Framework for propagating stress control message using heartbeat based iot remote monitoring analytics

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 10, No. 5, October 2020, pp. 4615~4622
ISSN: 2088-8708, DOI: 10.11591/ijece.v10i5.pp4615-4622  4615
Journal homepage: http://ijece.iaescore.com/index.php/IJECE
Framework for propagating stress control message using
heartbeat based iot remote monitoring analytics
Eisha Akanksha
Department of Electronics and Communication, CMR Institute of Technology, India
Article Info ABSTRACT
Article history:
Received Jul 26, 2019
Revised Feb 29, 2020
Accepted Mar 18, 2020
Abnormal level of stress is the root indicator factor to have significant impact
over the health of heart and there is a close relationship between the stress
levels with heart rate. Review of the existing literature showcase that there
has been various work that has been carried out towards investigation of
considering heart rate with an internet-of-things (IoT) system. Apart from
this, existing system doesnt offer any instantaneous solution where certain
intimation is offered in real-time to the user with wearables as a solution to
control the stress condition. Therefore, the current paper introduces a novel
framework where the sampled heart rates of the patients are captured by IoT
deivices. The aggregated data are further forwarded to the cloud analytic
system that uses correlation to extract the appropriate message. The system
after being applied with teh machine learning approach could further extract
the elite outcome followed by forwarding the contextual data to teh user.
Using an analytical modelliig, the proposed system shows that it offers better
accuracy and reduced processing time when compared with other machine
learning approach and thereby it proves to be cost effective solution in IoT
system over medical case study.
Keywords:
Heartbeat
Internet-of-things
Machine learning
Medical IoT
Remote monitoring
Copyright © 2020Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Eisha Akanksha,
Department of Electronics and Communication,
CMR Institute of Technology,
Bengaluru, India.
Email: eisha.a@cmrit.ac.in
1. INTRODUCTION
The internet-of-things (IoT) is the next upcoming technology that enables inter-connectivity of
multiple number of devices (or smart devices) where majority of the devices are basically sensors [1, 2].
One of the significant capabilities of the IoT is to perform sensing of the information remotely with an aid of
using multiple forms of infrastructure of the network [3]. IoT thereby allows integration of various devices
that are directly connected to the physical world and it also claims of offer better accuracy as well as cost
effective measures [4, 5]. Once various embedded systems are connected with the IoT, it transforms to
a suitable IoT ecosystem. At present, healthcare system is undergoing a tremendous revolution with
the proliferation of the usage of the IoT architecture offering maximum device-to-device connection.
The important aspect of the contribution of IoT in healthcare sector is to ensure acquisition of real-time
health data, forward it to the destination node, followed by the subjective analytical operation. The usage of
IoT application could possibly use cloud environment if a large stream of data is required to be transmitted,
stored and analyzed [6]. Basically, the ufosage of IoT in the medical sector can perform data acquisition from
different users as well as patient in order to resist further spreading of the disease. Inclusion of medical IoT
introduces various hardware-based devices that can perform improved quality of clinical diagnosis.
The primary intention of usage of IoT is to reduce the mortality rate by i) real-time remote monitoring system
of the patient, ii) offering comprehensive analysis of any form of critical diseases, and iii) performing
predictive analysis of the occurances of any life-threatening medical condition. At present, there are various

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Int J Elec & Comp Eng, Vol. 10, No. 5, October 2020 : 4615 - 4622
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discussion of application idea concerning with the IoT system viz. i) body wearable, ii) remote health
monitoring, iii) equipments to maintain vital statistics, iv) patient-oriented medicine, v) monitoring of
medical assets, and vi) advanced analytics of the medical data [7].
At present, there has been various research work being carried out towards the discussion of
the various strategies adopted for improving medical sector with inclusion of IoT [8-10]. However, there are
various practical problems associated with the usage of the medical IoT. The first problem of medical IoT is
the usage of sensors and body wearable. Irrespective of evolution of various forms of sensors in micro sizes,
it cannot be used for all the medical condition. Another biggest problem is that different number of sensor
acquires different data form and hence body wearable could offer discomfort and burden over the patient just
for acquiring different type of bio-signals if they are required. Another problem associated with the medical
IoT is the mechanism of transmission. Once the IoT device captures the information from the sensors or
actuators, it usually forwards the information without any forms of processing on it. This causes a problem when
there is a need of performing processing on the acquired data. The third problem associated with the medical
IoT is the application design of the analytical program. Development of sophisticated analytics calls for
comprehensive consideration of intention of the application design. At present, majority of the current
application calls for acquiring the data followed by storing and managing the data in server end. However,
certain system uses such analyzed data for the purpose of subjecting the data to the analysis where certain
information is derived and passed on to the service provider in order to act upon. However, such application
normally does no communicate with the user and such information is not even user friendly. Therefore, this
paper presents a discussion of the novel solution that perform forwarding of the contextual message to
the patient for the purpose of resisting the possible situation of stress. Section 1 discusses about the existing
literatures where different techniques are discussed for detection schemes used in power transmission lines
followed by discussion of research problems and proposed solution. Section 2 discusses about algorithm
implementation followed by discussion of result analysis in Section 3. Finally, the conclusive remarks are
provided in Section 4.
