Dr.

AMBEDKAR INSTITUTE OF TECHNOLOGY

(An Autonomous Institute Affiliated to Visvesvaraya Technological University, Belagavi, Accredited
by NAAC, UGC with ‘A’ Grade)
Near Jnana Bharathi Campus, Bangalore – 560056

DEPARTMENT OF ELECTRONICS AND TELECOMMUNICATION ENGINEERING

(Accreditated by NBA)
SEMINAR REPORT
On

“HOT Watch: IoT-Based Wearable Health

Monitoring System”
(Subject Code: 21ETS802)
Submitted in partial fulfillment of award of the Degree of
BACHELOR OF ENGINEERING
in ELECTRONICS & TELECOMMUNICATION ENGINEERING
Submitted By:
MANJUNATH G 1DA21ET022
Semester: VIII
Submitted for the Academic year 2024-25
UNDER THE GUIDANCE OF
Dr. Sudha H Timmaih
Assistant professor, Dept. of ETE

Visvesvaraya Technological University

JnanaSangama, Belagavi,Karnataka 590018
 Dr. AMBEDKAR INSTITUTE OF TECHNOLOGY
(Near Jnana Bharathi Campus, Bengaluru-560056)
(An Autonomous Institution, Affiliated to Visvesvaraya Technological University, Belagavi, Aided
by Government of Karnataka)
DEPARTMENT OF ELECTRONICS AND TELECOMMUNICATION ENGINEERING

CERTIFICATE

This is to certify that the seminar work entitled- “HOT Watch: IoT-Based Wearable Health
Monitoring System” is a bonafide work carried out by MANJUNATH G (1DA21ET022), in partial
fulfillment for the award of Bachelor of Engineering in Electronics and TeleCommunication
Engineering during the year 2024-2025. It is certified that all corrections/suggestions indicated for
internal assessment have been incorporated in the report deposited in the department library.The
seminar report has been approved as it satisfies the academic requirements in respect of the seminar
work prescribed for the Bachelor of Engineering Degree.

Signature of Guide Signature of Coordinator Signature of the H.O.D

Dr. Sudha H Timmaih Dr.Vidya Honguntikar Dr.Ravish D. K

Associate Professor Asscociate Professor Associate Professor and HOD
Department of ETE Department of ETE Department of ETE
 ACKNOWLEDGEMENT

I take this opportunity to express my profound gratitude and deep regards to Dr. M. N.
Thippeswamy, Principal, Dr. AIT, Bengaluru, for his moral support towards completing our
Seminar Work.

I wish to express my sincere gratitude to Dr. Ravish D K, HOD, Department of ETE, Dr. AIT,
Bengaluru for his constant encouragement throughout the course of the Seminar Work.

I wish to express my sincere gratitude to our Guide Dr. Sudha T Timmaih, Associate Professor,
Department of ETE, Dr. AIT, Bengaluru for her guidance, monitoring and constant encouragement
throughout the course of the Seminar Work.

I wish to express my sincere gratitude to our Seminar coordinator Dr. Vidya Honguntikar,
Associate Professor, Department of ETE, Dr. AIT, Bengaluru for her guidance, monitoring and
constant encouragement throughout the course of the Seminar Work.

I also take this opportunity to express a deep sense of gratitude to teaching and non-teaching staff
for his/her cordial support, guidance and valuable information, which helped us in completing this
task through various stages.

Finally, I thank almighty, my family members and friends for their constant support and
encouragement in carrying out the Seminar Work.

MANJUNATH G
[1DA21ET022]
 Abstract

The Internet of Things (IoT) and wearables involve small embedded devices with
sensors collecting data from their surroundings. Nowadays, people have added com
plexity as well as busyness to their lives and they do not give their health much thought
due to their hectic routines. Current health monitoring systems are often cumbersome
and inconvenient for patients, leading to poor adherence and delayed detection of health
issues. To address this problem, heart rate oxygen rate temperature watch (HOT Watch)
an IoT-based wearable health monitoring device has been pro posed to track the user’s
health condition and notify the person with their health details. The HOT Watch
employs sensors, such as the MLX90614 temperature sensor, AD8232
electrocardiogram (ECG) sensor, and MAX30100 oximeter sensor to gather health
metrics from users. The HOT Watch employs Arduino technology and Bluetooth
connectivity to transmit data to a mobile application and the Pan–Tompkins algorithm
(PTA) precisely determines the user’s heart rate. The proposed method displays the
essential health information and alerts users about their health status, including the
location data obtained from the GPS sensor in the watch. By continuously tracking vital
signs such as temperature, oxygen saturation, and heart rate, individuals can gain
valuable insights into their overall health status. These real-time data allow users to
monitor their well-being proactively and make informed decisions about their lifestyle
and activities. The accuracy of the proposed HOT Watch is 1.40%, 0.70%, and 2.47%
higher than the existing Sensor Patch, IoT-based wearable sensor (WS-IoT), and Neo
Wear, respectively.
 Table of Contents

Sl CHAPTERS PAGE NO
No
1 INTRODUCTION 1-3
2 LITERATURE REVIEW 4-5
3 PROPOSED SYSTEM 6-12
4 RESULTS AND DISCUSSION 13-19

5 ADVANTAGES, 20-22
DISADVANTAGES AND
APPLICATIONS

6 CONCLUSION & FUTURE SCOPE 23-26

7 REFERENCE 27-30
 List of Figures

Fig FIGURES PAGE

No NO
Fig 1 Proposed workflow of HOT Watch 7

Fig 2 Sensors integrated in HOT Watch 9

Fig 3 Architecture of PTA 10

Fig 4 ECG beat 11

Fig 5 Process of Pan Tompkins algorithm 12

Fig 6 HOT Watch’s hardware model 13

Fig 7 Login page of the application 14

Fig 8 HOT Watch usage among different users 15

Fig 9 Tracked health details of different users 15

Fig 10 Notification status of different users 16

Age and Gender-Based Evaluation of Heartbeat 17
Fig 11 Rate

(a) Readings of people’s oxygen rate at various 18

Fig 12 ages. (b) Different genders’ readings of human
oxygen rates

(a) Observations of the body temperature of 18

Fig 13 people at various ages. (b) Different genders
readings of the human body temperature.

