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KasunikaKarunarathne committed Mar 3, 2025
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108 changes: 94 additions & 14 deletions docs/elements.html
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Expand Up @@ -38,23 +38,24 @@ <h1>Features<br />

<!-- Gesture Recognition -->
<h2>Gesture Recognition</h2>
<p>Our advanced algorithms map positional data into precise control signals, enabling seamless and intuitive smart device automation.</p>
<p>Our gesture-controlled home automation system uses advanced machine learning algorithms to map hand movements into precise control signals, enabling seamless and intuitive smart device automation.</p>

<!-- System Architecture -->
<h2>System Architecture</h2>
<p>The FlickNest system integrates hardware and software for efficient data capture, processing, and execution of smart device commands:</p>
<p>The system integrates hardware and software components for efficient data capture, processing, and execution of smart device commands:</p>
<ul>
<li>The smart bracelet captures positional data using sensors.</li>
<li>Data is processed by the ESP32 microcontroller and sent to the mobile app.</li>
<li>The app translates gestures into commands and communicates with smart devices via Bluetooth or Wi-Fi.</li>
<li>The MPU6050 sensor captures motion data from wrist movements.</li>
<li>Data is processed by the ESP32 microcontroller using the Edge Impulse ML model.</li>
<li>Recognized gestures are sent as MQTT commands to AWS IoT Core, which triggers actions on connected devices like lights, sockets, and door locks.</li>
</ul>

<!-- Key Features -->
<h2>Key Features</h2>
<ul>
<li>Hands-free operation with intuitive gestures.</li>
<li>Inclusive design for accessibility.</li>
<li>Secure and privacy-focused communication protocols.</li>
<li>Dual authentication for door locks (fingerprint and gesture-based).</li>
<li>Real-time device control and status updates via a Flutter mobile app.</li>
<li>Secure communication using MQTT and AWS IoT Core.</li>
</ul>

<!-- Hardware Components -->
Expand All @@ -68,20 +69,67 @@ <h2>Hardware Components</h2>
</thead>
<tbody>
<tr>
<td>ESP32 Module</td>
<td>Processes sensor data and communicates with the app.</td>
<td>ESP32 (NodeMCU)</td>
<td>Processes sensor data, runs the Edge Impulse ML model, and communicates with AWS IoT Core via MQTT.</td>
</tr>
<tr>
<td>Sensors</td>
<td>Capture precise positional data for gesture recognition.</td>
<td>MPU6050 Sensor</td>
<td>Captures precise motion data for gesture recognition.</td>
</tr>
<tr>
<td>Battery Pack</td>
<td>Ensures long-term usability of the smart bracelet.</td>
<td>Fingerprint Scanner</td>
<td>Enables secure fingerprint-based door unlocking.</td>
</tr>
<tr>
<td>Smart Sockets & Light Modules</td>
<td>Controlled via ESP32 to automate appliances and lighting based on gesture commands.</td>
</tr>
</tbody>
</table>

<!-- Software Components -->
<h2>Software Components</h2>
<table>
<thead>
<tr>
<th>Component</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td>Edge Impulse ML Model</td>
<td>Classifies gestures in real-time on the ESP32 microcontroller.</td>
</tr>
<tr>
<td>MQTT Communication</td>
<td>Enables reliable message delivery between ESP32 and AWS IoT Core.</td>
</tr>
<tr>
<td>AWS IoT Core</td>
<td>Acts as the central MQTT broker for managing device commands.</td>
</tr>
<tr>
<td>Firebase</td>
<td>Provides real-time database updates and role-based access control (RBAC).</td>
</tr>
<tr>
<td>Flutter Mobile App</td>
<td>Displays real-time device status, logs, and gesture configuration settings.</td>
</tr>
</tbody>
</table>

<!-- System Workflow -->
<h2>System Workflow</h2>
<ol>
<li><strong>Gesture Captured:</strong> MPU6050 records wrist movements and sends data to the ESP32.</li>
<li><strong>Gesture Classified:</strong> Edge Impulse ML model identifies the gesture and maps it to a specific command.</li>
<li><strong>MQTT Message Published:</strong> ESP32 sends the command to AWS IoT Core via MQTT.</li>
<li><strong>Device Receives Command:</strong> Subscribed devices (door lock, smart socket, or light socket) perform the corresponding action.</li>
<li><strong>Firebase & UI Update:</strong> AWS Lambda updates Firebase, and the Flutter app reflects the real-time status.</li>
</ol>

<!-- Budget Breakdown -->
<h2>Budget Breakdown</h2>
<table>
Expand Down Expand Up @@ -175,7 +223,39 @@ <h2>Budget Breakdown</h2>
</tfoot>
</table>