This section discusses about the existing approaches where researchers have used heartbeat data
combined with IoT for facilitating healthcare sector with better advantage of clinical guidance. The recent
work of Majumder et al. [11] have developed a predictive system for assiting forecasting of the warning for
heart attack using wearable device over IoT. Zhang et al. [12] have developed a remotely tracking of patient
health using IoT integrated with sensor network. Discussion of wearable device towards the usage in
healthcare system in carried out by Al-Eidan et al. [13]. Similar form of discussion has been carried out by
Meharouech et al. [14]. Mahmoud et al. [15] have performed an investigation towards cloud-of-things
architecture associated with the healthcare system. The authors have mainly emphasized over energy
efficiency factor in this presented system. Espinilla et al. [16] has discussed about the decision making
framework that is capable of extracting knowledge over the bio-signals captured from the wearable
applications in IoT. Study towards integration of wearbale device and IoT was also carried out by
Monton et al. [17] using hardware-based approach in order to offer reduction in delay.
Another framework developed by Pasha et al. [18] emphasizing over the interoperability in
the system design of healthcare. Adoption of fog computing integrated with an IoT towards developing
a better form of healthcare system was carried out y Paul et al. [19] for increasing the efficiency of
the system associated with the healthcare monitoring system. Wu et al. [20] has presented model for retaining
energy efficiency of the system developed with wearable IoT system. The authors have claimed of their
enhanced applicability towards mobile application during critical situation. Guan et al. [21] have presented
another system design which assists the elderly patient remotely where a home gateway system is positioned
to trace the critical limits of the heartbeats signals. A specific prototype of healthcare monitoring has been
developed by Desai and Toravi [22] using hardware-based system. Ali et al. [23] have developed a system
that conjoins sensor network with mobile device for monitoring patient health condition on the basis of its
pulse. Similar form of study has been also carried out by Poorvi et al. [24] and Kumar et al. [25].
Sathishkumar et al. [26] have developed a prototype using IoT integrated with heartbeat sensor in order to
resist uneven situation that could make the road safety vulnerable. The study carried out by Irawan and
Juhana [27] using nearly the similar approach for tracking the heart rate of the patient using signal processing
approach. Discussion of the healthcare-based application has been carried out by Rodriques [28] as well as
there are also researchers that has focused on the security aspect of similar form of integrated IoT sensor
healthcare application as that seen in work of Yeh [29]. The work carried out by Fouhad [30] has developed
nearly similar prototype application focusing on the supportability of the telemedicine. Therefore, there are
various studies being carried out where heartbeat sensors were used for developing an IoT framework for
healthcare sector. Hamza et al. [31] have introduced a technique for smart car parking based on cloud method
containing various types of sensor. The PIRs has been employed to detect the object motion.Bhagchandani
and Augustine [32] have discussed a holistic solution by using the IOT method including data analytics.

Int J Elec & Comp Eng ISSN: 2088-8708 
Framework for propagating stress control message using… (Eisha Akanksha)
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The IoT allows real-time capturing and calculation of the medical data from smart sensors installed in
wearable devices.The work done by Wani and Revathi [33] the expansion of ransomware attacks for
ransomwares threatening IoT. An identification tool for IoTransomware attack is demonstareted that is
designed after reviewed of ransomware for the IoT. The presented technique shows the present IoT traffic
through SDN gateway. It utilizes policies borders in SDN organize for sensing of ransomware in the IoT.
The potential research problems associated with the existing system are as follows:
 Existing approaches of using wearable devices and IoT is restricted only by receiving the heartbeat signal
while not processing them for better clinical / motivational inference.
 At present, the studies do not discrete use machine learning techniques to arrive at user friendly
suggestion in faster track meant exclusively for user.