Fig 14 Comparison in terms of accuracy 19

HOT Watch: IoT-Based Wearable Health Monitoring System 2024-25

CHAPTER 1:
INTRODUCTION
The Internet of Things (IoT) has emerged as one of the most transformative technologies of the
modern era, revolutionizing the way we interact with our environment by enabling smart, connected
systems. Its rapid advancement has led to its adoption across a wide array of sectors, including smart
homes, industrial automation, agriculture, transportation, fashion, sports, and notably, healthcare
[1]. In the healthcare domain, IoT has opened new avenues for enhancing patient care, disease
prevention, and medical diagnostics by providing the capability to monitor health metrics
continuously and in real time [2], [3].
Traditional healthcare systems are often reactive in nature, relying heavily on physical visits to
hospitals or clinics, which may not always be feasible—especially for patients in remote or
underserved regions. Moreover, the growing global population, increased life expectancy, and rising
incidence of chronic diseases have placed a substantial burden on healthcare infrastructure. These
challenges have fueled the need for innovative healthcare technologies that are efficient, accessible,
cost-effective, and capable of offering proactive, remote, and personalized healthcare solutions.
IoT-based health monitoring systems fulfill this need by leveraging wearable devices embedded
with sensors to track critical physiological signals such as body temperature, respiratory rate,
blood pressure, SpO₂ (oxygen saturation), heart rate, electrocardiogram (ECG), and blood
glucose levels [4], [5]. These systems not only provide real-time data acquisition and analysis but
also enable healthcare professionals and individuals to take timely action, thereby reducing
emergency incidences and improving overall health outcomes.
Modern microelectronic sensors, integrated with communication modules and microcontrollers, can
now measure these health parameters with remarkable accuracy, miniaturization, and low power
consumption. The convergence of biomedical sensors, embedded systems, and wireless
communication technologies—especially Bluetooth Low Energy (BLE)—has led to the
development of portable and wearable health monitoring systems capable of transmitting data to
smartphones or cloud-based platforms for further analysis [13].
Vital signs such as heart rate, oxygen saturation, and body temperature are essential indicators
of a person's physical condition. Heart rate can reflect physical fitness, stress levels, and underlying
cardiovascular conditions. By continuously monitoring heart rate, individuals can gain insights into
their emotional and physical well-being and take preventive actions against stress-induced illnesses
[8]. Similarly, body temperature is a critical parameter used to detect the onset of infections such
as bacterial or viral diseases. Fever, the body’s natural immune response, can be detected early
through temperature monitoring, enabling faster diagnosis and treatment [9], [10]. Another
important health metric is respiratory rate, which indicates lung function and is vital for assessing
respiratory disorders such as asthma, pneumonia, or COVID-19 [11], [12].

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Despite the potential of wearable health monitoring technologies, current solutions suffer from
several limitations:
• High cost of sensors and system integration
• Limited precision and reliability of measurements
• Complicated setup procedures
• Bulky or uncomfortable designs
• Lack of user-friendly interfaces for data visualization and interpretation
These shortcomings often result in low user compliance, delayed diagnosis, and poor adoption of
technology in routine healthcare practices.
To overcome these barriers, this study proposes a novel HOT Watch—a Heart rate, Oxygen
saturation, and Temperature monitoring wearable device—designed to provide an affordable,
precise, and user-friendly health monitoring solution. The HOT Watch is engineered using three
primary sensors:
• MLX90614 for non-contact body temperature measurement,
• AD8232 ECG sensor for electrocardiogram signal acquisition,
• MAX30100 pulse oximeter for measuring SpO₂ and heart rate.
The device is powered by an Arduino microcontroller and communicates wirelessly with a
smartphone application using Bluetooth. Additionally, the HOT Watch is equipped with a GPS
module to identify the user's location in case of health anomalies, allowing the system to notify
caregivers or emergency services.
The core functional components and contributions of this work include:
1. Sensor Integration and Data Collection: The HOT Watch integrates multiple biomedical sensors to
simultaneously monitor vital signs including ECG, body temperature, and SpO₂.
2. Signal Processing and Heart Rate Detection: ECG data from the AD8232 sensor is processed using
the Pan–Tompkins Algorithm (PTA), a well-established method for detecting QRS complexes in ECG
signals, to accurately compute heart rate.
3. Real-Time Mobile Application Interface: Sensor data is transmitted to a mobile application via
Bluetooth. The app displays heart rate, oxygen level, and temperature in real-time, and classifies the
health condition as normal or abnormal. If abnormal, it alerts the user and includes their GPS location
for potential emergency assistance.
The HOT Watch bridges the gap between traditional healthcare services and modern digital health
monitoring by providing a lightweight, cost-effective, and scalable wearable device. It is
particularly useful for:
• Patients with chronic diseases,
• Elderly individuals living alone,
• Athletes and fitness enthusiasts,
• Individuals in remote areas with limited access to healthcare facilities.

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By enabling continuous, remote, and real-time health tracking, the HOT Watch empowers users
to proactively manage their health and seek timely medical attention when necessary. Furthermore,
the system is scalable and adaptable for future enhancements, such as cloud integration, AI-based
anomaly detection, and telemedicine support.

