IOT based Remote Heartbeat Monitoring
Stella Joseph
M.E, Dept of Applied Electronics
Loyola Inst. of Tech. and Science,
Thovalai, TamilNadu, India
stellaeceengineer@gmail.com
D. Ferlin Deva Shahila,
Assistant Professor, Depat. of ECE,
Loyola Institute of Technology and
Science, Thovalai, 629302.
ferlin_franklin@yahoo.com
Abstract—Deployment of Internet of Things (IoT) has made
sensors smarter so that they can interact, collaborate and share
experiences. IoT not only reduces human intervention but also
assists in taking timely decision. IoT based telehealth is a new
paradigm in the health-care industry. Proposed work is an
implementation of IoT based heartbeat monitoring system.
Proposed method is tested with a photo phlethysmography
sensor interfaced to a micro-controller. Micro-controller
monitors the heart beat sampling rate and then transmits
discrete values over ESP8266 Wi-Fi module. Thingspeak has
been used as the remote cloud server for secure data storage
and access. Key contributions of the work are, firstly enabling
secured cloud platform to keep track of heart beat and
secondly facilitating automatic alert system in case of
emergency. Besides this, the proposed set up supports made to
required sampling rate and investigation. Results have been
produced for different acquisition conditions. An analysis
scenario, with minimum and maximum thresholds, which can
send alert message if the heart beat rate goes beyond the range
has been manifested and tested. Proposed method can be
deployed for secured and continuous monitoring of heart beat
while customizing the analysis.
Keywords—Internet of Things (IoT), remote heartbeat
monitoring, ThingSpeak, ESP 8266
I. INTRODUCTION
IoT is a assemblage of sensors with reliable internet
configuration and meaningful simulation technology. By and
large IoT concepts produce enormous data collection and
aggregation. Multiple devices and sensors lead to sensible
analysis and uses. Off late human health monitoring systems
has moved from emergency based visits to hospitals to
periodical checkups, wearable sensor based data recording
and advanced level processing. Remote health monitoring is
one of the killing and rapidly growing applications of IoT.
IoT in healthcare is a heterogeneous computing, wirelessly
communicating system of apps and devices. The goal is to
provide recommendation service to patients and health
providers remotely, leading to better diagnose and monitor of
health related information. There is multitude of benefits of
telehealth service. Immediate medical attention, no waiting
in long queues, consulting experts remotely, reduced
documentation and paperwork, timely release of insurance
coverage, expanded reach to various health service provides
are some noteworthy outcome of IOT based telehealth
service.
Heart rate monitoring is a device which allows to
measure and record pulse rate in real time. Stored record can
also be used for later study. It is largely used in hospitals for
checking the health of patients(s) and performance of
physical exercise. More than three billion of people are at
risk of having heart attack. The focus of this research is to
monitor heart beat remotely. The goal is to develop a low
power, more reliable, non-intrusive data acquisition system,
XXX-X-XXXX-XXXX-X/XX/$XX.00 ©20XX IEEE
Suprava Patnaik
Professor, School of Electronics
Kalinga Inst of Industrial Tech
Bhubaneswar, Orissa
suprava.patnaikfet@kiit.ac.in
which gather information on the cloud and is accessible to
authenticate experts anytime from anywhere. Heartbeat can
be used for many applications. A few notable ones are:
drowsiness detection system for safe driving, for calculating
calories burned during exercise, post surgery health
monitoring, health surveillance system for physical or
constructive workers, HRV monitoring system for
hypertensive patients, monitoring chronic cardiovascular
disease, etc.
This work addresses an integration of heart beat sensing,
reliable wireless communication, and remote authenticated
access for appropriate action. Proposed authenticated access
includes access for uploading the data and analyzing the data
to and from the cloud.
II. LITERATURE REVIEW
Since the development of IoT concept many researchers
have contributed in various ways to the field of e-health care.
Key aspects of e-health care are remote study of health
record followed by recommending telemedicine. Several
publications have been studied and analyzed during the
execution period of proposed methodology. In [1], authors
have proposed an m-health acquisition and k-healthcare
analysis architecture to use smart phones and deploy IoT. An
autonomous IoT based wireless body area network
implementation has been discussed in [2], with a mention
about harvesting of solar energy that can be optimally used
by the wearable sensors. There are some works towards
using Arduino and Raspberry-pi processors for heartbeat
sensing [3, 4, 5] and implementing warning systems[6, 7]
using IoT.
