack every minute and die within an hour, and the success rate of
rescue is less than 10%. In fact, it is because most young people think that they are in good health and do
not immediately seek medical treatment if they feel uncomfortable, however such bad habits are the main
cause of sudden death.
In the networked physiological measurement system of this study, an Android mobile phone application
was used as the main physiological value display device, human physiological signals were acquired by a
data acquisition module, the values were wirelessly transmitted to the handheld application via Wi-Fi and
a Bluetooth module for display, and the handheld device application preliminarily diagnosed the values to
advise the users. The handheld application analyzed the risk level of the indicative physiological value
determination by doctors and used HRV for in-depth time-frequency analysis of the heart rate values. It
could make many adjustments according to the usersphysiological conditions, use the PSO algorithm to
seek the optimal value of the SDNN time domain analysis according to age, develop a range of risk
levels, and store physiological values using the Amazon EC2 platform to provide a reference for the
changes in measured values. The proposed system was built to prevent humans from experiencing
excessive stress due to overworking and to prevent users from developing cardiovascular and
cerebrovascular diseases. The unexpected karoshi is usually caused by the carelessly slow accumulation
of body loads.
Acknowledgement: This research was supported by the Department of Electrical Engineering, National
Chin-Yi University of Technology. The authors would like to thank the National Chin-Yi University of
Technology, Takming University of Science and Technology, Taiwan, for nancially supporting this
research. We thank the Amazon EC2 virtual machine cloud technology.
Funding Statement: The authors received no specic funding for this study.
Conicts of Interest: The authors declare that they have no conicts of interest to report regarding the
present study.
Availability of Data and Materials: Data sharing not applicable to this article as no datasets were generated
or analyzed during the current study.
References
[1] T. Lassila, L. Y. D. Marco, M. Mitolo, V. Iaia, G. Levedianos et al.,Screening for cognitive impairment by
model-assisted cerebral blood ow estimation,IEEE Transactions on Biomedical Engineering, vol. 65, no. 7,
pp. 16541661, 2018.
[2] F. K. McConnell and S. Payne, The dual role of cerebral autoregulation and collateral ow in the circle of willis
after major vessel occlusion,IEEE Transactions on Biomedical Engineering, vol. 64, no. 8, pp. 17931802,
2017.
[3] Q. Duan, J. Zeng, K. Chakrabarty and G. Dispoto, Accurate predictions of process-execution time and process
status based on support-vector regression for enterprise information systems,IEEE Transactions on Computer-
Aided Design of Integrated Circuits and Systems, vol. 34, no. 3, pp. 354366, 2015.
[4] D. Sobnath, T. Kaduk, I. U. Rehman and O. Isiaq, Feature selection for UK disabled studentsengagement post
higher education: A machine learning approach for a predictive employment model,IEEE Access, vol. 8, pp.
159530159541, 2020.
[5] J. Zhang, X. Wang, Z. Yu, Y. Lyu and S. Mao, Robust RFID based 6-doF localization for unmanned aerial
vehicles,IEEE Access, vol. 7, pp. 7734877361, 2019.
746 IASC, 2022, vol.31, no.2
[6] C. Di, B. Zhang, Q. Liang, S. Li and Y. Guo, Learning automata-based access class barring scheme for massive
random access in machine-to-machine communications,IEEE Internet of Things Journal, vol. 6, no. 4, pp.
60076017, 2018.
[7] M. A. G. Ghasri, A. M. A. Hemmatyar, S. Bayat and M. Mahdieh, Novel relay selection algorithms for machine-
to-machine communications with static RF interfaces setting,IEEE Access, vol. 8, pp. 189989190008, 2020.
[8] I. Soto, O. Amador, M. Urueña and M. Calderon, Strengths and weaknesses of the ETSI adaptive DCC
algorithm: A proposal for improvement,IEEE Communications Letters, vol. 23, no. 5, pp. 802805, 2019.
[9] M. Zhang, A. Raghunathan and N. K. Jha, Trustworthiness of medical devices and body area networks,
Proceedings of the IEEE, vol. 102, no. 8, pp. 11741188, 2014.
[10] G. Xu, Iot-assisted ECG monitoring framework with secure data transmission for health care applications,IEEE
Access, vol. 8, pp. 7458674594, 2020.
[11] P. P. Ray, N. Thapa and D. Dash, Implementation and performance analysis of interoperable and heterogeneous
IoT-edge gateway for pervasive wellness care,IEEE Transactions on Consumer Electronics, vol. 65, no. 4, pp.
464473, 2019.
[12] G. Bedi, G. K. Venayagamoorthy, R. Singh, R. R. Brooks and K. C. Wang, Review of internet of things (IoT) in
electric power and energy systems,IEEE Internet of Things Journal, vol. 5, no. 2, pp. 847870, 2018.
[13] M. Mohammadi, A. A. Fuqaha, M. Guizani and J. S. Oh, Semisupervised deep reinforcement learning in support
of IoT and smart city services,IEEE Internet of Things Journal, vol. 5, no. 2, pp. 624635, 2017.
[14] L. P. Fraile, S. Tsampas, G. Mylonas and D. Amaxilatis, A comparative study of loRa and IEEE 802.15.4-based
IoT deployments inside school buildings,IEEE Access, vol. 8, pp. 160957160981, 2020.
[15] H. E. Sayed, S. Sankar, M. Prasad, D. Puthal, A. Gupta et al.,Edge of things: The big picture on the integration of
edge, IoT and the cloud in a distributed computing environment,IEEE Access, vol. 6, pp. 17061717, 2017.