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CHAPTER 2:
LITERATURE REVIEW
Over the past few years, wearable technology integrated with IoT has garnered significant
attention for its potential in real-time health monitoring applications. Researchers and developers
have proposed various systems, architectures, and wearable devices to enhance the efficiency,
accessibility, and accuracy of healthcare delivery. This section presents a comprehensive review of
recent literature in the field, highlighting their approaches, achievements, and limitations.
Wu et al. [18] introduced a wearable sensor patch that is compact, lightweight, and consumes
minimal power, making it suitable for IoT-based healthcare environments. Their study showed that
the sensor patch performed well when benchmarked against a commercially available medical
reference device. However, a key drawback of this system is the high cost associated with sensor
patch development and deployment, which limits its feasibility for large-scale or long-term use.
Huifeng et al. [19] developed an IoT-enabled wearable sensor system (WS-IoT) designed
specifically for athletes. The system monitors continuous health data such as activity levels and vital
signs using wearable devices. To process the collected data, machine learning algorithms with
efficient optimization techniques were implemented. While their findings indicate that the system
is effective in monitoring athlete health, its application remains narrowly focused on sports
scenarios, lacking generalization to broader healthcare contexts.
Al Bassam et al. [20] designed an IoT-based wearable health monitoring system targeted at
tracking COVID-19-related vital parameters. The system allows for real-time symptom
monitoring, analysis, and patient health status management. The researchers highlighted the use of
digital remote platforms to facilitate patient recovery monitoring. Though impactful during the
pandemic, this solution is tailored specifically for infectious disease monitoring, limiting its
general usability for broader health monitoring needs.
Cay et al. [21] developed NeoWear, a smart textile-based chest band with IoT integration, aimed at
detecting infant apnea episodes by continuously monitoring respiratory rates. Their experimental
results demonstrated promising accuracy—up to 97%—in identifying apnea incidents using an
infant mannequin. However, the solution is niche-focused and may not scale well across different
age groups or health conditions.
Qiu et al. [22] proposed an IoT-based Hierarchical Health Monitoring Model (IoT-HHMM) for
evaluating wearable systems used by athletes. This hierarchical model demonstrated a high
accuracy rate of 98.4% in comparison with conventional evaluation approaches. The primary
limitation lies in the lack of hardware or system implementation, as the study focuses more on
theoretical modeling than on real-world deployment.
Nwibor et al. [23] designed an IoT-enabled remote health monitoring system capable of tracking
blood pressure (BP), heart rate (HR), and oxygen saturation (SpO₂) levels. The system analyzes
photoplethysmogram (PPG) signals to estimate these health parameters. The authors introduced a

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custom AMBP detection technique for identifying the PPG signal’s peak. Despite offering
improved signal analysis, the system may face challenges in power consumption and sensor
calibration in mobile environments.
Mohammadian et al. [24] presented a wearable light sensor node integrated with IoT functionality
to monitor environmental data index (EDI) exposure. The device supports 10 optical measurement
channels ranging from 415 nm to 910 nm and operates with a 30-second sampling cycle, lasting up
to 3.5 days on a single charge. While it offers a good balance between affordability and accuracy,
its application is limited to environmental sensing, not vital sign monitoring.
Mai et al. [25] introduced the Ear-EEG Emotion Recognition (EEER) system, which integrates
ear-based EEG sensors with IoT and applies a deep neural network (DNN) for emotion detection
and interpretation. The system utilizes the ViT model with SPT and LSA, achieving an average
accuracy of 92.39% on unseen datasets. While innovative in emotional health detection, the system
lacks vital sign tracking features and focuses on cognitive monitoring rather than physical health.

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CHAPTER 3:
PROPOSED SYSTEM
HOT WATCH
In this section, we introduce the HOT Watch, a novel IoT-based wearable health monitoring system
designed to continuously track and evaluate an individual’s vital signs and promptly notify them of their
health status. The primary objective of the HOT Watch is to provide real-time health monitoring
through an integrated system of biomedical sensors, a processing unit, and a mobile application, making
it highly suitable for use in personal healthcare, remote monitoring, and emergency alert systems.
A. System Design Overview
The HOT Watch is equipped with multiple physiological sensors including:
• MLX90614 Temperature Sensor – for non-contact body temperature measurement.
• AD8232 ECG Sensor – for capturing the user’s electrocardiogram (ECG) signals.
• MAX30100 Pulse Oximeter Sensor – for monitoring blood oxygen saturation (SpO₂) and pulse
rate.
These sensors are embedded within a compact wearable device and are connected to an Arduino
microcontroller with Bluetooth connectivity, enabling seamless data transmission to a dedicated
mobile application.
The system workflow is as follows:
1. Data Acquisition: The sensors collect data including ECG waveform, body temperature, and blood
oxygen level.
2. Heart Rate Calculation: The ECG data is processed using the Pan–Tompkins Algorithm (PTA) to
accurately extract the heart rate from QRS complexes.
3. Wireless Transmission: The processed and raw sensor data are transmitted to a smartphone app via
Bluetooth.
4. Display and Notification: The mobile application presents the health parameters and provides real-
time alerts if abnormal values are detected.
5. Location Tracking: The device includes a GPS module that identifies and reports the user’s location,
which is useful in emergencies.
The complete workflow is visually represented in Figure 1, showcasing the interactions between the
sensing unit, processing unit, mobile application, and user.
B. Addressing Limitations in Existing Systems
The design of the HOT Watch is motivated by the limitations observed in current health monitoring
technologies. These include:
• High cost of commercial solutions.
• Fragmented functionality requiring multiple devices for different measurements.
• Limited integration with mobile platforms.
• Lack of real-time location tracking for health alerts.
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By consolidating multiple functionalities into a single, compact device, the HOT Watch overcomes
these challenges. It enables continuous, low-cost, real-time health monitoring, and is especially
beneficial for elderly individuals, patients with chronic illnesses, athletes, and people living in remote
areas.
C. Data Collection and Transmission
Each sensor plays a specific role in monitoring vital signs:
• The MLX90614 measures body temperature through infrared detection, ensuring hygiene and
comfort.
• MAX30100 monitors SpO₂ and heart rate using optical technology (red and IR light absorption).
• The AD8232 ECG sensor captures electrical signals from the heart and produces an ECG
waveform.
The data collected from these sensors is transmitted to the Arduino controller. The ECG signal is
further processed using the Pan–Tompkins Algorithm for heart rate calculation, while the other
sensor data are directly sent to the mobile application via Bluetooth. The system also utilizes a GPS
sensor to determine the user's geolocation, providing additional safety by enabling location-based alerts
in the event of health anomalies.

