NextGen Patient Care - Unleashing IoT for Health

Monitoring
A PROJECT REPORT

Submitted by

BL.EN.U4CSE20072 K.SAAITEZA REDDY

BACHELOR OF TECHNOLOGY

IN

COMPUTER SCIENCE AND ENGINEERING

AMRITA SCHOOL OF COMPUTING, BENGALURU

AMRITA VISHWA VIDYAPEETHAM

BENGALURU 560 035

MAY 2024
 AMRITA VISHWA VIDYAPEETHAM

AMRITA SCHOOL OF COMPUTING, BENGALURU, 560035

BONAFIDE CERTIFICATE
This is to certify that the project report entitled “NextGen Patient Care -
Unleashing IoT for Health Monitoring” submitted by

BL.EN.U4CSE20072 K.SAAITEZA REDDY

in partial fulfillment of the requirements as part of Bachelor of Technology

in “COMPUTER SCIENCE AND ENGINEERING” is a Bonafide record of the
work carried out under my guidance and supervision at Amrita School of Computing,
Bengaluru.

Mr. Niranjan D K Dr. Gopalakrishnan E.A

Faculty Associate Chairperson
Dept. of CSE, School of Computing Dept. of CSE, School of Computing

This project report was evaluated by us on ………

Internal Examiner 1 Internal Examiner 2 External Examiner

Dept. of CSE, ASC, Bangalore

 ACKNOWLEDGEMENTS
The satisfaction that accompanies successful completion of any task would be
incomplete without mention of people who made it possible, and whose constant
encouragement and guidance have been source of inspiration throughout the course
of this project work.

I offer my sincere pranams at the lotus feet of “AMMA”, MATA

AMRITANANDAMAYI DEVI who showered her blessing upon me throughout
the course of this project work.

I owe my gratitude to Prof. Manoj P., Director, Amrita Vishwa

Vidyapeetham Bengaluru Campus. I would like to express my heartfelt gratitude to
Dr. Gopalakrishnan E.A., Principal, Amrita School of Computing, Bengaluru for
his valuable support and inspiration.

It is a great pleasure to express my gratitude and indebtedness to my project

guide Niranjan D K Faculty Associate, Department of Computer Science and
Engineering, Amrita School of Computing, Bengaluru for his valuable guidance,
encouragement, moral support, and affection throughout the project work.

I would like to thank the project panel members for their suggestions,
encouragement, and moral support during the process of project work and all faculty
members for their academic support. Finally, I am forever grateful to my parents,
who have loved, supported and encouraged me in all my endeavors.

I
ABSTRACT MAY 2024

ABSTRACT
In order to improve patient care, advanced technologies have been discovered as a
result of increasing demand for effective emergency health care services.
This study focuses on creating a smart system to keep track of people's health using
IoT technology. We'll use devices like ESP8266 and Arduino to monitor real-time
health data, like temperature and pulse rate. All this information will be securely
stored in the Power Bi, making it easy to access and keeping it private.
Their medical history will be collected and safely stored in the Power Bi.
Then, using ESP8266, we'll build a simple device that measures an individual's
temperature, ECG, and pulse rate. The collected data will be transferred to a Power
Bi for storage and analysis. The system will continuously analyze the health data and
compare it with previous records to identify any odd changes or possible health
issues.
After setting up the IoT device, we'll test it by checking the temperature, ECG
and pulse rate of a few people and comparing the results with their past health data
stored in the cloud. This will help us identify any early changes or health problems.
Our system consists of small devices to measure health, and a way to send this
data. We make sure that the health information is kept private and follows all the
rules to protect sensitive data.
Overall, this project aims to create a simple and effective way to monitor
health using IoT, making it easier for people and doctors to work together for better
health outcomes.

II
TABLE OF CONTENT MAY 2024

TABLE OF CONTENTS

Page No.

ACKNOWLEDGEMENTS i
ABSTRACT ii
LIST OF FIGURES iv
CHAPTER 1- INTRODUCTION 1-5
1.1 INTRODUCTION 1-5
1.2 MOTIVATION 5
CHAPTER 2 – LITERATURE SURVEY 6 - 12
2.1 LITERATURE REVIEW 6 - 11
2.2 SUMMARY OF LITERATURE 12
CHAPTER 3 – SYSTEM REQUIREMENTS AND ANALYSIS 13 - 17
3.1 SOFTWARE REQUIREMENTS 13 - 14
3.2 HARDWARE REQUIREMENTS 14 - 17
CHAPTER 4 – SYSTEM DESIGN 18 - 22
4.1 HIGH-LEVEL DESIGN 18 - 19
4.2 LOW-LEVEL DESIGN 19 - 22
CHAPTER 5 – SYSTEM IMPLEMENTATION 23 - 29
5.1 MODULES USED WITH DESCRIPTION 23 - 25
5.2 ALGORITHMS USED WITH DESCRIPTION 25 - 29
5.2.1 UNO Code 25 - 27
5.2.2 NODE MCU CODE 27 - 28
5.2.3 RANDOM FOREST CODE 29
CHAPTER 6 – SYSTEM TESTING 30 - 35

CHAPTER 7 – RESULTS AND ANALYSIS 36 - 38

7.1 RESULTS AND ANALYSIS 36 - 37
7.2 CHALLENGES 37

III
TABLE OF CONTENT MAY 2024

7.3 SYSTEM SETUP 38

CHAPTER 8 – CONCLUSION AND FUTURE ENHANCEMENT 39 - 40
8.1 CONCLUSION 39
8.2 FUTURE WORK 39 - 40
REFERENCES 41 – 42

IV
LIST OF FIGURES MAY 2024

LIST OF FIGURES
Fig 1.1 IoT patient-based health monitoring system 2

Fig 2.1 IOT based patient checking framework. 6

Fig 2.2 Block diagram of IOT based health monitoring system 7

Fig 2.3 Remote Patient Health Monitoring Platform 8

Fig 2.4 Hospital Environment using IoT 9

Fig 2.5 The art framework/architecture for IoT-based smart and 10

ubiquitous healthcare.