 Assessing the reliability of the study using accuracy parameter and faster receiving of the signals in
presence of a computational modeling is absent.
 Existing approaches mainly uses prototyping scheme where only narrowed area of implementation can be
assessed.
Therefore, the problem statement of the proposed study can be stated as “Developing a framework
that can facilitate user-friendly inference to countermeasure against their stress level with cost effective
modeling is a complex and challenging task”. The next section highlights the solution for this.
The prime aim of the proposed system is to offer a novel design of a framework that offers
contextual services considering the case study of healthcare sector integrated with IoT system.
The implementation of the proposed system is carried out using analytical research methodology where
the prime concern is to offer accurate forwarding of the contextual messages to the user under variable stress
condition in order to beat the critical. The schematic diagram of the proposed system is shown in Figure 1.
Acquisition of
Signals
Signal
Classification
1
2
Processing
Signals
3
Transmitting
Signals
4
Analytical
Operation
5
Machine
Learning
6
Extract
Contextual
Message
8
Forward
Message to User
9
Heart beat
Figure 1. Proposed schema of implemented methodology
Figure 1 highlights the adopted schema of implementation. The core modules of the proposed
system are acquisition of signals, processing of signals, transmitting the signals, followed by analysis of
the signal. The proposed system considers the input as heartbeat of the user as the core indicators of stress.
It is known that different levels of heartbeat consider different levels of stress. An IoT environment is
constructed that connects the user’s wearble device, extracts all the continuous data of heartbeat, followed by
further processing the data. The proposed system than perform matching with the data with the clinical
reference in order to ensure if the heartbeat signals obtains are critical or under critical. Only the critical data
are extracted and indexed for shapping it in the form of query signals. The newly indexed query signals are
then transmitted to the cloud-application where the analytics are running. The on-cloud analytics retains
the list of all the specific messages in the form of clinical stress hierarchy. Correlation-based analysis is
performed in order to match the filtered query signals with that maintained in the cloud. After the outcome of
specific message is obtained, a machine learning approach is offered to perform training towards the obtained
correlated data. This training process finally offers accurate contextual message that is forwarded to the user
via network over the communication device of the user.

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2. SYSTEM DESIGN
The complete design of the proposed system is constructed using divide and conquers approach that
mainly emphasize over obtaining accuracy of the vital stat e.g. heart beat of the user. Different individual
modules of design have been constructed in order to develop the proposed model. This section discusses
about assumption-dependencies, implementation strategy, and execution flow of proposed system as follows:
2.1. Assumptions and dependencies
The first assumption of the proposed study is that there is a synchronized set up of the wearable
body sensors with the various IoT devices forming a smart healthcare analytic system. There are also good
possibilities of using different forms of wearable sensors connected with each other and form a body area
network. The second assumption of the proposed system is that heartbeat data is continuously monitored by
the sensors; however, the filtration of the acquired signal corresponding to the severity of the stress condition
is carried out by the sensor and not by the IoT gateway that receives the signal. The third assumption of
the study is that complete data is forwarded through a secured and error free communication channel over
predefined IoT environment. Similarly, there are various dependencies of the proposed system e.g. i)
an error-free communication channel is required for performing transmission of the acquired heartbeat signal
to the destination node, ii) There is also a dependency of elastic storage capacity in order to retain all
the trained information obtained from the training phase. The trained data will be used for performing
validation of accuracy as performance measures of proposed forecasted contextual message. iii) The final
dependency of the proposed system is that there should be a hierarchical list of clinical/motivational
suggested message that are specific to actual medical context of the captured heartbeat of the user.
2.2. Implementation strategy
The implementation of the proposed system is carried out by sequential module design which is
meant for performing following steps of operation with defined strategies:
 Acquisition of signals
The proposed study considers that a user is wearing a body wearable sensor that can measure
the true heart beats shown in Figure 2. The study considers that there are three categories of heartbeat viz. i)
category-1: High beat rate (>100 beats per minute), ii) category-2: Medium beat rate (100-60 beats per
minute), and iii) category-3: (<60 beats per minute). The heart beat is a continuous signal which the sensor
captures followed by segregating the signals on the basis of the categories and then it is further subjected to
processing in the next step.