Fig. 1. Proposed workflow of HOT Watch.

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i. Sensing Unit
The core of the HOT Watch lies in its sensor integration, which enables the real-time monitoring of
critical physiological parameters. Users wear a compact smartwatch-style device embedded with
various biomedical sensors and wireless communication modules. These components work together to
collect, process, and transmit health data efficiently and accurately.
The primary physiological metrics monitored by the HOT Watch include:
• Body Temperature
• Heart Rate (ECG)
• Blood Oxygen Saturation (SpO₂)
• User Location
1) MLX90614 Infrared Temperature Sensor
The MLX90614 is a highly accurate, non-contact infrared sensor used for measuring body
temperature. It is ideal for wearable applications due to its:
• Compact form factor
• Low power consumption
• Capability to measure temperature from a distance
By detecting infrared radiation emitted from the skin surface, the MLX90614 provides accurate
readings without requiring direct contact, enhancing user comfort and hygiene.
2) MAX30100 Pulse Oximeter Sensor
The MAX30100 is a dual-purpose sensor that combines:
• Pulse oximetry (SpO₂ measurement)
• Heart rate monitoring
It utilizes photoplethysmography (PPG) to measure the variation in light absorption caused by
changes in blood volume under the skin. This sensor uses two LEDs (red and infrared) and a
photodetector to determine:
• Oxygen saturation levels (SpO₂)
• Pulse rate (BPM)
Its integration into the HOT Watch allows continuous and non-invasive monitoring of a user’s
cardiopulmonary status.
3) AD8232 ECG Sensor
The AD8232 is an analog front-end module used for ECG signal acquisition. It captures the electrical
signals generated by cardiac activity and provides a clear ECG waveform that can be used to:
• Detect arrhythmias
• Measure heart rate
• Analyze QRS complexes using the Pan–Tompkins Algorithm (detailed in the next section)
This sensor ensures high signal fidelity with minimal noise, making it suitable for continuous cardiac
monitoring in wearable devices.

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4) GPS Sensor
A GPS (Global Positioning System) module is included to determine the precise geographic location
of the user. It communicates with a network of Earth-orbiting satellites to triangulate position, enabling:
• Real-time location tracking
• Emergency alerts with location sharing
• Context-aware health notifications
This feature enhances user safety, especially in remote health monitoring and emergency scenarios.
5) Arduino ESP8266 Microcontroller
Sensor data are managed and transmitted using the Arduino ESP8266, an open-source microcontroller
with built-in:
• Wi-Fi
• Bluetooth
It acts as the central hub for the HOT Watch, responsible for:
• Interfacing with all sensors
• Pre-processing sensor signals
• Transmitting data wirelessly to the mobile application
The ESP8266 is widely used in IoT applications due to its:
• Low cost
• High processing power
• Efficient communication capabilities

Fig. 2. Sensors integrated in HOT Watch

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ii. B. Data Processing

A key innovation in the HOT Watch lies in its ability to process physiological signals in real-time to
derive meaningful health metrics. Among these, heart rate calculation is particularly critical, as it
serves as a vital indicator of cardiovascular health. For this purpose, the system employs the Pan–
Tompkins Algorithm (PTA) to extract and analyze ECG signals collected by the AD8232 sensor.

1) Pan–Tompkins Algorithm (PTA)

The Pan–Tompkins Algorithm is a widely recognized and reliable method for QRS complex
detection in ECG signals. The QRS complex corresponds to the depolarization of the ventricles and is
the most prominent waveform in an ECG signal. By detecting R-peaks—the highest point in the QRS
complex—PTA enables accurate heart rate estimation.
Processing Steps in PTA:
The architecture of the Pan–Tompkins Algorithm is designed as a multi-stage signal processing
pipeline, consisting of the following steps:

Fig. 3. Architecture of PTA

i) Bandpass Filtering
To eliminate baseline wander, motion artifacts, and high-frequency noise, the raw ECG signal is first
filtered using a bandpass filter. This filter isolates the frequency band typically associated with QRS
complexes (approx. 5–15 Hz), enhancing the signal-to-noise ratio.
Filtered signal representation:
F=Y[m]
where Y[m] denotes the filtered ECG signal at the discrete time index m.

ii) Differentiation
The next step involves differentiating the filtered signal to emphasize the slope information of the QRS
complex.

