An Efficient Detecting System for Health Using Internet of Things
P.Selvaprasanth1, M.Arun Kumar2 & A.Manoj Prabaharan3
1,2,3Assistant Professor, Department of ECE, Sethu Institute of Technology, Tamilnadu, India.
Article Received: 07 October 2019 Article Accepted: 19 January 2020 Article Published: 09 February 2020
1. INTRODUCTION
Diseases are usually caused by certain physiological parameters of the human body, such as heart rate, oxygen
saturation, body temperature, blood pressure, etc. - associated with your changes. Diagnosis of these diseases
involves some hospital controls to measure differences in physiological parameters, and then determines the
presence or absence of these diseases. Due to recent developments in the Internet of Things and wireless sensor
networks, many attempts have been made to transmit patient data remotely without having to go to a hospital
[1]-[5]. This will help doctors / practitioners determine what action to take or refer for specific medical care. In
critical situations, the transmission of critical patient data can have a significant impact on patients' lives [6]. With
the availability of cloud computing, a paradigm shift in computing and data storage, IoT-based health monitoring
systems have found new ways to innovate. In the cloud, patient data is processed and stored. Not only can
patients' viability be monitored in real time, but they can be rescued for historical review. Storing a patient's data
in the cloud has many benefits, including availability, reliability, and convenience at a relatively low cost [7], [8].
The system proposed provides a real-time, secure solution for private health records in the cloud. IoT biosensors
are used to record key biological parameters (heart rate, oxygen saturation, and body temperature) of a
comfortable patient. The IoT-based microcontroller then processes the public cloud, encrypts it, and delivers its
secure health parameters. Patient data is protected by the AES algorithm. AES is used in the our system to protect
patient data before it is stored in the cloud. This ensures the confidentiality of the data and the secure
dissemination of patient data across public networks. Also, our system provides an alarm system by sending an
email to the parents of some patients or to a coordinator if the physiological signals are outside the normal range.
2. RELATED WORK
Recent developments are adopted in the literature of health monitoring systems. These developments span over a
wide range of technological and functional aspects. For example, MobiHealth [9] and MobiCare [10] systems
employ mobile cellular networks (GPRS and UMTS) to transmit vital signs to healthcare centers. Gupta et al. [11]
adopted IoT and microcontroller to monitor the vital signs of the patient. They considered only one perspective, A B S T R A C T
This document presents a secure health monitoring system based on the IoT which shortens the distance between a patient and the medical organization concerned. Vital signals captured by sensors are processed and encrypted using the Advanced Encryption Standard (AES) algorithm before being sent to the cloud for storage. A Node MCU microcontroller is used to perform processing and encryption functions and to provide connectivity to the cloud via Wi-Fi. In addition, a medical specialist can view private health data in real time only after providing the decryption credentials. In addition, the proposed system provides an alert by sending an e-mail to certain parents of patients or to a coordinating specialist if the vital signs are outside normal rates. The proposed system provides confidentiality, security and real-time connectivity for private health data records.
which is the ECG signal. Raspberry Pi was used to collect data from wearable sensors and send it to the MySQL
database. The authors also employed the GSM wireless network to send alert messages to healthcare centers in
emergency cases.
In [12], the authors integrated IoT and cloud computing in building ECG mobile app. Microcontroller board was
used to capture ECG signals from a patient and send it to the mobile device in a wireless manner using Bluetooth.
ECG data is saved as a binary file into an SD card of the mobile phone and the user has the ability to send this file
to the cloud to become available for specialist inspection.
In [13], the authors implemented an IoT sensing module to measure various vital signs (ECG, body temperature,
patient position). This module is connected to a local webserver via COM connection for local monitoring and can
send measurements to cloud storage for remote monitoring.
3. PROPOSED SYSTEM
The implementation of the proposed system involves a three-layer structure of different technologies collaborated
together to accomplish the system goal. The layers of the proposed system are the patient layer, the cloud layer, and
the doctor/specialist layer. The system architecture of the proposed system with three layers is shown in Fig. 1, and
described as follows.