IoT based remote monitoring involves due authorization
and access control of data received from distance
environment. A trivial approach towards protecting data and
individual information is encryption. It is applied before data
being uploaded onto the cloud servers. Encryption reduces
privacy risks. However it removes search capabilities and
therefore resulting in functionality loss towards finding off
the beaten conditions. A searchable encryption scheme for
secure remote monitoring (SRM) has been addressed in [9]
and authors have claimed that it can also detect outlier. In [8]
authors have demonstrated a hardware based solution Intel
SGX.In one of the papers [10], the authors have discussed
the importance of sharing e-medical records securely and
reliably.
ThingSpeak [11] is a password protected cloud based
research system to ensure security. One can send data to
ThingSpeak from personal Wi-Fi enabled devices, create
instant visualizations of live data and send alerts using
personalized web services like Twitter. ThingSpeak is a user
friendly development platform that permits to create sensor
logging applications, tracking applications, and status update
through a social network.
There are many inspiration factors behind secure
healthcare service. Firstly, transforming from paper-based
health records to an everlasting Electronic Health Records
(EHRs) [13]. Secondly, it can captivate the benefit of large
storage capacities [14]. Besides these the increasing
importance of experts across organizations in sharing and
collaborative use of health data [15, 16] can be made
possible by cloud based secured health services.
III.
PROPOSED METHODOLOGY
A simplified schematic of the proposed proposed
methodology is shown in figure 2 and the components are
discussed in this section. Stethoscope and Electrocardiogram
are the two commonly used devices for heartbeat monitoring.
While the former requires expertise later is expensive. Apart
from the traditional devices heartbeat can be measured in an
automated way by means of wearable optical sensors. These
heartbeat sensors are based on the principle of photo
phlethysmography (PPG) [11]. PPGs are nothing but a set-up
of LED that imparts light and photo diode to collect the
reflected light from the blood. Any change in the blood
volume at any vascular region of the body, usually finger
tips, causes a change in the light intensity. PPG is a
noninvasive, low cost, and simple optical measurement
technique applied at the surface of the skin to measure
physiological parameters.
Figure.1 (a) PPG Sensor
A.
(b) ESP 8266
Architecture and Sensing Module
connecting smart sensors and micro-controllers to Internet.
It has an integrated TCP/IP protocol stack. ESP 8266
maximum working Voltage is 3.6V. It can be used for bidirection data transmission. The ESP8266 is capable of
either hosting an application or offloading all Wi-Fi
networking functions from another application processor.
B.
Block diagram and Implementation
Fig. 3 is the block diagram of the proposed
monitoring system. Key components of the setup are: i) the
PPG sensory unit SEN-11574, ii) Local processing unit, iii)
Wi-Fi module ESP 8266 for secure access of internet iv)
LCD setting for data display and iv) Result analysis and
alarming coding for emergency action. In this work Arduino
Uno micro-controller is used as the central control unit.
Arduino is preferred to Raspberry-pi or Odroid because of
its PWM control facility. It supported the requirement of
reading an analog signal from PPG sensor and sampling it at
a desired baud rate.
Heart beat monitoring requirements are case specific.
Heart beats leading to intrinsic variation in blood volume in
human circulatory system and it is controlled by the
Sinoatrial (SA) and Atrio-Ventricular (AV) nodes. For a
middle aged person with a healthy heart, rate is between 60100 beats per minute when resting. Several clinical and
technical factors can lead to heart rate variability. Dizziness,
exercising, breathlessness etc. can cause quicker heart beats
known as tachycardia. Syncope, diaphoresis, etc. causes
slower heart beats called bradycardia. Like blood pressure
heartbeat also changes with age. From clinical perspective
heartbeat are strongly influenced by age, gender, functional
capacity, chronic comorbidities and therapy.
The following figure shows the architecture of the setup.
Figure.3 Block diagram
Considering the fact that there exist various underlying
situations responsible for heart rate variability, result analysis
need to be tailored made and application specific.
Figure.2: Architecture of heart beet sensing system
Arduino in used to receive the data from the PPG sensor
and send the discrete samples to cloud server through ESP
8266. ESP8266 is a 3V Wi-Fi module often used for
C.