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Differentiated signal:
X[m]=Y[m]−Y[m−1]
where X[m] is the rate of change of the ECG signal, highlighting steep slopes indicative of QRS
complexes.

iii) Squaring
The differentiated signal is then squared to:
Make all data points positive
Amplify higher slopes and suppress smaller fluctuations
Squared signal:
Z[m]=X[m]2
where Z[m]is the squared output at sample index m.

iv) Moving Window Integration

A moving window integrator smooths the squared signal to generate an envelope of the QRS complex.
The window size PP is chosen based on the typical duration of the QRS complex (e.g., 150 ms).
Integrated signal:
V[m]=∑j=m−P+1mZ[j]
where V[m] is the integrated signal at sample index mm, and PP is the window length.

Fig. 4. ECG beat

v) Thresholding and Peak Detection

To accurately detect R-peaks, the integrated signal V[m] is evaluated against a dynamic threshold,
calculated using:
• Mean and standard deviation of the signal over time
• Adaptive thresholding logic to account for signal variability
This allows robust identification of QRS complexes even in the presence of noise.

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2) Heart Rate Calculation

Once R-peaks are detected, the time difference between consecutive R-peaks (known as the RR
interval) is used to compute the heart rate (in beats per minute, BPM) using the formula:
Heart Rate (BPM)=60/RR interval (in seconds)
The calculated heart rate, along with temperature and SpO₂ readings, are transmitted to the mobile
application.

System Workflow and User Interface

The final outputs—heart rate, body temperature, and oxygen saturation—are displayed on a mobile
application connected via Bluetooth to the HOT Watch. Additionally, a GPS sensor transmits the user's
location data, enabling location-based health alerts in case of abnormal health readings.
➢ The mobile app provides:
• A real-time display of all vitals
• Notifications when abnormal values are detected
• Location tagging for emergency response

Experimental Implementation and Validation

The complete HOT Watch system was validated through hardware implementation and experimental
testing in real-world scenarios. Key features of the testing phase include:
• Integration of MLX90614, MAX30100, AD8232, GPS, and Arduino ESP8266 into a functional
prototype.
• Real-time data acquisition from users of varying ages and genders to assess performance.
• ECG data were analyzed using PTA, while temperature and oxygen levels were monitored using
corresponding sensors.
The processed data were visualized graphically, showcasing trends in health parameters and validating
the system's accuracy, responsiveness, and reliability across diverse user profiles.

Fig. 5. Process of Pan Tompkins algorithm

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CHAPTER 4:

RESULTS AND DISCUSSION

In this study, a real-time HOT Watch, an IoT-based wearable health monitoring system, has been successfully
developed to monitor and analyze the user’s physiological health parameters under both normal and abnormal
conditions. This section presents the detailed analysis of the implementation, real-world testing, and results to
evaluate the accuracy, efficiency, and feasibility of the proposed system.

A. Hardware Implementation

The hardware implementation of the HOT Watch involves the integration of several vital components that
enable continuous and non-invasive health monitoring. The main components include:

• MLX90614 Infrared Temperature Sensor – for accurate body temperature measurement.

• MAX30100 Pulse Oximeter – for monitoring blood oxygen saturation (SpO₂) and heart rate.

• AD8232 ECG Sensor – for capturing the electrical activity of the heart.

• GPS Module – for real-time user location tracking.

• Arduino ESP8266 – a microcontroller with built-in Wi-Fi and Bluetooth connectivity, used to process
sensor data and transmit it to the mobile application.

Fig. 6 shows the hardware prototype of the HOT Watch. The sensors are carefully embedded into a compact
wearable device to ensure comfort and usability. The wiring and connections are optimized for reliable data
acquisition and wireless communication.

All sensors are connected to the ESP8266 microcontroller, which handles real-time data collection and
preprocessing before transmitting it to the mobile application via Bluetooth. This hardware configuration was
tested for compatibility, durability, and ease of use in various daily life settings.

Fig. 6. HOT Watch’s hardware model

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B. Mobile Application Interface

The mobile application functions as a real-time dashboard for users to monitor their health parameters. After
logging in through the secure authentication screen shown in Fig. 7, users can access an intuitive interface that
displays their:

• Heartbeat rate (in BPM)

• Body temperature (in °C)

• Blood oxygen saturation level (SpO₂)

• Health status notifications (normal or abnormal)

• Real-time location (coordinates or map-based)

The application also alerts users in case of abnormal readings, with corresponding health advisories and GPS
location tagging. This ensures that caregivers or emergency services can be informed when necessary.

Fig. 7. Login page of the application

C. Real-World Deployment and Observations

To assess the effectiveness of the HOT Watch in everyday scenarios, it was tested on multiple users across
different environments. Fig. 8 shows real-world usage by several individuals wearing the HOT Watch while
walking, sitting, and engaging in light physical activity.

The device was able to accurately monitor the physiological parameters without hindrance to the users’
movement or comfort. The photoplethysmography (PPG)-based heart rate sensor effectively captured pulse
wave signals from the wrist, while the MLX90614 sensor provided accurate skin temperature readings.

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Similarly, the MAX30100 oximeter performed well in detecting oxygen saturation levels based on light
absorption techniques.

Fig. 8. HOT Watch usage among different users

D. Tracked Health Details Analysis

A detailed visualization of the health parameters recorded for four users is presented in Fig. 9(a–d). The graphs
for each user display:

• Heart rate trends over time

• Temperature variations

• SpO₂ fluctuations

These metrics are useful for identifying patterns and deviations from normal health baselines. The system
enabled continuous tracking, which is particularly beneficial for users with chronic conditions or those under
post-surgical observation.

The data demonstrated that the HOT Watch provided consistent and reliable readings under different
physiological and environmental conditions.