A.Patient Layer
The patient layer consists of the patient itself, and an IoT module. The IoT module consists of a number of
biomedical sensors that measure the key vital data (i.e. heart rate, blood oxygen saturation, and body temperature),
and a WiFi-based microcontroller which processes this vital data, encrypts it using AES algorithm, and sends it
directly to the cloud database over WiFi. MAX30102 [14] is a high sensitivity pulse oximeter employed to measure
heart rate and blood oxygen saturation. as shown in Fig. 2-a. DS18B20 sensor [15] is used to measure body
temperature, as shown in Fig. 2-b. These sensors are connected to the ESP8266 NodeMCU [16],[17],[18],[19]
microcontroller which controls the whole system and provides the processing and transmission functionalities (Fig.
2-c). ESP8266 NodeMCU is an emerged IoT device with small size, low cost, self-contained WiFi module, high
processing speed, and is also capable of running self- contained applications.
B.Cloud Layer
The cloud layer is responsible for providing a safe place for private health data. Cloud receives sensitive data from
the patient layer and stores it in a ciphered form, which makes the system more robust against not only external
attacks but also internal attacks that can be initiated by the cloud service provider itself. The cloud layer is not
charged in any processing on data but delivers data as it is to the next layer.
C.Doctor/Specialist Layer
This layer enables specialists at trusted healthcare centers to monitor and track the patient’s vital data in real-time.
This enables specialists to predict any unusual activity and can assign precautions to prevent any emergency case. A
First, specialists should login via a web interface to be authenticated and prevent fraud access, and then they are
directed to the monitoring dashboard. The web interface is developed using HTML5, JavaScript, BootStrap, and
ASP.NET.
Fig.1. Architecture of the proposed system
Fig.2.: (a) MAX30102 sensor, (b) DS18B20 sensor, and (c) ESP8266 NodeMCU WiFi Devkit
Although there are plenty of researches and papers on the topic of health monitoring, our research, unlike most
monitoring systems, added some key contributions in the field that are:
1) The proposed system relies on a WiFi-based connection, which provides fast communication between the patient
module and doctor module with low power consumption compared to other technologies such as GPS, ZigBee, or
GSM technologies. 2) It encrypts patient vital signs before sending away to the cloud, which provides a high level of
security of information, and keeps privacy for the patient itself. 3) Also, it provides email-based notifications sent
to those related to the patient in emergency cases.
4. SYSTEM IMPLEMENTATION
This section presents the development and hardware implementation of the proposed remote health monitoring
system. Figure 3 shows the hardware implementation of IoT module with relevant sensors and microcontroller are
connected together. The complete system flowchart is depicted in Fig. 4. Basic actors and their functionalities are
described as follows:
A.Sensor Module
This module involves capturing raw physiological data from a patient body and sending this data to a Micro
Controller Unit (MCU) for processing. This module comprises two sensor types: pulse sensor and body
temperature sensor. The MAX30102 pulse oximeter sensor, as shown in Fig. 2-a, is involved in this study to
measure heart rate and blood oxygen saturation (SPO2). The MAX30102 includes internal LEDs, photodetectors,
optical elements, and low-noise electronics with ambient light cancellation. DS18B20 temperature sensor, shown
in Fig. 2- b, is used to sense the skin temperature of a patient. The DS18B20 digital thermometer provides 9-bit to
12-bit precision for Celsius temperature measurements.
B.IoT Module
IoT module is the coordinator of the whole patient layer. Process flow along this module includes the following
steps:
1) Receiving raw physiological data from sensors through an appropriate interface (I2C or 1-Wire).
2) Processing received data and converting it into numerical values (heart rate, blood oxygen saturation,
and body temperature).
3) Encrypting vital signs using the AES algorithm with a 128-bit key.
4) Establishing a connection to the cloud database over a WiFi connection.
5) Sending ciphered data to cloud storage.
6) Sending an alert email message in emergency cases or some vital data outside the normal range.