Figure 2. Acquisition of signal
 Processing the signals
The sensors are also considered to posses a capability to perform processing of the heart beat signals
that corresponds to unique levels of stress of the user. For this purpose, the system considers that fact of
possible situation of stress level is associated with either tachycardia or bradycardia, which is both considered
as lethal conditional. Such condition will require an immediate medical attention. All the signals of
heartbeats will be forwarded to different carriers of communication channel between wearable sensor and
nearest IoT device (or IoT gateway system). It is to be understood that IoT gateway also acquire signals from
other mobile users and will be responsible for further processing it. By processing, it will mean that a module
is constructed that considers the category-1 and category-3 signals and time-stamps it. This signal is then
further indexed in the form of two specific query systems. The first query is formulated for category-1 signal
while the second query is formulated for cateogory-3 signal. Therefore, the processing of this signal is
carried out for only critical data i.e. for category-1 and category-3 while leaving the non-critical signal i.e.
category-2. Figure 3 offers pictorial representation of this operation.
Heat Beat
Classification
Category-3 (Lower Heat beat)
Category-2 (Medium Heat beat)
Category-1 (High Heat beat)
User with
Wearable
Sensor

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 Transmitting the signals
The transmission of the filtered signal from the prior stage is carried out in this module. The filtered
signal mainly consists of indexed signal of category-1 and category-3 where the indexing will mean
embedding the respective heartbeat with time stamp and user identity. The fused data is now forwarded to
the IoT gateway node followed by forwarding the same to the access point and edge server. However, while
doing so, there is quite a possibility that any of this networking device (e.g. gateway node, access point, edge
server) is found to be currently processing a job. In such condition, an adaptive queue system is considered
where the incoming task is reposited for immediate processing. Another interesting part of this transmission
module is that it can also support prioritizing the any specific incoming signals using two attributes. The first
attribute for prioritizing the incoming signal will be based on certain cut-off level of heart beat. An additional
much-critical cut-off level can be given to further develop an additional queue for such incoming signals and
they will be first processed followed by prior queue. The second attribute for prioritizing will be wait-time of
queue (such situation can occur due to network problems or high density traffic). If a signal packet is found
to wait for more than a specific duration of time, it will be soon pushed to priority queue. The end result of
this module is that the fused data are successfully transmitted to the cloud application that runs analytics for
further analyzing the fused signals. Figure 4 represents the pictorial way of this transmission process.
Figure 3. Processing the signals
Figure 4. Transmission of signals
 Analysis of signals
This is the last phase of implementation that emphasize on yielding a definitive motivational /
clinical message (or suggestion) to the respective users on the basis of obtained signals shown in Figure 5.
The study consider a present of distributed motivational / clinical message maintained over the storage units
with a preamble matching with specific condition of the heart beat. The proposed system applies a correlation
of the obtained signal with the correlated value of the messages and only the highly correlated value is
shortlisted. However, in order to prevent any form of false positive, the proposed sysem applies machine
learning algorithm that performs training operation in order to validate the outcome resulted from correlation
and finally obtain the elite message to be forwarded to user.
Figure 5. Analysis of signals

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2.3. Execution flow
The proposed system constructs an algorithm that is responsible for transmitting the contextual
message over IoT environment on the basis of the heart beat data. The algorithm takes the input of d (sensory
data) and H/M/L (high/medium/low heartbeat) which after processing will yield an outcome of μ (contextual
message). The steps of algorithmic execution are as follows:
Algorithm for forwarding contextual message via IoT
Input: d (sensory data), H/M/L (high/medium/low heartbeat),
Output: μ (contextual message)
Start
1. Fori =1:(dmax)T
2. If 1<i<dlow
3. Flag dL
4. ElseIfdlow<i<dmed
5. Flag dM
6. ElseIfdmed<i<dhigh
7. Flag dH
8. End
9. Ford=[L H]
10. compared with scond
11. τ = gen [squeryHsqueryM]
12.End
13.applyC=f(τ, dblist)
14. μg(C)
End
The discussion of the execution flow of the proposed algorithm is as follows: Assuming that a body
wearable sensor device has captured heart-beat data d using a sample period of T and the sampled data is
forwarded to the IoT gateway node in the proximity. Consider that dmax is the maximum size of the sampled
heat-beat data; the algorithm evaluates the type of the heart beat data in terms of cut-off. If the heart-beat is
found to be with low cut-off dlow (Line-2) than the filtered data d is flagged as L (Line-3). Similarly,
the heart-beat data d is compared with medium (dmed) and high (dhigh) cut-off to flag M and H respectively
(Line-5 and Line-6). For an effective analysis, the algorithm ignores M signal but considers L and H signal.