Fig. 9. Tracked health details of different users

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E. Health Notification System and GPS Integration

Fig. 10(a–d) illustrates the notification system and GPS functionality for four users. When the system detects
a critical health parameter (e.g., heart rate above a threshold or low oxygen levels), the application immediately
notifies the user with a “Health Alert” status. These notifications are accompanied by the user’s current location,
determined via the GPS module integrated in the HOT Watch.

This functionality is particularly valuable in emergency situations where immediate assistance might be
required. The health status notifications are categorized as:

• Normal: All parameters within acceptable medical limits.

• Abnormal: At least one parameter exceeds critical thresholds.

This real-time notification system supports proactive healthcare by encouraging timely medical intervention.

Fig. 10. Notification status of different users

B. Experimental Results of HOT Watch

The experimental evaluation of the proposed HOT Watch focuses on its ability to accurately monitor critical
health parameters and analyze variations based on age and gender. Additionally, the system’s performance is
compared against other state-of-the-art health monitoring technologies. The following subsections detail the
evaluation process and results.

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1) Age and Gender-Based Evaluation of Heartbeat Rate

Heart rate is a vital indicator of physiological health and varies across individuals depending on age and gender.
Using ECG signals processed through the Pan–Tompkins Algorithm (PTA), the HOT Watch effectively
captured and analyzed heart rate patterns.

• Younger individuals exhibited higher heart rates compared to adults and the elderly, consistent with
medical expectations.

• Gender-based comparison revealed that females tended to have slightly elevated average heart rates
compared to males across similar activity levels.

These trends are reflected in Fig. 11(a) (age-based heart rate evaluation) and Fig. 11(b) (gender-based heart
rate evaluation), showcasing the HOT Watch's capability in identifying physiological differences across
demographics.

(a) (b)

Fig. 11. (a) Depicts heart rates spanning ages 5 to 20 up to 90 bpm, bpm, including normal adult rates at 72
bpm and average rates for adults aged 45 to 65 at 65 bpm. (b) Illustrates that women typically have higher heart
rates than men, with peaks on the right and left representing heart rates for individuals of the same age.

2) Age and Gender-Based Evaluation of Oxygen Saturation (SpO₂)

The HOT Watch continuously monitors blood oxygen saturation using the MAX30100 oximeter sensor.
Oxygen saturation levels are known to be influenced by both age and physiological condition.

• Fig. 12(a) illustrates SpO₂ levels over time across different age groups. The left section of the graph
represents adults, the center shows young children, and the right section represents elderly users.

o Elderly users exhibited relatively lower oxygen levels, possibly due to diminished lung capacity
or chronic conditions.

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• Fig. 12(b) compares oxygen saturation between genders. The data indicates that females typically
exhibited slightly lower SpO₂ levels than males, though both remained within medically acceptable
ranges.

These findings validate the device’s precision in monitoring subtle variations in oxygen saturation and its ability
to distinguish trends based on user demographics.

(a) (b)

Fig. 12. (a) Readings of people’s oxygen rate at various ages.

(b) Different genders’ readings of human oxygen rates

3) Age and Gender-Based Evaluation of Body Temperature

Body temperature is another key physiological parameter measured using the MLX90614 infrared temperature
sensor. The HOT Watch provides consistent and accurate temperature readings across users.

• Fig. 13(a) presents an age-based comparison of body temperature. The graph shows a subtle decline in
average temperature with increasing age, aligning with medical studies that suggest aging bodies tend
to maintain a slightly lower core temperature.

• Fig. 13(b) plots gender-based temperature readings. The graph reveals minimal variation between males
and females, indicating that body temperature is not significantly influenced by gender. Both genders
maintained normal physiological ranges.

This consistency confirms the HOT Watch’s effectiveness in long-term health tracking.

Fig. 13. (a) Observations of the body temperature of people at various ages.

(b) Different genders’ readings of the human body temperature.

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4) Accuracy Comparison with Existing Methods

To further validate the HOT Watch’s effectiveness, a comparative study was conducted with other notable
health monitoring technologies, including:

• Sensor Patch [18]

• WS-IoT [19]

• Neo Wear [21]

Fig. 14 illustrates this comparative analysis, focusing on system accuracy in monitoring health parameters.

Method Accuracy (%)

Sensor Patch 98.00

WS-IoT 98.70

Neo Wear 96.40

HOT Watch (Proposed) 99.40

The HOT Watch outperforms existing techniques with an accuracy of 99.40%, demonstrating:

• A 1.40% improvement over Sensor Patch,

• A 0.70% improvement over WS-IoT,

• A 2.47% improvement over Neo Wear.

These results underscore the reliability and enhanced precision of the HOT Watch, particularly in real-time
monitoring scenarios, making it a superior alternative in the field of wearable health technology.

Fig. 14. Comparison in terms of accuracy

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CHAPTER 5:

ADVANTAGES, DISADVANTAGES AND APPLICATIONS

Advantages of HOT Watch

1. Continuous Health Tracking: HOT Watch enables uninterrupted monitoring of vital metrics,
empowering users to stay aware of their health at all times, especially during physical activities or rest
periods.

2. Multi-Sensor Integration: By combining temperature, oxygen saturation, and heart rate sensors, it
offers a holistic picture of an individual's health.

3. Compact and Wearable Design: The sleek design ensures users feel comfortable wearing it throughout
the day without it being obtrusive or bulky.

4. Emergency Alerts: Automatic notifications when abnormal conditions are detected prevent delays in
medical intervention during health crises.

5. Scalability: The device’s integration with IoT platforms supports broader healthcare systems, such as
hospital monitoring systems or community health programs.

6. Accessibility for Diverse Users: Affordable pricing makes it suitable for rural and urban populations,
ensuring that healthcare access is equitable.

