IoT based on secure personal healthcare using RFID technology and steganography

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 11, No. 4, August 2021, pp. 3300~3309
ISSN: 2088-8708, DOI: 10.11591/ijece.v11i4.pp3300-3309  3300
Journal homepage: http://ijece.iaescore.com
IoT based on secure personal healthcare using RFID technology
and steganography
Haider Ali Khan1
, Raed Abdulla2
, Sathish Kumar Selvaperumal3
, Ammar Bathich4
1,2,3
Faculty of Computing, Engineering, and Technology, Asia Pacific University of Technology and Innovation (APU),
Technology Park Malaysia, Bukit Jalil, Kuala Lumpur, Malaysia
4
Faculty of Electrical Engineering, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia
Article Info ABSTRACT
Article history:
Received Sep 23, 2020
Revised Dec 4, 2020
Accepted Jan 14, 2021
Internet of things (IoT) makes it attainable for connecting different various
smart objects together with the internet. The evolutionary medical model
towards medicine can be boosted by IoT with involving sensors such as
environmental sensors inside the internal environment of a small room with a
specific purpose of monitoring of person's health with a kind of assistance
which can be remotely controlled. RF identification (RFID) technology is
smart enough to provide personal healthcare providing part of the IoT
physical layer through low-cost sensors. Recently researchers have shown
more IoT applications in the health service department using RFID
technology which also increases real-time data collection. IoT platform
which is used in the following research is Blynk and RFID technology for the
user's better health analyses and security purposes by developing a two-level
secured platform to store the acquired data in the database using RFID and
Steganography. Steganography technique is used to make the user data more
secure than ever. There were certain privacy concerns which are resolved
using this technique. Smart healthcare medical box is designed using
SolidWorks health measuring sensors that have been used in the prototype to
analyze real-time data.
Keywords:
IoT
NodeMCU
RFID
Sensors
Smart healthcare
Steganography
This is an open access article under the CC BY-SA license.
Corresponding Author:
Raed Abdulla
School of Engineering
Asia Pacific University of Technology and Innovation (APU)
Technology Park Malaysia, Bukit Jalil, Kuala Lumpur 57000, Malaysia
Email: dr.raed@apu.edu.my
1. INTRODUCTION
People's safety and critical health become a paramount concern whereas, an estimate of 421 million
hospitalizations adverse each year. A lot of research has been studied and conducted related to the IoT using
it to acquire data continuously and wirelessly which is distributed in the healthcare field. With the
employment of IoT in the health sector, which is defined on mobile or computer, medical sensors, and
researcher's communication technology for healthcare services [1-3]. Remote monitoring has become a
strategic tool for social platforms over the last few years. Over the past few years, RFID has become emerged
for automated and data collection identity. RFID technology is applied and manufactured by a lot of
industries in terms of retail and logistics but it is not related to the medical instrument [4]. Emerging
technology is related to the IoT and increment of a variety of cheap medical sensors such as wearable,
environmental, and implanted, have the potential habitat of a person and the internet to manage and produce
medical knowledge. In recent research goals of extraction of physical information about tags through
electromagnetic signals received. RFID, therefore, provides efficient ways of interaction of a person with the