These tasks are accomplished using the ESP8266 NodeMCU developing kit, shown in Fig. 2-c. NodeMCU is an
Arduino-like board with extra beneficial features, such as 802.11 b/g/n WiFi support, integrated TCP/IP protocol
stack, 3.3v operating voltage, low current consumption (10µA~170mA), attachable flash memory (16MB), and
high processor speed (80~160MHz). NodeMCU is programmed using open-source Arduino IDE 1.8.5 in order to
accomplish its commissioned tasks.
C.Cloud
Cloud is the place where patient data is stored. It provides a means of transportation for patient data from patient
layer to medical organization so that a specialist can access and diagnose patient vital signs from anywhere at
D.Hospital Local Server:
This entity is responsible for receiving data from cloud storage, decrypting it with the appropriate decryption key,
and then delivering it to the doctor terminal. It also holds a SQL database comprising a table for patient
information, and another table for login credentials in order to control access to the system and provide
authorization for users according to granted permissions.
E.Doctor Terminal
It is the last destination of patient data where vital data of patients are examined by a specialist to determine any
health issues associated with this data and can assign precautions to prevent any emergency case. First, the
specialist is asked to provide his credentials to determine his roles, and after that, he can proceed to the monitoring
dashboard to view and interact with patient data in real-time. The monitoring dashboard is updated automatically
with every update in the cloud database.
Fig.4. System development flowchart
5. EXPERIMENTAL RESULTS
This section presents the experimental results of the developed Secure Health Monitoring System using IoT and
Cloud Computing.
The proposed system provides a way to keep an eye on key biological indicators of a patient in a secure and real-
time basis. First, IoT biosensors are used to capture key biological parameters from a patient. Then, an IoT-based
microcontroller processes, encrypts, and delivers it to the cloud. Moreover, only patient relatives or specialists at
decryption credentials. Securing patient data ensures data privacy and secure distribution of patient data in public
networks. In addition, the proposed system provides an alert by sending an email to some patient relatives or
coordinating doctor if vital signs are outside of normal rates.
Monitoring dashboard, shown in Fig. 5, reveals patient vital data displayed in real-time for each physiological
parameter. Figure 6 shows an alert message sent to a patient relative from the proposed system indicating that
monitored patient is in an emergency case.
Fig.5. Monitoring dashboard
Fig.6. Alert message sent indicating that monitored patient is in emergency case
6. CONCLUSION
Health monitoring systems play a key role in health and early warning of health-related issues. In
addition, these systems reduce medical costs in terms of periodic hospital check-ups and medical visits.
data from a patient's location to a relative or doctor. This article introduces a secure, inexpensive and
reliable health monitoring system that provides real-time monitoring dashboards for biological
indicators in a secure environment using IoT technology and cloud computing. The proposed system
included the AES algorithm to encrypt the vital signals occupied by the sensors before sending them to
the cloud for storage. The Node MCU microcontroller is used to perform processing and encryption
functions and to connect to the cloud over WiFi. In addition, the proposed system provides an alert
when sending e-mails to individual patient relatives or the coordinator when physiological signals are
outside normal speed.
REFERENCES
1. Karthick, R and Sundararajan, M: “A Reconfigurable Method for Time Correlated Mimo Channels with a Decision Feedback Receiver,” International Journal of Applied Engineering Research 12 (2017)
5234.
2. Karthick, R and Sundararajan, M: “PSO based out-of-order (ooo) execution scheme for
HT-MPSOC”Journal of Advanced Research in Dynamical and Control Systems 9 (2017) 1969.
3. Karthick, R and Sundararajan, M: “Design and Implementation of Low Power Testing Using
Advanced Razor Based Processor,” International Journal of Applied Engineering Research 12 (2017)
6384.
4. Karthick, R and Sundararajan, M: “A novel 3-D-IC test architecture-a review,” International Journal
of Engineering and Technology (UAE)7 (2018) 582.
5. R.Karthick, P Selvaprasanth, A ManojPrabaharan, “Integrated System For Regional Navigator And Seasons Management,” Journal of Global Research in Computer Science 9(4),2018(11-15).