Fig 2.6 IOT based coma patient checking framework. 11

Fig 3.1 Arduino 14

Fig 3.2 Node MCU ESP8266 16

Fig 3.3 Pulse Sensor 16

Fig 3.4 DHT11 Sensor 17

Fig 3.5 I2C OLED Display 17

Fig 4.1 System Architecture 18

Fig 4.2 OLED to Arduino board 19

Fig 4.3 DHT11 to Arduino board 20

Fig 4.4 Pulse to Arduino board 20

Fig 4.5 ECG to Arduino board 21

Fig 6.1 pulse, temperature, humidity, and ECG values. 29

Fig 6.2 Sensor’s data are being sent to ThingSpeak. 29

Fig 6.3 CSV file download. 30

V
LIST OF FIGURES MAY 2024

Fig 6.4 Random Forest Model Result 31

Fig 6.5 SVM Model Result 31

Fig 6.6 KNN Model Result 32

Fig 6.7 Patient data downloaded from thingspeak and visuals from 33
excel.

Fig 6.8 Visuals of Patient data in Power BI 34

Fig 7.3 Patient Health Monitoring System 37

IV
INTRODUCTION MAY 2024

CHAPTER - 1
INTRODUCTION
In today's fast changing healthcare environment, where technological developments
are changing every aspect of patient care, the implementation of modern technologies
stands out as a sign of hope for improving health outcomes and boosting patient
experience. Among these advancements, the Internet of Things (IoT) stands out as a
game changer, promising an evolution in how we monitor, manage, and deliver
healthcare services.

The Internet of Things is basically a large network of interconnected devices, sensors,

and systems that work together to gather, analyze, and share data in real time. This
integrated ecosystem has the potential to transform healthcare delivery by giving
healthcare professionals and patients legendary access to rapid, accurate, and
actionable health information.

Our initiative, aptly named "NextGen Patient Care - Unleashing IoT for Health
Monitoring," highlights IoT's transformative power in healthcare. By welcoming IoT
technologies, we hope to bring in a new era of patient care defined by continuous
monitoring, early detection, and unique treatment options.

In the old way of doing things in healthcare, patients usually wait until they feel sick,
or their conditions get bad before they seek help. This puts a lot of pressure on
hospitals and clinics and makes it harder to treat them effectively.

However, we now have the chance to overcome these challenges and move toward a
more proactive and patient-focused style of treatment thanks to the development of
IoT-enabled healthcare solutions. Patients are given the ability to take part in the
management of their own health and well-being using a variety of networked devices
and sensors that can monitor many health metrics in real-time.

Additionally, IoT-enabled healthcare systems may make it easier for medical

professionals to collaborate and communicate with one another, resulting in the

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delivery of more integrated and planned treatment. The opportunities that the Internet
of Things (IoT) offers the healthcare industry are almost endless, covering everything
from predictive analytics and preventive treatments to remote patient monitoring.

With our project, we hope to fully utilize the Internet of Things to not only monitor
and control health but also to completely change the healthcare experience for
everyone involved—providers, patients, and caregivers. We are setting off on a
journey towards a future where healthcare is not just about treating illness but also
about empowering people to live healthier, more fulfilling lives by accepting
innovation and the promise of IoT.

Fig 1.1. IoT patient-based health monitoring system

The smooth integration of hardware and software components, each of which is

essential to the collecting, sending, and analysis of health data, is the basis of our
project. Every component—from advanced algorithms for data processing to sensors

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INTRODUCTION MAY 2024

that can monitor vital signs—combines to form an equilibrium that enables people to
take greater control of their health.

Moreover, the whole healthcare cycle may benefit from increased efficiency and
effectiveness thanks to IoT-enabled medical technologies. IoT data can provide
valuable insights into various aspects of patient care, such as treatment plan
improvement and diagnostic process efficiency. This can ultimately lead to improved
patient satisfaction and better outcomes.

Utilizing Power BI, a powerful and approachable data visualization platform that
forms the basis of our IoT-driven healthcare solution, is at the foundation of our
project. By converting raw data into useful insights that help with well-informed
decision-making, Power BI enables us to fully utilize the amazing potential of data.

Through the utilization of Power BI, we may easily import and examine historical
medical records, leading to a whole comprehension of a person's medical history.
With the use of this comprehensive view, we can spot trends, patterns, and possible
risk factors that could otherwise go neglected, enabling us to execute specific
measures and targeted preventative measures.

We may present complicated health data in an understandable manner by using

visualizations like charts, graphs, and dashboards. In addition to improving
comprehension, this gives patients the confidence to take an active role in their
treatment process. We promote an active wellness management culture by giving
patients clear visual representations of their health state and changes.

Furthermore, Power BI's interactive features enable real-time monitoring and analysis
of health data, allowing for immediate actions and necessary changes to treatment
plans. Better health outcomes are the ultimate result of this flexible approach to
healthcare, which gives patients and healthcare professionals the opportunity to make
decisions based on the latest information.