Int J Elec & Comp Eng ISSN: 2088-8708 
IoT based on secure personal healthcare using RFID technology and steganography (Haider Ali Khan)
3301
surrounded environment with the last few meters of IoT concerning quantification [5]. RFID system having a
battery-powered surrounding will be wearable smart IoT device supporting aid services with a network of the
reader. The project focuses on the scenery of current RFID work from an IoT viewpoint. The project is
launched to aim the IoT based system for personal healthcare using RFID technology. This research will
cover passive devices having a UHF short range of (10-13.56 MHz) which are capable of providing enough
read range to implement sensors for wellness and monitoring the local quality environment [6]. IoT is the
most innovative resource in manufacturing, commercial, and residential structures every day, playing a
critical role [7-12]. The use of IoT in monitoring and personal healthcare helps reduce human interaction in
remote monitoring, processing, and review of RFID-powered data [13-16].
IoT based Personal healthcare RFID systems in Smart spaces have been developed being costly, In-
home IoT based technology a suitable platform is still missing with user security concerns and implantation
of RFID tags in the human body has also been a major issue raised by many people. According to the study
in [17] found that average people questioned the idea of implanted chips. Security and safety of the user are
still missing in smart health care systems using RFID which is a questionable problem till now.
There was much research that has been conducted contributing to the field of IoT technology to
acquire data continuously which is processed timely and wirelessly in Personal healthcare, but the results are
not satisfactory [18-20]. RFID systems can help identify patients but built systems efficacy and user's
protection are still missing [21-22]. Systems that have been developing so far lack all health parameters in
one system which is not convenient for the user’s experience [23-25]. The system with different sensors
measuring heart rate, oxygen level, body temperature, humidity level, air quality, and sweat level of the body
using GSR can be more effective and efficient for user experience and personal healthcare parameters. In
general, most of the systems built based on RFID healthcare do not effectively cover the concerning
parameters. RFID used in this project will act as a security shield for the user's safety in the health care
system. Given all this, with all physical health parameters and data reliability, a wearable smart design or
medical box would be more successful.
This research aims to design and develop IoT based Personal healthcare system using RFID
technology for the user's better health analyses and security purposes by developing a two-level secured
platform to store the acquired data in the database using RFID and Steganography. The current medical
model towards medicine filed can be wisely boosted involving sensors such as environmental, wearable
sensors. RFID smart health care system enables the remote assistance of the user's health and monitoring
parameters. Making the environment smart enough through low-cost energy-autonomous and disposable
sensors. For necessary evolution, IoT passed personal health care using RFID technology comes in handy.
Sensors are used for air monitoring and to avoid the inhalation of anesthetic gases along with a check on
other health parameters such as heart rate, oxygen level, body temperature, humidity level, air quality, and
Co2. The model/design which will be used in this proposal consists of body network sensors attached to the
microcontroller Arduino Mega2560 and Arduino Uno linked with Node MCU. The final testing will include
IoT and the data will be displayed on a mobile android app. RFID adoption in healthcare will improve patient
safety which includes monitoring, physical health, surrounding information, and body health level in all
aspects.
This paper is organized as follows. In the following section, we discuss the issues of people's safety,
critical health and IoT based Personal healthcare RFID systems. In section 2, we describe the overall block
diagram and design consideration. In section 3, we explain the results occupied from the data analysis using
smart healthcare medical box. Then, we evaluate the system performance in section 4 and give a conclusion
in section 5.
2. PROPOSED DESIGN METHOD
2.1. Overall block diagram
The key sections of the framework as proposed are shown in Figure 1 where a type of block is
connected with arrows indicating the relationships between the blocks and the contact with data stream
directions. Stage 1 of the block diagram is the main controller of the system which is Arduino mega 2560.
There are multiple sensors connected with the microcontroller which are taking input data from the hand as
shown in stage 2 of the block diagram. For the two-step, security feature the keypad is connected with the
Arduino mega which requires an input password to access the system.
Stage 3 is the IoT platform using Blynk software where all the necessary data is displayed as shown
in the block diagram. NodeMCU is connected with Arduino mega using Rx pin which transmits data from
Arduino once the passcode is correct and displays it on the IoT platform using Wi-Fi connection. An RFID
reader is connected to NodeMCU which is operated manually and also used for two-step security features in
the project. To access the data on the IoT platform, the user has to place their RFID card on the reader. If the