7. User Empowerment: Provides users with direct access to their health data, enabling informed decisions
regarding lifestyle changes, medication adherence, or seeking medical advice.

8. Environmental Impact: Its energy-efficient design reduces power usage, ensuring sustainability.

9. Support for Preventive Healthcare: Promotes proactive health management by identifying potential
health risks before they escalate.

10. High Usability Across Age Groups: Suitable for elderly individuals needing constant monitoring,
active adults tracking fitness, and pediatric care for early health interventions.

Disadvantages of HOT Watch

1. Data Overload: For some users, the constant stream of information may cause unnecessary stress or
worry, especially when interpreting health metrics inaccurately.

2. Durability Concerns: Wearable devices are prone to damage from impact, water, or temperature
fluctuations, which could affect their functionality.

3. Limited Functionality in Extreme Conditions: Environmental factors like extreme heat or cold may
affect sensor performance or battery life.
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4. Health Metric Variability: Factors like skin tone, tattoos, or motion artifacts may interfere with
readings, leading to slight inaccuracies.

5. Connectivity Dependency: Without Bluetooth or internet, users cannot access or share their health
metrics, limiting utility in remote areas.

6. Maintenance Requirements: Regular updates to software, calibration of sensors, and potential repairs
may inconvenience some users.

7. Data Security Risks: Although safeguards are in place, concerns about data theft or breaches can deter
users from adopting IoT health devices.

8. Healthcare Provider Resistance: Some doctors may not trust wearable devices’ data due to variability
or lack of standardization in readings.

9. Battery Longevity: Increased functionality might require improved battery capacity, as frequent
recharging can frustrate users.

10. Limited Accuracy for Rapid Data Changes: During intense physical activity or abrupt health changes,
the device might lag in adjusting readings.

Applications of HOT Watch

1. Chronic Disease Management:

o Effective for tracking vitals in patients with diabetes, hypertension, or respiratory illnesses,
providing data for managing conditions daily.

o Long-term monitoring helps identify trends or anomalies.

2. Fitness Optimization:

o Athletes can use HOT Watch to track heart rate zones during training sessions, ensuring peak
performance and avoiding overexertion.

o Recovery metrics enable safe post-workout activities.

3. Emergency Health Monitoring:

o Alerts users and caregivers to sudden changes in health, such as abnormal heart rate or oxygen
levels, ensuring timely interventions.

o GPS integration aids emergency responders in locating patients.

4. Elderly Support Systems:

o Enables caregivers to monitor the vitals and locations of elderly individuals remotely, ensuring
they are safe and healthy.
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o Notifications about falls or unusual conditions provide added security.

5. Post-Surgical Care:

o Tracks recovery progress through vital metrics, ensuring that patients are healing correctly and
identifying complications promptly.

o Reduces the need for frequent hospital visits by transmitting data remotely to physicians.

6. Mental Health Monitoring:

o Identifies stress patterns by analyzing heart rate variability (HRV) and other metrics, supporting
therapies for individuals with anxiety or emotional strain.

o Useful for professionals in high-stress fields like healthcare or military.

7. Corporate Wellness Programs:

o Employers can offer HOT Watch to employees to promote health tracking and fitness, fostering
a healthier and more productive workplace.

o Aggregated anonymized data can inform workplace health initiatives.

8. Global Health Studies:

o Data from users worldwide can provide insights into regional health trends, supporting
epidemiological studies.

o Useful for monitoring pandemic indicators like oxygen saturation levels or fever spikes.

9. Maternal Health:

o Pregnant women can use HOT Watch to monitor their vitals, ensuring both the mother's and
baby’s health are closely tracked.

o Alerts for abnormalities like fever or heart rate irregularities enhance safety during pregnancy.

10. Rehabilitation and Physical Therapy:

o Helps patients undergoing therapy monitor their vitals during sessions, ensuring activities are
conducted safely.

o Supports therapists in customizing plans based on real-time data.

11. Community Health Monitoring:

o Suitable for deployment in public health initiatives where large groups need monitoring, like
during marathons or festivals.

o Provides organizers with insights to improve safety and medical readiness.

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CHAPTER 6:

CONCLUSION & FUTURE SCOPE

CONCLUSION

In this study, we presented the design, development, and implementation of HOT Watch, an IoT-based
wearable health monitoring system engineered to address the increasing demand for real-time and remote
health tracking in modern healthcare. The proposed solution aims to empower individuals with continuous
access to their physiological metrics while also assisting healthcare providers with reliable and timely data for
assessment and intervention.

The HOT Watch system successfully integrates a suite of biosensors, including the MLX90614 infrared
temperature sensor, AD8232 ECG sensor, and MAX30100 pulse oximeter sensor, within a compact and
wearable form factor. These sensors collectively monitor key health indicators such as body temperature,
heart rate (via ECG analysis), and blood oxygen saturation (SpO₂). A GPS module is also incorporated,
enabling location-based health alerts during emergencies, further enhancing user safety.

Central to the functionality of the HOT Watch is the Pan–Tompkins Algorithm (PTA), which processes the
ECG signal to accurately detect QRS complexes and calculate heart rate. This robust algorithm, known for its
precision in ECG analysis, ensures that real-time heart monitoring is not only possible but highly accurate, even
in varied user conditions. Data collected from the sensors are wirelessly transmitted via the Arduino ESP8266
microcontroller, utilizing its built-in Wi-Fi and Bluetooth capabilities, to a custom-built mobile application.
The app acts as the primary interface for users, allowing them to visualize, interpret, and receive notifications
about their health status.