[16] S. Mubeen, S. A. Asadollah, A. V. Papadopoulos, M. Ashjaei, H. P. Breivold et al.,Management of service level
agreements for cloud services in IoT: A systematic mapping study,IEEE Access, vol. 6, pp. 3018430207, 2017.
[17] B. Xu, L. D. Xu, H. Cai, C. Xie, J. Hu et al.,Ubiquitous data accessing method in IoT-based information system
for emergency medical services,IEEE Transactions on Industrial Informatics, vol. 10, no. 2, pp. 15781586,
2014.
[18] I. Bermudez, S. Traverso, M. Munafò and M. Mellia, A distributed architecture for the monitoring of clouds and
CDNs: Applications to Amazon AWS,IEEE Transactions on Network and Service Management, vol. 11, no. 4,
pp. 516529, 2014.
[19] H. Puripunpinyo and M. H. Samadzadeh, Design, prototype implementation, and comparison of scalable web-
push architectures on Amazon web services using the actor model,in 2017 25th Int. Conf. on Systems
Engineering (ICSEng), 2017.
[20] K. Swedha and T. Dubey, Analysis of web authentication methods using Amazon web services,in 2018 9th Int.
Conf. on Computing, Communication and Networking Technologies (ICCCNT), India, 2018.
[21] T. Yaqoob, H. Abbas and M. Atiquzzaman, Security vulnerabilities, attacks, countermeasures, and regulations of
networked medical devicesa review,IEEE Communications Surveys & Tutorials, vol. 21, no. 4, pp. 3723
3768, 2019.
[22] Y. Xie, X. Li, S. Zhang and Y. Li, iCLAS: An improved certicateless aggregate signature scheme for healthcare
wireless sensor networks,IEEE Access, vol. 7, pp. 1517015182, 2019.
[23] A. Argha, J. Wu, S. W. Su and B. G. Celler, Blood pressure estimation from beat-by-beat time-domain features of
oscillometric waveforms using deep-neural-network classication models,IEEE Access, vol. 7, pp. 113427
113439, 2019.
[24] F. Gao, Q. Peng, X. Feng, B. Gao and Y. Zheng, Single-wavelength blood oxygen saturation sensing with
combined optical absorption and scattering,IEEE Sensors Journal, vol. 16, no. 7, pp. 19431948, 2016.
[25] U. Asgher, R. Ahmad, N. Naseer, Y. Ayaz, M. J. Khan et al.,Assessment and classication of mental workload in
the prefrontal cortex (PFC) using xed-value modied beer-Lambert law,IEEE Access, vol. 7, pp. 143250
143262, 2019.
IASC, 2022, vol.31, no.2 747
[26] T. Muhammed, R. Mehmood, A. Albeshri and I. Katib, Ubehealth: A personalized ubiquitous cloud and edge-
enabled networked healthcare system for smart cities,IEEE Access, vol. 6, pp. 3225832285, 2018.
[27] H. Jin, Y. Luo, P. Li and J. Mathew, A review of secure and privacy-preserving medical data sharing,IEEE
Access, vol. 7, pp. 6165661669, 2019.
[28] Z. Wang, T. Zhang, L. Kong and G. Cui, Prediction-based PSO algorithm for MIMO radar antenna deployment
in dynamic environment,The Journal of Engineering, vol. 2019, no. 20, pp. 66466650, 2019.
[29] Y. Shi and R. C. Eberhart, A modied particle swarm optimizer,in 1998 in IEEE Int. Conf. on Evolutionary
Computation Proc. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360), USA, 1998.
[30] Y. Shi and R. C. Eberhart, Empirical study of particle swarm optimization,in Proceedings of the 1999 Congress
on Evolutionary Computation-CEC99 (Cat. No. 99TH8406), USA, vol. 3, pp. 19451950, 2002.
[31] H. M. Hasanien, Particle swarm design optimization of transverse ux linear motor for weight reduction and
improvement of thrust force,IEEE Transactions on Industrial Electronics, vol. 58, no. 9, pp. 40484056, 2011.
[32] L. Tong, X. Li, J. Hu and L. Ren, A PSO optimization scale-transformation stochastic-resonance algorithm with
stability mutation operator,IEEE Access, vol. 6, pp. 11671176, 2017.
[33] H. Jiang, Z. He, G. Ye and H. Zhang, Network intrusion detection based on PSO-xgboost model,IEEE Access,
vol. 8, pp. 5839258401, 2020.
[34] J. Indumathi, A. Shankar, M. R. Ghalib, J. Gitanjali, Q. Hua et al.,Block chain based internet of medical things
for uninterrupted, ubiquitous, user-friendly, unappable, unblemished, unlimited health care services (BC IoMT
U6 HCS),IEEE Access, vol. 8, pp. 216856216872, 2020.
[35] J. G. García, C. T. Orriols, F. E. Romero and E. B. Delgado, Construction of empirical care pathways process
models from multiple real-world datasets,IEEE Journal of Biomedical and Health Informatics, vol. 24, no.
9, pp. 26712680, 2020.
[36] A. F. Klaib and M. S. Nuser, Evaluating EHR and health care in Jordan according to the international health
metrics network (HMN) framework and standards: A case study of hakeem,IEEE Access, vol. 7, pp. 51457
51465, 2019.
748 IASC, 2022, vol.31, no.2
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