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Int J Elec & Comp Eng, Vol. 11, No. 4, August 2021 : 3300 - 3309
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card ID is correct, data will be transmitted or otherwise, the system will display access denied text. For
security purposes, the user is required to enter a security passcode to assess. The system and get full access to
all the functions within the system in such a way that each user, that is, the RFID cardholder has their
password.
Stage 4 consists of an IoT database where the passcode and user ID are stored in arrays along with
RFID tag ID and user data received from the sensors. The data is stored in a tabular fashion to analyze user
previous data and recent data and check whether the patient needs immediate medical assistance or not. If the
data falls below the threshold value, then the authorities and family members are informed using SMS
technology making the system automated and completely closed.
Stage 5 shows the LCD system installed on the smart healthcare medical box for quick assistance of
the user from which they can get to know their healthcare parameters and can easily access healthcare data in
their surroundings e.g. (temperature, humidity, and air quality (Co2)). Each block diagram is divided into
step by step procedures which best describes the best possible ways to implement the idea practically.
Figure 1. Overall main block diagram of a complete system
2.2. Working principle
Turn on the system by using an N.O switch as shown above in Figure 2. Once the system gets turned
on, all the components attached to that system are powered on such as Arduino Mega, NodeMCU which are
used for data acquisition and transmission. The sensing circuitry consists of a heart rate sensor, GSR sensor,
air quality sensor, smoke detection sensor, body temperature sensor, and digital humidity and temperature

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sensor which are being connected to the main microcontroller, Arduino Mega. The display unit of the system
consists of one OLED and one LCD screen for displaying the patient relative data. Arduino Mega, that is, the
brain of the smart healthcare system is utilized within the implemented system to acquire data from all
sensors excluding the RFID reader that is connected to the Nodemcu.
Figure 2. Complete flowchart of the system
The main reason behind connecting the RFID reader to the Nodemcu instead of connecting it to the
main Microcontroller is that the Arduino Mega SCL and SDL pins are occupied by the display unit
represented by the OLED and LCD screens. In terms of user security, the developed medical healthcare
system incorporates a reliable security system for securing patent data. For security purposes, the user is
required to enter a security code to access the system and get full access to all the functions within the system
in such a way that each user is provided with a personal RFID card and passcode. Once the RFID is placed
and the corresponding password is entered, the system will send the data to the IoT cloud using the data
received by the NODEMCU from the Arduino Mega. To get easy access to the data, the user is required to
place their hand on the machine and place their RFID card on to the system. Once the system recognizes the
user card, data will be shown on the IoT platform. The transmitted data will be sent to a DATABASE so that
each user data can be stored safely which can be later reviewed for better medical health analysis. This
database file is called out in the MATLAB program. Run the MATLAB program and GUI will appear. Click
on the user button to enter. Each user is asked to put in their passcode assigned personally to them only. Once
the user enters the password, on the left side an image will appear, and on the right-side data will be appeared
which has been stored in the database. The image on right can only be seen by others and the data can be
seen by the user himself only. This technique is call steganography which is used to keep the user data
secure.

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3. SOFTWARE RESULTS (GUI)
Figure 3 shows the results occupied from the data analysis using smart healthcare medical box. The
galvanic skin response is measured using GSR sensors on the fingers and the working principle of it is very
simple. As the user moves his fingers, there is sweat on the fingers from which the sensor detects the
emotional change of the user. For example, as seen in Figure 4, there is a galvanic skin response graph with
two-line indicating the normal emotion was the user and the change in emotion by just moving his finger.
Redline shows the normal emotion and the white line shows a change in emotion as the user moves his
fingers. If the sweating threshold moves above the pre-defined value which is 60 then the white line starts to
move above of red line for a specified time as shown in the graph. The live graph which is used to display the
emotional change of the user has the ability to shown live data for up to 3 months. The smoke detection
alarm is used for two different conditions, such as the "Normal" condition and "Alert" condition. As the
threshold passes 102 then the smoke detection sensors (MQ-135) sends the data to the app as “Alert” and the
value is showed to see how much smoke is present in that particular area. Max 30102 sensor is used to
measure heart rate and Spo2 level of the body. The body temperature is sensed by the LM 35 sensor module
which shows an accurate body temperature which is 36. Air quality, which is shown in Figure 5, the GUI is
CO2 gas present in the air which is also measured by MQ 135 air quality sensor. User is required to place
their hands on the device. Place RFID card on the reader to send the data on IoT platform and the data is
stored locally on txt. file. Run MATLAB and call text. file there in the code and run. The text. file in which
the data is being stored is called out in the steganography program where the whole data is being hidden in a
random image.
Figure 3. Software results
Run MATLAB code and a GUI will be displayed. Select user login and out in the unique password
for each user to view the data. Data can be only viewed by the user who has entered the unique password and
others will only see an image where the text is being stored and hence data is not visible to the unknown.
Users can compare the data with the IoT platform which is Blynk in this case where data is transmitted using
NodeMCU. Following are the steps that how the steganography technique is applied to keep the data secure
and private for the users and family members so that their data is not in the wrong hands. This technique is
very useful in the healthcare field as many of the users had a concern that their data is being stolen and used
against them but now that will not be so easy again.