The hardware implementation and real-world testing validate the effectiveness of the HOT Watch across
different user groups, segmented by age and gender. Analysis indicates that the system effectively tracks
variations in health parameters and adapts well to diverse physiological baselines. Comparative studies reveal
that the HOT Watch achieves an accuracy rate exceeding 99.4%, outperforming existing health wearables
such as Sensor Patch (98%), WS-IoT (98.7%), and Neo Wear (96.4%). These findings underscore the
superior performance and reliability of the proposed device.

Despite its strong performance, the HOT Watch is not without limitations. Factors such as environmental
conditions, motion artifacts during intense physical activity, and long-term battery reliability could affect data
consistency. Moreover, user-specific calibration and data privacy concerns are areas requiring further
exploration.

Looking ahead, future work can focus on:

• Integrating AI and machine learning models for advanced predictive analytics and personalized
health insights.

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• Expanding sensor capabilities to include additional health parameters such as blood pressure, glucose
levels, and hydration.

• Enhancing the mobile application with features like cloud data storage, doctor-patient
communication, and emergency contact alerts.

• Implementing data encryption and security protocols to safeguard user information.

• Conducting large-scale clinical trials to validate device performance across various health conditions
and demographics.

In conclusion, the HOT Watch signifies a forward-thinking approach to wearable health technology,
combining usability, accuracy, and affordability to support both individual health management and broader
public healthcare strategies. As healthcare continues to shift toward preventive and personalized care, devices
like the HOT Watch stand at the forefront of a technological revolution in digital health monitoring.

FUTURE SCOPE

1. AI-Powered Health Insights:

o The HOT Watch can leverage AI to analyze trends in the collected data, identifying subtle
patterns that may indicate early signs of diseases like arrhythmias, diabetes complications, or
respiratory issues.

o AI-powered systems could customize health recommendations based on user behavior,

providing suggestions for diet, exercise, or lifestyle changes tailored to individual needs.

2. Predictive Healthcare Analytics:

o Integrating predictive analytics can help forecast potential health risks. For instance, by
analyzing changes in ECG patterns or oxygen levels over time, the system can warn users about
the possibility of a cardiac event.

o Predictive models could also provide seasonal or age-specific health insights, helping users
prepare for environmental changes or aging-related conditions.

3. Enhanced Data Sharing and Telemedicine Support:

o The device can be optimized for seamless integration with telemedicine platforms, enabling
healthcare providers to remotely access and analyze patient data in real time.

o Health reports generated by the device can be formatted and transmitted directly to physicians,
reducing the need for in-person visits and expediting consultations.

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4. Cloud Integration for Global Accessibility:

o Cloud-based storage could ensure secure, long-term data retention and accessibility across
devices.

o It would also enable family members or caregivers to monitor users’ health from remote
locations, providing peace of mind and immediate support in emergencies.

5. Innovative Use Cases:

o Sports Medicine: The HOT Watch can support athletes by optimizing their performance and
recovery, tracking metrics like heart rate variability (HRV), hydration levels, and oxygen
consumption.

o Rehabilitation: Individuals recovering from surgeries or injuries can benefit from constant
tracking of vital metrics, ensuring they are progressing as expected.

o Pediatric Care: Customized versions could be developed to monitor children’s health,

including detecting fever spikes, abnormal oxygen levels, or stress.

6. Advanced Sensors and Features:

o Future iterations could include sensors for monitoring:

▪ Blood Pressure: Essential for tracking hypertension or stress.

▪ Glucose Levels: A boon for diabetic patients.

▪ Respiratory Rate: Crucial for detecting respiratory ailments or infections.

o Additional features like fall detection could make the device indispensable for elderly care.

7. Gamified Health Management:

o A gamification module within the mobile application could encourage users to adopt healthier
habits. Features like earning rewards for achieving daily step goals or maintaining normal
metrics would keep users engaged and motivated.

8. Privacy and Security Upgrades:

o Future iterations should focus on enhancing encryption and data protection, ensuring
compliance with healthcare privacy regulations like HIPAA (USA) or GDPR (Europe).

o Blockchain-based security mechanisms could be explored for safeguarding user data and
ensuring tamper-proof health records.

9. Solar-Powered Wearables:

o To address the issue of battery life, solar-charging capabilities could be integrated, enabling
users to recharge their devices on the go.
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10. Localization and Accessibility:

o Multi-language support for the mobile app can cater to users worldwide, making it more
inclusive.

o Interfaces designed for individuals with visual or auditory impairments could further enhance
the device’s accessibility.

11. Collaborations with Healthcare Organizations:

o Partnerships with hospitals or health insurance providers can expand the HOT Watch’s role in
preventive healthcare, incentivizing its adoption through discounts or rewards.

o The device could also be used in large-scale health studies, gathering population data to
understand regional or demographic health trends.

12. Environmental Sensors:

o Adding sensors to detect environmental factors like air quality or UV exposure can enhance its
value for users living in urban or high-risk areas.

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CHAPTER 7:

REFERENCE
To maintain the academic integrity and credibility of your report, references should be formatted according to
IEEE standards. Below is a template to guide your citation style, followed by examples derived from the
contents of the HOT Watch document:

Formatting IEEE-Style References

1. For Journal Articles:

Author(s), “Title of the article,” Journal Name, vol. x, no. y, pp. xxx–xxx, Month Year, DOI.

2. For Conference Papers:

Author(s), “Title of the paper,” in Proceedings of the Conference Name, Location, Year, pp. xxx–xxx,
DOI.

3. For Books:
Author(s), Title of the Book, xth ed., City, Country: Publisher, Year, pp. xxx–xxx.

4. For Online Sources:

Author(s), “Title of the document,” Website Name, Accessed: Month Day, Year. [Online]. Available:
URL

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