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Essentially, Power BI is an effective instrument for turning data into information that
can be used to improve health. By utilizing this platform to its fullest, we enable
patients to take charge of their health and wellbeing and give medical professionals
with the resources they need to provide individualized, high-quality care.

A carefully designed network of hardware components, each carefully chosen to

fulfill its unique purpose in gathering and sending crucial health information, forms
the foundation of our Internet of things environment. Our solution is centered upon
the Arduino UNO, which facilitates smooth connection between the Power BI
platform and the many sensors. When combined with the popular C++ programming
language and the Arduino Integrated Development Environment (IDE), our gadget
provides effective real-time data processing and analysis, enabling rapid decision-
making and intervention.

Comprehensive health monitoring is made possible using sensors like the DHT
Temperature Sensor and Pulse Sensor, which allow us to record essential
physiological parameters accurately and precisely. The DHT Temperature Sensor
captures ambient temperature to ensure environmental factors are considered in our
research, while the Pulse Sensor measures heart rate and blood oxygen levels,
offering essential information into cardiovascular health. Together, these sensors
offer a comprehensive picture of a person's health status, facilitating early detection
of problems and preventive measures.

Additionally, the addition of an OLED display gives our system a new level of user
involvement. With the help of the OLED Display's simple visual indicators, users can
keep an eye on their health in real time and get alerts when there are changes from the
usual. Our device's benefit is improved by its user-friendly interface, which
encourages people to actively participate in their healthcare journey and make well-
informed decisions.

In the end, our project signifies a paradigm change in the way we see the provision of
healthcare. By utilizing the potential of IoT technology, we may encourage people to

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INTRODUCTION MAY 2024

actively manage their health and well-being and enhance the quality of treatment. Our
proactive measures and data-driven decision-making are establishing the way for a
future in which healthcare is accessible, efficient, and personalized for everybody.

1.1Motivation
In the airline's Gurugram office, a 37-year-old Air India pilot passed away from a
heart attack. He was taken to a nearby hospital and given CPR, but he was not able to
be saved.

Patient health monitoring systems are becoming more and more important. This is
demonstrated by the many cases in which early indicators of medical concern were
missed, resulting in life-threatening situations or even deaths. The tragic event of an
Air India employee who, despite having passed all previous medical examinations,
developed an unexpected health problem that ultimately proved fatal serves as a
moving illustration of this necessity. This incident, which happened during a regular
task at his place of employment, serves as an important example of the sudden onset
of serious health crises in otherwise apparently healthy people.

Therefore, the primary motivation behind this project is the use of technology to
develop a strong health monitoring system that can significantly decrease the number
of medical emergencies and enhance people's general safety and wellbeing, especially
in situations where there is a lot of stress and a plenty on the line.

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LITERATURE REVIEW MAY 2024

CHAPTER - 2

LITERATURE REVIEW
[1] The proposed system makes use of sensors, microcontrollers, and Internet of
Things technologies to send real-time patient data to a medical server, enabling
healthcare professionals to diagnose patients remotely. Real Time data transmitted to
server.

Fig 2.1. IOT based patient checking framework.

The aim of this project is to create an effective patient health monitoring system by
using the latest developments in sensor, microcontroller, and Internet of Things (IoT)
technology. The main objective is to enable the real-time transfer of patient data to
medical servers so that healthcare professionals may monitor patients from a distance
and react quickly if their health worsens.

[2] represents the IoT-based healthcare system to improve patient care, reduce
healthcare costs, and provide real-time online patient condition updates, thereby
encouraging early medical treatments and better quality of care. The monitoring of
essential parameters and the alerting of doctors if any parameter exceeds threshold
values.

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Through constant monitoring of critical health metrics like blood pressure, oxygen
saturation, body temperature, and heart rate, the Internet of Things system makes sure
that problems are found as soon as possible. With real-time medical care adjustments
based on the most recent data, this continuous monitoring allows a more flexible and
adaptable approach to patient care, resulting in more personalized and efficient
healthcare solutions.

Fig 2.2. Block diagram of IOT based health monitoring system.

One of the most important features in avoiding any health emergencies is the system's
capacity to notify medical professionals when specific parameters cross pre-
established limits. These alerts can be set up to take a variety of actions, such as
immediately notifying a doctor's mobile device or connecting with emergency
services to enable fast reaction and ensure that patients receive the care they require.

[3] focuses on the continuous remote monitoring of critical health metrics, the
suggested system uses Raspberry Pi, Internet of Things (IoT) devices, and cloud
platforms to improve patient care, increase efficiency, and transform healthcare
delivery.

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The paper highlights the critical need to include modern digital health technology,
including remote monitoring, as brought to light by the Covid-19 outbreak. It
promotes the use of wearable sensors as necessary instruments for real-time patient
monitoring, with cloud based IoT systems making access to data possible.

Fig 2.3. Remote Patient Health Monitoring Platform

The suggested approach makes use of the Internet of Things to link wearable sensors
to cloud servers, allowing the smooth transfer of medical information for remote
health monitoring. A complete smart healthcare monitoring system with cloud
storage, IoT servers, embedded sensors, and communication channels is described in
this paper. This method intends to improve patient care, lower problems, and provide
healthcare decision-makers with current information through continuous remote
monitoring made possible by Raspberry Pi, IoT devices, and cloud platforms. In
doing so, it will change the way healthcare is delivered.

[4] presents an overview of measuring vital signs including temperature, heart rate,
and ECG using sensors and microcontrollers, it fulfills the requirement for continuous
patient monitoring, particularly for individuals who are at home.

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Fig 2.4. Hospital Environment using IoT.