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Figure 4. MATLAB GUI (steganography)
4. TESTING OF THE PROPOSED DESIGN
4.1. Air quality and smoke detection
The sensor output was connected to Arduino mega. Sensor MQ 135 was used to measure Co2 in the
surroundings for patients' better health analysis and monitor breathing conditions. The gases were also
identified by assigning the threshold using the same sensor. The voltage output of the sensor was connected
to 5v of Arduino mega and the Gnd was connected with the ground of Arduino mega using wires. The
software used to program the sensor was Arduino IDE and the programming language used for conduction
the data was C+. The data was analyzed using the serial monitor in the Arduino software by Serial. print
function. Co2 is shown in PPM and smoke detection is shown in percentage with a threshold of 102 which
means once the percentage crosses the threshold of 102 the Alert sign will be indicated on a mobile app using
Blynk. The average of samples is not taken for this experiment as Co2 in the air is not constant and changes
rapidly. There were in total of 10 samples taken for the following experiment.
In normal conditions, the Co2 present in the air is around 450-550 PPM and smoke present in room
condition is about 60-80 percent. Once the sensor was exposed to the smoke of cigarette the smoke threshold
crossed 120 and gave an alert on the mobile app and the value of Co2 was shoot to 755 PPM indicating the
danger in the user's surroundings. The C02 detection test is shown in Figure 5. The data were also analyzed
in the Arduino graph plotter where the data can be seen in form of a graph and the amount of change
happening at a particular time which is shown in Figure 6 which is smoke detection. Air quality rises from
the normal threshold which is about 450-580 PPM once the smoke is detected by the sensor. The more smoke
is present in the air determines the more PPM which can be dangerous for the user in terms of personal
healthcare.
4.2. Latency test
NodeMCU is connected with Arduino mega and transmits data once it is connected with Wi-Fi.
Latency test helps to determine how much latency is there in the system which is a system delay while
processing the data. As a result, it can be predicted whether the device will meet the criteria of low latency
and hence take steps to minimize it before losing vital data. Figure 7 shows the latency test of the hardware
and the expected time data is from 0 seconds to 1 second. Values of both real-time data are not constant
whereas, expected data is constant along with average data, and hence it can be analyzed that there is a delay
in the system of 1.18 s. Figure 8 shows the latency test of the software and its results. The range of software
is from 0 seconds to 1.4 seconds which is also real-time data and the expected time data is from 0 seconds to
0.8 seconds. Values of both real-time data are not constant and expected data is constant along with average
data and hence it can be analyzed that there is a delay in the system of about 1.7 s.