IoT Middleware Setting
IoT is one of the upcoming concepts. It aims for
convergence of technology, applications, things, devices,
etc., and provide innovative service at low-cost by using
wireless communication protocols. Two key perspective of
IoT are; firstly observing data for real time actionable
insight and secondly proactive or predictive monitoring.
Cloud computing along with IPV6 provide everything to
promote, develop and integration IoT. There are so many
people in the world who will like to use e-health check up
support and get rid from suffering because they do not have
proper access to hospitals and health monitoring. Every
object which connects to IoT requires a unique address or
identification from IPv6. Key requirements of IoT based
health monitoring are scalability, privacy and security,
distributed and decentralized monitoring, reliable real time
communication. Apart from things unique identification and
data visualization the cloud based software acts as a bridge
between an operating system or database and applications,
especially on a network, plays crucial role in any IoT app.
There are many middleware platforms. Thingspeak,
Ubidots, AWS IoT, Temboo, Corlysis, Horavue are some
well known IoT platforms that are widely available on the
internet. Selecting an IoT platform can be based on important
aspects like easy user interface, connectivity compatibility,
the delay involved, inbuilt features of the platform, ease of
sensor integrity and finally the service cost. These platforms
support many of the common IoT protocols like TCP, UDP,
MQTT, CoAP and HTTP. This work is based on
experimentation on thingsspeak as the middleware.
Thingspeak has better visualization and warning mechanism
that are supported by MATLAB. IoT supports for placing
things anywhere, its the challenge for middleware to make
resources usable securely from everywhere.
ThingSpeak based on HTTP protocol and supports two
way transfer of information from various device. It is highly
flexible when new and advanced functions need to be added.
It requires each user to create individual accounts and create
channels for every sensor. Microprocessors can be
connected to ThingSpeak cloud through ESP 8266 by using
‘AT’ command and appropriate baud rate . Sensors need to
be assigning with individual channels accessed by means of
API Key. Role of ESP module is to establish Wi-Fi
connection by using the AT Commands. Arduino then
establishes TCP connection to the ThingSpeak cloud so that
the sensed discrete values are transmitted at a pre-defined
baud rate. Fig.4 shows the complete set up used for this
experimentation.
Figure.4 Physical setup for heartbeat monitoring
Remote health monitoring involves patient’s private
information. Records need to be maintained confidentially
however allowing access to experts and also for trustworthy
analysis. Privet information can’t be shared publicly in order
to avail the service. Advanced cryptography tools, such as
homomorphic encryption, secure multi-party computation,
searchable encryption can assure security of Middleware
cloud. However, they may cause delay in communication by
imposing substantial computation overhead. ThingSpeak
allows blindfold computation. Only the processed results are
decrypted and made available by using secret keys.
IV.
RESULTS AND CONCLUSION
The proposed methodology has been tested for various
situations and results are produced in this section. In order
to compare the accuracy of cloud reading another LCD
display is configured locally. Average beats per minute
(BPM), computed over every minute and rounded to nearest
integer, is found to be accurate for all the set ups. A few
cases have been shown here, to ensure that locally sensed
information are saved in the middleware without any loss.
Figure. 5 (a)
Figure 5 (b)
Fig. 5(a) and (b) shows the middleware plots and LCD
display of average heartbeat for a person aged 25 years. Fig.
6(a) and (b) are the similar results but for an aged person.
Both are for resting condition with an average rate 88 and
111. Middleware displays are matching exactly with the on
site LCD observations. A simplistic analysis observed
during this experimentation is average heart rate is higher
for the aged person. Information communication delay is
insignificant and there is no error noticed due to
discretization or communication over internet.
Figure 7 (b)
Figure 6 (a)
Fig. 7(a) and 7 (b) are the results for the earlier 25 years
person but after trade-mill exercise. Goal was to monitor
functioning of heart under stress. BPM is monitored during
the purposeful physical activity and after an hour of exercise.
This time the average heart beat is more than that of exercise
state. Therefore the proposed set-up can also be used to
monitor how long it takes for the heart to resume its resting
rate, that is heart-rate recovery time. Generally, faster a
person's heart rate recovers, or reaches its resting rate, the
healthier heart condition he or she is in.
This work concludes that real time heart beat monitoring
is possible and remote analysis wherever required can lead to
better health monitoring. IoT and Cloud computing will
revolutionize
e-health
care.
However
it
needs
experimentation on large-scale IoT based data storing,
processing and retrieval.
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