This rIoT.arch presents an innovative approach using an Internet of Things-based

Patient Health Monitoring system to address the significant problem of unexpected
health crises, particularly among older patients. The device seeks to track patients'
health parameters in real time, including their temperature and heart rate, by using
sensor technology and internet access. The device, which is equipped with an
Arduino Uno and a Wi-Fi connection, guarantees ongoing patient health tracking, and
sends alarms when any irregularities are found. The suggested method allows for
remote monitoring, giving loved ones and caregivers peace of mind and maybe
saving lives by enabling quick medical action.

[5] explains architecture of an Internet of Things-based health care management

system (HCMS) is covered, including Internet of Things medical devices, IoT
internet services, and data management. The paper highlights the advantages of IoT
in healthcare, including remote patient monitoring, better diagnosis, and cost savings,
through a thorough evaluation of related works.

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Fig 2.5. The art framework/architecture for IoT-based smart and ubiquitous healthcare.

The Internet of Things (IoT) applications in healthcare are a quickly growing sector,
and e-health monitoring systems are the main topic of this study. IoT makes it
possible to continuously monitor patients' physical and mental health by utilizing
networked devices like wearable sensors and remote gadgets. The main goal is to
enable medical professionals to remotely monitor patients by automating medication
distribution based on patient conditions.

The study uses a descriptive research approach to carefully examine current research
and highlight the potential of IoT in improving medical care services. This includes
precise diagnosis, lowered hospitalization rates, and enhanced data accuracy for
scientific research. The evaluation highlights the benefits and drawbacks of Internet-
based healthcare monitoring systems while showing how essential they are to the
transformation of normal medical procedures.

[6] presents an overview of sensors such as temperature, heartbeat, eye blink, and
SPO2
The suggested system improves patient health care and lowers medical costs by
enabling rapid identification of problems.

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With the aim of greatly improving patient care and possibly saving lives through
early identification of health concerns, this research suggests an Internet of Things-
based method for continuously monitoring the health parameters of coma patients.

Fig 2.6.IOT based coma patient checking framework.

The system uses smart sensors to track vital indications like oxygen saturation levels,
heart rate, eye movement, and body temperature. These sensors include temperature,
heartbeat, eye blink, and SPO2 sensors. The system makes use of the Arduino Uno
board as a microcontroller and cloud computing to enable authorized users' laptops
and cell phones to receive data through a cloud server.

The potential of the Internet of Things to completely transform coma patient

healthcare monitoring can be seen collected data is saved and analysed for further
analysis and decision-making.

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2.1 Summary

S.No. Gaps

1 Data security and privacy precautions, which are essential when

handling sensitive patient health data transferred over the internet. The
paper's lack of a comparison between recent and historical medical
data may make it more difficult to identify patient issues early on.

2 In scenarios with unstable Wi-Fi connectivity, the device ability to

monitor in real-time may be put at risk, which could cause delays in
the delivery of vital patient data to medical experts.

3 If previous medical records are not compared with current patient data,
significant patterns, changes, or abnormalities that may indicate the
beginning of a possible health problem may go undetected.

4 Transmitting critical patient data to healthcare professionals may be

interrupted or delayed in locations with poor or unstable Wi-Fi service.
This may influence timely patient monitoring and treatment,
particularly in urgent cases requiring emergency medical assistance.

5 The paper acknowledges some challenges such as data privacy and

interoperability issues. It can't sufficiently examine potential hazards
like data breaches, vulnerabilities in security, or ethical problems
related to IoT devices collecting private medical information.

6 The system may encounter problems transferring patient data in some

places with unstable network coverage or during network failures,
which could cause interruptions or delays in monitoring and alerting.

CHAPTER – 3

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SYSTEM SPECIFICATIONS

3.1 Software requirements

Windows 11:
Microsoft's Windows OS (Operating System) family of software runs on a range of
gadgets, including desktops, laptops, tablets, and smartphones. The newest version is
Windows 10, which was made available in July 2015. A graphical user interface, a
web browser, a collection of apps, and support for hardware like keyboards, mouse,
and gaming controllers are all included in the Windows OS. Additionally, the OS
comes with several essential functions including networking, security, and system
administration.

Arduino IDE:
The Arduino Integrated Development Environment (IDE) is an open-source software
platform used for developing code for Arduino microcontroller boards. The IDE
provides a user-friendly interface for writing, compiling, and uploading code to an
Arduino board.
The Arduino IDE supports a simplified programming language based on C/C++,
which makes it accessible even to beginners with little or no programming
experience. It also provides a library of pre-written code, called "sketches," that can
be used as building blocks for creating more complex programs.
The IDE includes a text editor with features like syntax highlighting, auto-
completion, and error highlighting, making it easier for developers to write and debug
code. It also includes a serial monitor for viewing and sending data between the
computer and the Arduino board, which is particularly useful for debugging.

Power BI:

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Microsoft created Power BI, a powerful business analytics tool that enables users to
see and analyse data from a variety of sources interactively and easily. Data-driven
decision-making is made easier for enterprises by enabling them to translate raw data
into meaningful and visually appealing reports and dashboards. Many functions are
available with Power BI, including collaboration tools, data modelling, data
networking, data preparation, and visualization. Power BI's powerful features and
user-friendly interface enable users at all organizational levels to get important
insights from their data, allowing them to spot patterns, recognize trends, and make
well-informed decisions that boost company success.