6. Karthick, R and Prabaharan, A.Manoj and Selvaprasanth, P. and Sathiyanathan, N. and Nagaraj, A.,
High Resolution Image Scaling Using Fuzzy Based FPGA Implementation (March 15, 2019). Asian
Journal of Applied Science and Technology (AJAST), Volume 3, Issue 1, Pages 215-221, Jan-March
2019 . Available at SSRN: https://ssrn.com/abstract=3353627
7. Karthick, R and Sundararajan, M., Hardware Evaluation of Second Round SHA-3 Candidates Using
FPGA (April 2, 2014). International Journal of Advanced Research in Computer Science &
Technology (IJARCST 2014), Vol. 2, Issue 2, Ver. 3 (April - June 2014). Available at SSRN:
https://ssrn.com/abstract=3345417.
8. Karthick, R and and Prabaharan, A.Manoj and Selvaprasanth, P.,Internet of Things based High
Security Border Surveillance Strategy (May 24, 2019). Asian Journal of Applied Science and
Technology (AJAST), Volume 3, Issue 2, Pages 94-100, Apr-June 2019. Available at SSRN:
9. Karthick, R and Sundararajan, M: “SPIDER based out-of-order (ooo) execution scheme for
HT-MPSOC” International Journal of Advanced Intelligence paradigms, In Press.
10. Karthick, R and John Pragasam, D “Design of Low Power MPSoC Architecture using DR Method”
Asian Journal of Applied Science and Technology (AJAST) Volume 3, Issue 2, Pages 101-104, April
-June 2019.
11. Karthick, R and Sundararajan, M., Optimization of MIMO Channels Using an Adaptive LPC
Method (February 2, 2018). International Journal of Pure and Applied Mathematics, Volume 118 No.
10 2018, 131-135. Available at SSRN: https://ssrn.com/abstract=3392104
12. Karthick, R and Rinoj, B. Micheal Vinoline and Kumar, T. Venish and Prabaharan, A.Manoj and
Selvaprasanth, P., Automated Health Monitoring System for Premature Fetus (July 27, 2019). Asian
Journal of Applied Science and Technology (AJAST) (Peer Reviewed Quarterly International Journal)
Volume 3, Issue 3, Pages 17-23, July -September 2019. Available at SSRN:
https://ssrn.com/abstract=3427756
13. Karthick, R., “Deep Learning For Age Group Classification System”, International Journal Of
Advances In Signal And Image Sciences. Volume 4, Issue 2, Pages 16-22, 2018.
14. R. Karthick, N.Sathiyanathan, “Medical Image Compression Using View Compensated Wavelet Transform” Journal of Global Research in Computer Science 9(9), 2018(1-4).
15. P. Meenalochini and S. P. Umayal ,Comparison of Current Controllers on Photo Voltaic Inverters
Operating as VAR Compensators, Journal of Electrical Engineering The Institution of Engineers,
Bangladesh Vol. EE 38, No. I, June, 2012.
16. R. Karthick, P. Meenalochini, A. Manoj Prabaharan, P.Selvaprasanth, M.Sheik Dawood, “A
Dumb-Bell Shaped Damper with Magnetic Absorber using Ferrofluids” International Journal of
Recent Technology and Engineering 8(4S2), 2019(317-320).
17. R.Karthick, R. Senthamil Selvan ,R. S. D. Wahidabanu , B. Karthick, M. Sriram , “Development of Secure Transport System Using VANET” Test Engineering and Management, Vol.82(Jan-Feb 2020),
2020(2073 – 2078).
18.N. Sathiyanathan, Selvakumar. S, P. Selvaprasanth, “A Brief Study on IoT Applications”
,International Journal of Trend in Scientific Research and Development (IJTSRD) Volume 4 Issue 2,
February 2020 Available Online: www.ijtsrd.com e-ISSN: 2456 – 6470 @ IJTSRD .
19. N.Sathiyanathan S.Selvakumar & P.Selvaprasanth, “Ultimate Benefits of US-Biopsy Vs Guided
Mammography Biopsy for Possible Mini-Analysis without a Specific Breast Mass”, Asian Journal of