A temporary buffer space is created that retains only L and H (Line-9). The algorithm also constructs
a clinical condition scond to define the degree of criticality of the L and H type of signal of heartbeat and this
condition is a referential point of stress factor too. A consideration of tachycardia (<60 beats per minute) and
bradycardia (more than 100 beats per minute) is also considered while forming the conditional function scond
(Line-10) in order to further confirm the severity of stress as both the conditions are considered to be
dangerous. The system then formulates a function to generate the normalized heart rate corresponding to H
and M and is represented as squeryH and squeryM respectively (Line-11). The query matrix τ retained
the structured form of the query signal squeryH and squeryMand this message is forwarded to the cloud
aanalytical application via gateway node, access point, and edge server. This process of query generation is
repeated for all the value of sampled sensory data M and H (Line-9 to Line-12). After the matrix τ is obtained
in the cloud analytical application it matches with the hierarchical list of pre-defined messages dblist. It should
be noted that there are different forms of prediefined messages unique made for each categories of queries.
The algorithm than applies correlation function f(x) over received data τ with list of pre-defined messages
dblist (Line-13). As correlation results in sorted value of higher correlated data as an output; therefore there
are chances that such outcome could also have outliers. Apart from this, as there are various types of disease
condition that could also have similar pattern of heart beat. Therefore, forwarding same contextual message
could be proved as false positive. This problem is solved using machine learning approach, where training is
carried out considering specific charecteristics of user’s data along with the generated heart beat data in order
to conclude the elite outcome. The elite outcome will mean that the algorithm successfully concluded about
one message from the list of message that is highly suitable for the user in its current health condition or
called as real-time contextual data. The proposed system applies a machine learning function g(x)
considering the contextual data C as the input argument in order to generate an elite outcome μ (Line-14).
Therefore, the proposed execution of the algorithm is highly a progressive step with an inclusion of only one
iterative step during training process which also maintains a higher degree of cost effectiveness while
processing the heartbeat signal for the purpose of forwarding the correct contextual messages to the user via
any communicating devices.
3. RESULT ANALYSIS
The scripting of the proposed system has been carried out using MATLAB considering the heart rate
dataset [34]. As the proposed system deals with offering the precise contextual message for user, therefore,

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it is essential to validate if the correct contextual data has been received and if it has been received on time.
Therefore, the core performance parameter considered are accuracy of choosing the right contextual message
and processing time that defines the duration spend from acquiring the heart beat data to receiving of
contextual message to the user. The proposed system also performs comparative analysis with the existing
machine learning approach to find the best approach.
The study outcome clearly shows that deep neural network offers better advantage with respect to
the accuracy shown in Figure 6 and processing time shown in Figure 7. The core reason for the higher
accuracy is its capability to recognize the error even if the error is outside of the tolerance level. This feature
is not present in other forms of machine learning approach. The prime reason behind this is increased
training time involved in neural network and support vector machine (SVM) for matching the best result with
the correlated data maintained over the list. Therefore, it can be said that proposed system offers
cost effective solution where the contextual data is accurately generated as well as incurs less time to
forward the correct contextual message to the user and hence it offers practical advantage over critical
clinical requirement.
Figure 6. Comparative analysis of accuracy Figure 7. Comparative analysis of processing time
4. CONCLUSION
Acquisition of the bio-signals can be suitably used for controlling stress factor with an aid of IoT.
However, processing the bio-signals like heartbeats is an important task that requires a cost effective method
for transforming the signals over the presence of analytics. Therefore, the proposed system introduces
a mechanism that extracts the heartbeat signal, processes it, and transmits it to the edge server via IoT
gateway system. The proposed system uses correlation-based approach in order to extract the definitive
motivational/clinical suggestion that are meant to be directly forward to the user in order to instantly control
the level of stress. Further machine learning approach is used for further extracting the accurate contextual
message. The simulated outcome of the study proves that proposed system offer good accuracy and reduced
processing time to do the entire operation.
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[34] “BIDMC PPG and Respiration Dataset,” [online], Avaible: https://physionet.org/pn6/bidmc/, [Retrieved on 27-06-2019].
BIOGRAPHY OF AUTHOR
Eisha Akanksha, she is an associate professor, Department of Electronics & Communication,
CMRIT, Bengaluru, India. Her areas of interest are wireless communications, Light Fidelity,
Internet of Things and Cloud Computing. She has around 6 years of experience in teaching field.
She has worked 2.4 Years of Industry Experience- Worked as software engineer in Sasken
Technologies Bangalore, India.

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