3.2 Hardware requirements

Arduino UNO:
The Arduino UNO is a popular microcontroller board that is widely used in
electronics and robotics projects. It is based on the Atmel ATmega328P
microcontroller and features 14 digital input/output pins, 6 analog inputs, a 16 MHz
quartz crystal, a USB connection, a power jack, and an ICSP header.
The digital I/O pins on the Arduino UNO can be configured as either input or output
pins and can be used to interface with various electronic components like sensors,
LEDs, and motors. The analog inputs on the board can be used to read analog signals
from sensors and other devices.

Fig 3.1. Arduino

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The board is powered either through the USB connection or through an external
power supply connected to the power jack. The ICSP header can be used for
programming the microcontroller using an external programmer, if needed and
compatible with the Arduino IDE.

Jumper Wires:
Jumper wires are a type of electronic wiring commonly used in prototyping and bread
boarding circuits. They are typically made of flexible, insulated wire with pins or
connectors on either end to easily connect electronic components together.

Bread Board:
A breadboard is a type of prototyping board used for designing and testing electronic
circuits. It is a reusable board that allows electronic components to be easily
connected without the need for soldering. A typical breadboard consists of a plastic
base with a series of interconnected metal strips or holes. The holes or strips are
arranged in a grid pattern, with rows and columns marked by different colors to aid in
component placement and wiring.
Electronic components such as resistors, capacitors, transistors, and ICs can be easily
inserted into the holes on the breadboard, and then connected using jumper wires.
The interconnected strips or holes on the breadboard allow components to be easily
connected without the need for soldering.
Components can be easily inserted and removed without any permanent alterations,
making breadboards highly reusable.
Wonderful for quick experiments and modifications because soldering is not
required, which reduces off setup time and makes modifications easier.
Supports a wide range of components, from resistors and capacitors to integrated
circuits and microcontrollers.

Node MCU ESP8266:

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Fig 3.2. Node MCU ESP8266

ESP8266 microcontroller-based Node MCU is a widely used development board for
a variety of Internet of Things (IoT) applications. It is perfect for connecting devices
to the internet and allowing communication between them because it has built-in Wi-
Fi features.

Pulse Sensor:
A pulse sensor is a type of sensor used to detect and measure a person's heart rate in
real-time. It works by detecting the changes in blood volume in the body caused by
the beating of the heart.

Fig 3.3. Pulse Sensor

DHT11 Sensor:
The DHT11 sensor is a low-cost digital temperature and humidity sensor. It is

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commonly used in various DIY electronics projects and IoT (Internet of Things)
applications due to its simplicity and affordability.
The DHT11 sensor can measure both temperature and humidity levels in the
surrounding environment.

Fig 3.4. DHT11 Sensor

I2C OLED Display:

For electronics projects, the 1.3-inch I2C OLED display module is a flexible and
easy-to-use display option that provides excellent resolution, low power
consumption, and simple integration using the I2C interface. It can be applied to
several things, including wearable devices, Internet of Things projects, portable
instruments, and more.

Fig 3.5. I2C OLED Display

CHAPTER - 4

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SYSTEM DESIGN

4.1 HIGH LEVEL DESIGN

Fi
g 4.1. System Architecture

● Each sensor (pulse sensor, temperature sensor, and ECG sensor) is connected

to the Arduino Uno.

● The Arduino Uno reads sensor data from its input pins.

● These sensors collect physiological data: heart rate, body temperature, and

electrocardiogram signals respectively.

● The Arduino Uno sends the processed sensor data to the NodeMCU.

● This communication occurs through the digital pins 5 and 6 on the Arduino

Uno, which are connected to the NodeMCU's RX and TX pins, respectively.

● The NodeMCU receives sensor data from the Arduino Uno.

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● The NodeMCU, equipped with Wi-Fi capability, connects to the Internet.

● It sends the collected sensor data to a ThingSpeak channel using HTTP

protocols.

● Data is stored in ThingSpeak, which also provides initial real-time

visualization and analysis features.

● Data stored in ThingSpeak is exported manually as a CSV file.

● This file contains timestamped entries of the sensor data, allowing for past

data analysis.

● The CSV file exported from ThingSpeak is uploaded into Microsoft Power

BI.

● Power BI handles data processing and modeling so that the visualization tools

can be customized to fulfill certain analytical requirements or to detect

patterns.

● Predictive modeling, correlation plots, and time series analysis are some of

the advanced data analytics features offered by Power BI.

● The data visualizations allow users to explore and dig deeper into time periods
or events.

4.2 LOW LEVEL DESIGN

OLED to Arduino UNO:

● Connect GND of the OLED to analog pin GND of the Arduino board.

● Connect Vcc of the OLED to 3.3V to the Node MCU board.

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● Connect the SDA of the OLED to the SDA of the Arduino board.

● Connect SCL of the OLED to the SCL of the Arduino board.

Fig 4.2. OLED to Arduino board

DHT11 to Arduino UNO:

● Connect +ve of the temperature to the 5V of the Arduino board.

● Connect -ve of the temperature to the GND of the Arduino board.

● Connect OUT of the temperature to the Digital D4 of the Arduino board.

Fig 4.3. DHT11 to Arduino board

Pulse to Arduino UNO:

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● Connect +ve of the Pulse to the 5V of the Arduino board.

● Connect -ve of the Pulse to the GND of the Arduino board.

● Connect Signal of the Pulse to the Analog A0 of the Arduino board.

Fig 4.4. Pulse to Arduino board.

ECG to Arduino UNO:

● Connect GND of the ECG to the GND of the Node MCU.

● Connect 3.3V of the ECG to the 3.3V of the Arduino board.

● Connect OUTPUT of the ECG to the Analog A1 of the Arduino board

● Connect LO- of the ECG to the Digital 11 of the Arduino board.