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Figure 5. Co2 Graphical testing Figure 6. Smoke detection graphical testing
Figure 7. Latency test hardware Figure 8. Latency test software
4.3. Heartrate testing
This section well illustrates the use of the max 30102 sensor for measurement of heart rate and Spo2
level in the body. The sensor is used for measuring the heart rate and Spo2 level of the user. The sensor was
programmed using Arduino software and C+ programming language. The microcontroller used to program
the sensor was NodeMCU and data was analyzed and send to the Blynk app using Wi-Fi. There was serval
testing done for measuring the accuracy and precision of the sensors used in the smart healthcare medical box
and it was compared by Samsung S10 mobile phone which is much more accurate. The values of the
obtained heart rate are shown in Figure 9. The average value of heartrate measured from Samsung s10 was
taken as the values shown by the device are not precise and accurate enough as per their information on the
Samsung website. The average value which was calculated was 83.5 BPM and the average value of smart
healthcare box was 98 BPM at the same interval giving an error of 15.5%. Data measured by smart medical
box is not accurate as the sensor used to measure the data was not industrially manufactured and it can be
improved. SPo2 is blood oxygen level measured by two different devices having 12 sample data each as
shown in Figure 10. Sample data were fluctuating for both of the devices because with average was
calculated and the final sample data was analyzed in the IoT platform. The average data of Samsung s10 was
accurate enough rather than that of smart healthcare medical box because the sensor used in getting the data
was not industrial accurate enough.
4.4. Interbeat analysis
The pulse sensor which is used to measure heart rate is maxed 30102 and the user can get the data
only by placing one finger on the sensor. From the pulse sensor, interbeat time can be calculated which is the
time required for one heartbeat. The test is done for various patients with a change in activities. Interbeat time
is calculated for two activities one is when the patient is at rest and the other one is when the patient is

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exercising. The data which was collected is for 5 different people to know the accuracy of the tests and the
error difference between interbeat actual time and detected time. The data is shown in tabulated form in
Tables 1 and 2. Interbeat is measured in milliseconds.
Figure 9. Heartrate testing plotter
Figure 10. Spo2 testing plotter
Table 1. Interbeat time at rest
No of
People
Interbeat Time
Actual (ms)
Interbeat Time
Detected (ms)
Error
Difference
Accuracy %
1 731.7 731.7 0.00 100
2 731.7 750 -18.3 97.4
3 652.1 625 27.1 95.8
4 833.3 882.35 49.05 94.1
5 769.2 750 19.2 97.5
Table 2. Interbeat time at rest
No of
People
Interbeat Time
Actual (ms)
Interbeat Time
Detected (ms)
Error
Difference
Accuracy %
1 468.7 483.8 -15.1 98.9
2 444.4 428.5 15.9 96.4
3 394.7 379.7 15 96.1
4 454.5 468.7 -14.2 96.8
5 434.7 491.8 -57.1 86.86

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4.5. GUI & GSR testing
The galvanic skin response is calculated by GSR finger sensors and is very easy to use. As the user
moves his finger, the sensor detects emotional change for the user by sweating his finger. The line displays
natural emotion and the white line indicates emotional change when the user's fingers pass. The white line
travels above a red line for a specified time, as seen in the graph, when it is above a given value of 60. The
Arduino-Software was used to program the NodeMCU ESP8266 microcontroller with the Arduino super
RFID reader that handles all sensors and displays. The heart rate is determined by max 30102 and Spo2 is
also calculated by the same oxygen level of the blood of the body. The sensor module of LM 35 sensors is
used to sensor the body temperature, which is precisely 36. Air quality which is shown in the GUI is CO2 gas
present in the air which is also measured by MQ 135 air quality sensor. The line displays natural emotion and
the white line indicates emotional change when the user's fingers pass. Figure 11 shows the testing of the
complete GUI. The white line stays above the red line for the defined time, as seen in the graph if the sweat
level moved above the value 60. There was a total so 10 samples taken while doing the final testing of the
prototype and these testing were taken as an average of the complete system.
Figure 11. Testing of complete GUI
5. CONCLUSION
This research aims to design and develop IoT based secure personal healthcare system using RFID
technology and steganography for user's better health analyses and security purposes. The proposed design
has been programmed successfully on Wi-Fi-based Arduino Mega integrated with MATLAB using two
different GUI. One GUI is used for the IoT platform which is Blynk and the data is smartly transmitted there.
Another GUI is for securing data behind an image using Steganography Technique. 1st GUI was built on
Blynk and 2nd GUI was built on MATLAB app designer. The consumer had a separate database with RFID
technology to make health assessments safer and more reliable and open only to doctors and consumer
relatives. objectives were achieved by the integration of sensors using Arduino mega and the implementation
of two-step security. The RFID was used in such a way that the user is only capable of getting the data once
they have placed their card on the reader where the data is stored in a database and IoT.
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