● Connect LO+ of the ECG to the Digital 10 of the Arduino board.

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Fig 4.5. ECG to Arduino board

CHAPTER – 5
SYSTEM IMPLEMENTATION

5.1 Modules used with description:

Modules used in our project:

1. LiquidCrystal_I2C

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2. DHT11
3. SoftwareSerial
4. Wire
5. Adafruit Sensor

LiquidCrystal_I2C:

Liquid Crystal is a commonly used header file in the Arduino libraries that can be
used to control different types of Liquid Crystal Display modules. In terms of
Arduino, the Liquid_Crystal module is a convenient tool as it is used for handling
Liquid Crystal Displays (LCDs) via the I2C communication protocol. Because of
these functions it provides for purposes of initializing and controlling the display,
interfacing with LCD modules has been simplified using Liquid Crystal. The library
allows users to change the cursor's position, display custom characters, and write text
on LCD screen. It also performs backlight control so that you can increase/decrease
the visibility or turn it on/off depending on lighting conditions.

DHT11:

The DHT module is a sensing unit that detects and measures humidity as well as
temperature levels in its environment. It detects and transforms chances in humidity
levels and temperature into electronic signals. Usually, it contains a digital output
interface which makes it simple to connect with microcontrollers for data retrieval
purposes. Utilizing a single-wire interface simplifies wiring, only one digital pin is
needed for both data transmission and power supply. Signal processing, calibration
and error checking are performed inside the module so that reliable and precise
measurements can be obtained under different environmental conditions.

SoftwareSerial:

SoftwareSerial simulates serial hardware using software timing mechanisms. It uses

interrupts and precise timing to send and receive bits on digital pins through bit-

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banging. This can use more CPU than hardware serial and may be less reliable under
some conditions—for example, higher baud rates or if multiple software serial ports
are active.

Wire:

The Wire.h library is one of the fundamental parts of the Arduino programming
environment, and it allows Arduino boards to communicate with I2C / TWI (Two-
Wire Interface) devices. The library provides an interface to facilitate the use of the
I2C bus, a widely used protocol in the communication between a microcontroller and
peripheral devices, such as sensors, displays, or even other microcontrollers. I2C
protocol has multiple slave devices connected over a common bus. Two wires are
needed—one for data (SDA) and one for clocking (SCL). One master device will be
in control of them all. The abstractions within the Wire.h library make most of the
underlying I2C communication complexity easy to use in simple functions within the
Arduino IDE.

Adafruit Sensor:

The module offers a consistent API for interacting with different sensors, which
makes it easy for developers to write code that can work with various types of
sensors. It is made to work with a wide range of sensors from Accelerometers to
Gyroscopes to Magnetometers and even Temperature Sensors among others designed
for creators who need such products in their design systems. This includes all sorts
like accelerometers, gyroscopes, magnetometers, temperature sensors. To connect to
a sensor in the Adafruit Sensor library one usually will make a sensor object instance.
This object stands for an actual sensor connected to the Arduino board. After that,
you can call Adafruit Sensor library functions to get sensor data.

5.2 Algorithms used with description:

5.2.1 UNO Code:

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Initialization:

● Initialize all necessary components, including the OLED display, pulse

sensor, DHT11 sensor, and ESP8266 module.

● Set pin modes and baud rates for communication interfaces (Serial,

SoftwareSerial).

Main Loop:

● Continuously execute the main loop, where the program's core functionalities

are performed.

Heart Rate Measurement:

● Read analog data from the pulse sensor connected to pin A0.

● Calculate the heart rate (beats per minute) based on the sensor data.

● Compare the calculated heart rate with a predefined threshold to determine

whether a heartbeat is detected.

● If a heartbeat is detected (heart rate above threshold), turn on the built-in LED

as a visual indicator.

ECG Measurement:

● Read analog data from the ECG sensor connected to pin A1.

● Convert the analog data to a meaningful ECG value.

● Output the ECG value to the serial monitor for visualization or further

processing.

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Temperature and Humidity Measurement:

● Read data from the DHT11 sensor connected to pin 4.

● Retrieve temperature and humidity values from the sensor.

● Output the temperature and humidity values to the serial monitor for

visualization or further processing.

Data Transmission:

● Combine the temperature and humidity values into a string along with

appropriate labels.

● Send the data string to the ESP8266 module via SoftwareSerial for

transmission to external devices or servers.

OLED Display:

● Use the U8glib library to display information on the OLED screen.

● Display a welcome message during startup.

● Display the current heart rate, ECG value, temperature, and humidity in

separate screens or pages.

● Update the OLED display periodically with new sensor data.

Loop Delay:

● Introduce delays between sensor readings and OLED updates to ensure stable

operation and prevent data overload.

Serial Communication:

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● Use the Serial interface for debugging and monitoring sensor data on a

computer.

● Use SoftwareSerial to communicate with the ESP8266 module for data

transmission.

Repeat:

● Repeat the main loop indefinitely to continuously monitor sensor data, update

the OLED display, and transmit data as required.

This algorithmic description outlines the key steps and functionalities performed by
your Arduino code, providing a high-level understanding of its operation.

5.2.2 NODE MCU Code:

This code is for an ESP8266-based device, likely an ESP8266 NodeMCU, which

sends data to ThingSpeak, an IoT platform, for visualization and analysis. Here's a
breakdown of the algorithm used:

Initialization:

Initialization:

● Initialize serial communication for debugging and monitoring purposes.

● Connect to the specified Wi-Fi network using the provided SSID and

password.

● Wait until the Wi-Fi connection is established before proceeding.

Main Loop:

● Continuously run the main loop to perform data transmission to ThingSpeak.

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Wi-Fi Connection:

● Attempt to connect to the specified Wi-Fi network using the provided SSID

and password.

● Wait for the Wi-Fi connection to be established before proceeding.

Data Transmission to ThingSpeak:

● Check if a connection to the ThingSpeak server can be established.

● Construct a POST request string containing the API key and the data to be

sent (presumably sensor data read from the serial port).

● Send the POST request to the ThingSpeak server over HTTP.

● Close the connection to the server after sending the data.

● Print debug messages to the serial monitor indicating that the data has been

sent to ThingSpeak.

Delay and Serial Data Handling:

● Introduce a delay of at least 15 seconds between successive data transmissions

to comply with ThingSpeak's rate limits.

● Check if there is serial data available (presumably sensor data).

● If serial data is available, read it and transmit it to ThingSpeak.

● Repeat this process indefinitely in the main loop.

Serial Communication:

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● Check for incoming serial data.

● If serial data is available, read it and transmit it to ThingSpeak.

Serial Debugging:

● Echo any received serial data to the serial monitor for debugging purposes.

● This ensures that the device is receiving data from connected sensors or other

peripherals.

Serial Delay:

● Introduce a delay of 10 seconds after transmitting serial data to prevent rapid

consecutive transmissions.

This algorithm continuously reads data from the serial port, sends it to ThingSpeak
for storage and visualization, and maintains the Wi-Fi connection for smooth data
transmission. The delay between transmissions ensures adherence with ThingSpeak's

rate limits and prevents flooding the server with data.

5.2.3 Random Forest Code:

Train-Test Split:

train_data = data.iloc[:-250]

test_data = data.iloc[-250:]

train_data: This variable contains all rows of the dataset except the last 250 rows.

test_data: This variable contains the last 250 rows of the dataset.

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The data is divided into training and testing sets using this split. The model is
constructed using the training data, and its performance is evaluated using the testing
data.

Import Libraries:

joblib: Used for saving and loading the trained model.

pandas: Used for data manipulation and analysis.

RandomForestClassifier: A machine learning algorithm from scikit-learn.

classification_report, accuracy_score: Used for model evaluation.

Load the Trained Model:

loads the pre-trained RandomForestClassifier model from a file named

random_forest_model.joblib.

Map Prediction to Medical Condition:

The prediction is mapped to a human-readable medical condition.

CHAPTER - 6
SYSTEM TESTING

A dialogue box displaying the data, including the pulse, temperature, humidity, and
ECG readings, is displayed by the Serial Monitor.

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Fig 6.1. pulse, temperature, humidity, and ECG data.

The NodeMCU Serial Monitor is used to track the progress of data transmission to
ThingSpeak.

Fig 6.2. Sensor’s data are being sent to ThingSpeak.

Within the 'Data Import / Export' tab, you will have options to specify the range of
data you want to download.

Choose the format in which you want to download the data. I’ve used CSV (Comma-
Separated Values), which is a convenient format as it can be easily imported into
spreadsheet software like Microsoft Excel.

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The data will be processed, and a CSV file will be generated. Click the download link
or the file icon to save the CSV file to your computer.

Fig 6.3. CSV file download

To predict health outcomes, I've implemented Random Forest, SVM and KNN
machine learning models.

During training, several decision trees are built using the Random Forest ensemble
learning technique, which produces the mode of the classes (classification) of each
individual tree. The accuracy of the Random Forest model was 0.96.

With extremely high recall and precision, it succeeds in classifying the majority class
(Class 0). The model likewise shows good performance for the minority class (Class
1), but with a slight decline in recall.

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Fig 6.4. Random Forest Model Result

Class 0 is classified with excellent recall and precision, suggesting that the SVM
model performs extremely well in minimizing false negatives and recognizing true
positives for this class.

Class 1 has strong recall and precision in classification, however slightly lower
metrics than Class 0. This implies that while the model can accurately predict Class 1
in most cases, it might miss some instances.

High performance and accuracy are shown by the SVM model in all significant
classification measures. With excellent recall and precision, it excels in classifying
the majority class (Class 0). The approach works well for the minority class (Class 1),
although recall for that class is slightly lower than for Class 0.

Fig 6.5. SVM Model Result

The KNN model is good at finding true positives for class 0, but it also generates a
higher number of false positives. Class 0 is classified with moderate precision but
high recall.

Class 1 has a low recall and low precision of classification, indicating that the KNN
model has a major problem detecting genuine positives and reducing false negatives
for this class.

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With an accuracy of 0.696, the KNN model performs moderately overall. It performs
noticeably worse for Class 1, with poor precision and recall, even if it has a
reasonable recall for Class 0. The imbalance suggests that KNN is not a good fit for
this specific classification problem, especially when handling data that is not
balanced.

Fig 6.6. KNN Model Result

Based on the accuracy scores, Random Forest and SVM perform better than KNN.

Random Forest and SVM are nearly equivalent in performance, with Random Forest
slightly ahead.

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Fig 6.7. Patient data downloaded from thingspeak and visuals from excel.

This file contains timestamped entries of the sensor data, allowing for past data
analysis and backup.

The CSV file exported from ThingSpeak is downloaded and uploaded into Microsoft
Power BI.

This data is imported into Power BI for analysis and visualization.

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Fig 6.8. Visuals of Patient data in Power BI

To ensure accuracy in visualization, ECG, pulse, and temperature sensor data were
cleaned and transformed.
shows the trend of the temperature, pulse, and ECG rates during the observation
period.
Data can be filtered by users according to particular time intervals, sensor types, or
medical conditions.
Enhanced decision-making and in-depth analysis are made possible by this
interaction.

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CHAPTER – 7
RESULTS AND ANALYSIS
7.1 Result and analysis:
The IoT Health Monitoring System represents an innovation in the integration of
hardware and software components to facilitate remote health monitoring and
analytics of vital physiological data. The NodeMCU and Arduino Uno are the
microcontrollers at the center of the system architecture, which undertake the
responsibility for data acquisition, processing, and transmission. Additionally, a suite
of sensors composed of the Pulse Sensor, Temperature Sensor, and ECG Sensor are
used for the capture of key health metrics such as heart rate, body temperature, and
electrocardiogram signals.

Sensors collect real-time physiological data, which is transferred into the Arduino
Uno for preprocessing and conditioning. Here, it becomes an intermediary in the
process of converting the analog sensor readings into digitized data for transmission.
The Arduino Uno communicates with the NodeMCU, harnessing its Wi-Fi capability
to connect it to the internet.

Once connected, the NodeMCU uses HTTP requests in sending the sensor data,
which has been processed, to ThingSpeak—a cloud-based IoT analytics platform.
ThingSpeak is the central repository of the collected data, complete with its high-end
storage and analytics features. Data is structured into channels within ThingSpeak for
ease of access and retrieval.

Power BI provides the facility of dynamic dashboards and reports, which means the
representation of important health metrics in real time. The user extracts important
insights from trends, patterns, and anomalies in the data through the use of these
interactive visualizations, which can include charts, graphs, and gauges.

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The results of the IoT Health Monitoring System are multifaceted and far-reaching.
In addition to real-time monitoring capabilities, the system allows for early detection
of potential health issues through proactive analysis of sensor data. Not only that, but
the predictive analytics capabilities embedded in Power BI empower users to forecast
and predict potential health outcomes based on historical trends in the data.

7.2 Challenges:
In this project we face various challenges like:

1. Hardware Selection:
Selecting hardware for health monitoring projects might be difficult because there
are so many alternatives. It is straightforward to know the properties,
compatibility, and specs of different sensors, microcontrollers, and
communication modules. We gathered data on various hardware components and
did a thorough investigation to address this difficulty. For us to be able to make
informed decisions, we also searched online for suggestions for developing an
IoT project.
2. Programming Skills:
Our project involves programming to control the hardware, process data, and
implement communication protocols. We have faced difficulties with coding. We
have gone through varies Online tutorials, documentation, and examples as they
can be valuable resources for learning and practicing programming concepts.
3. Power Management:
IoT devices need to be efficient in terms of power consumption, especially if they
are intended to operate for extended periods without frequent battery replacement
or recharging. We have struggle with optimizing power management and
balancing functionality with power requirements. We consider using low-power
hardware components.

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7.3 System setup:

Fig 7.3. Patient Health Monitoring System

The system includes three main types of sensors: ECG sensor, Pulse sensor, and
Temperature sensor. These sensors are responsible for collecting vital health data
from the patient.

The main data collecting device is an Arduino Uno, which reads sensor data every 10
seconds on a regular basis.

The NodeMCU uploads the sensor data to ThingSpeak, where it is stored and made
available for real-time monitoring and analysis.

The system continuously monitors and transmits patient data to the cloud.

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CHAPTER – 8
CONCLUSION AND FUTURE SCOPE

8.1 Conclusion:
The Patient Health Monitoring System is a very important innovation within the
bounds of healthcare technology, using IoT, data analytics, and visualization to
remotely monitor and manage patient health. The system delivers real-time insights
into life physiological data, such as heart rate, temperature, and electrocardiogram
signals, through the integration of NodeMCU, Arduino Uno, ThingSpeak, and Power
BI. The system collects, analyzes, and visualizes this data, enabling healthcare
providers to track patient health proactively, identify early warning signs of a
potential health problem, and intervene promptly to improve the patient's health.

● Remote Monitoring: The device allows remote access to patient health data by a

health practitioner, thus making appropriate decisions for the patient irrespective
of their location.

● Early Detection: The system allows the detection of health problems early by

analyzing the trends and anomalies of the physiological data, allowing timely
intervention and treatment.

● Data-driven decisions: With the help of interactive dashboards, Power BI enables

healthcare providers to make informed choices in the care of patients that will
ensure better patient outcomes and patient satisfaction.

8.2 Future Scope:

● Predictive Analytics: Integrate machine learning algorithms that predict health

outcomes in the future on a trend of past data, hence enabling proactive

intervention and personalized care for patients.

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● Remote alerts and notifications: Build alerting mechanisms to send healthcare

providers critical health events in real time, which may permit timely intervention
and reduce response time.

● Electronic Health Records Integration: Integrate the system into existing EHR

systems that enable a complete view of patient health data and communication
between healthcare providers.

● Improved UI: Create a user-friendly interface for patients to see their health data,

set personalized health goals, and track progress over time, encouraging patient
participation and self-management.

● Telemedicine Integration: Integrate telemedicine functionalities that would allow

remote consultations and virtual visits to improve access to healthcare services

and reduce pressure from healthcare infrastructures.

The Patient Health Monitoring System is huge in its potential to revolutionize

healthcare delivery, making it possible to offer proactive, individualized, and data-
driven patient care. The system will continue innovating to maintain its relevance in
the face of changing patient and provider needs and in the pursuit of quality
improvement in health outcomes and life quality.

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REFERENCES MAY 2024

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