<!-- Conclusion -->
<h2>Conclusion</h2>
<p>This design ensures a seamless, low-latency, and secure gesture-controlled home automation experience. By integrating ESP32 NodeMCU, fingerprint scanner, smart sockets, and MQTT-based communication, our system enables effortless automation and real-time device control.</p>

<!-- Future Developments -->
<h3>Future Developments</h3>
<ul>
<li>Integration with voice assistants like Alexa and Google Assistant.</li>
<li>Expansion to support more devices and appliances.</li>
<li>Enhanced machine learning models for improved gesture recognition accuracy.</li>
</ul>

<!-- Commercialization Plans -->
<h3>Commercialization Plans</h3>
<ul>
<li>Partnering with smart home device manufacturers for integration.</li>
<li>Launching a subscription-based service for advanced features.</li>
<li>Expanding to international markets with localized support.</li>
</ul>

<!-- GitHub Repo Link -->
<h3>GitHub Repository</h3>
<p>Explore our project on GitHub: <a href="https://github.com/cepdnaclk/e20-3yp-FlickNest" target="_blank">FlickNest GitHub Repo</a></p>

<!-- CN ePortfolio Links -->
<h3>CN ePortfolio Links</h3>
<ul>
<li><a href="https://www.thecn.com/TD852" target="_blank">Dilshan D.M.T. (E/20/069)</a></li>
<li><a href="https://www.thecn.com/VC531" target="_blank">Rathnaweera R.V.C. (E/20/328)</a></li>
<li><a href="https://www.thecn.com/WD211" target="_blank">Dilshan W.M.N.(E/20/455)</a></li>
<li><a href="https://www.thecn.com/KK1737" target="_blank">Karunarathne K.N.P.(E/20/189)
</a></li>
</ul>
</section>
</div>

Expand All @@ -196,4 +276,4 @@ <h2>Budget Breakdown</h2>
<script src="assets/js/main.js"></script>

</body>
</html>
</html>
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112 changes: 95 additions & 17 deletions docs/index.html
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Expand Up @@ -41,17 +41,53 @@ <h1>Welcome to<br />
<div id="main">

<!-- Featured Post -->
<article class="post featured">
<header class="major">
<h2>Introducing FlickNest<br />
Gesture-Controlled Smart Automation</h2>
<p>Our innovative smart bracelet captures hand gestures and translates them into commands for seamless smart device control.</p>
</header>
<a href="#" class="image main"><img src="images/bg.jpg" alt="FlickNest Smart Bracelet" /></a>
<ul class="actions special">
<li><a href="generic.html" class="button large">Learn More</a></li>
</ul>
</article>
<div id="main">
<!-- Featured Post -->
<article class="post featured">
<header class="major">
<h2>FlickNest</h2>
<h3> Gesture-Controlled Smart Automation</h3>
</header>

<!-- Video Section -->
<video width="100%" controls>
<source src="video/demo.mp4" type="video/mp4">
Your browser does not support the video tag.
</video>

<!-- Introduction Section -->
<h2 style="text-align: left;">Introduction</h2>
<p>
Smart home automation has transformed the way we interact with our living spaces, but traditional control methods like smartphone apps and voice commands often fall short in terms of efficiency, accessibility, and convenience. Our project introduces a gesture-controlled home automation system, enabling users to seamlessly control devices using simple hand movements. By integrating machine learning, IoT, and cloud computing, we provide an intuitive, hands-free solution for smart living.
</p>

<!-- Real-World Problem & Solution Section -->
<h2 style="text-align: left;">Real-World Problem & Solution</h2>
<p>
Existing smart home control systems lack inclusivity for individuals with physical disabilities, elderly users, or those engaged in activities where accessing a phone or voice assistant is inconvenient. Moreover, many gesture-based systems depend on external cameras, limiting their reliability and practicality for home automation.
</p>
<p>
Our solution utilizes a wearable FlickNest band equipped with an MPU6050 sensor and ESP32 microcontroller to capture and process hand gestures. The data is processed locally using Edge Impulse’s TinyML model, then transmitted via MQTT to AWS IoT Core, where a Lambda function updates Firebase in real time. This allows connected devices—such as smart locks, lights, and appliances—to respond instantly. A Flutter mobile app keeps users informed of device states, ensuring a seamless and intelligent home automation experience.
</p>

<!-- Impact Section -->
<h2 style="text-align: left;">Impact</h2>
<p>
🔹 Enhanced Accessibility – Empowers individuals with disabilities by providing a hands-free way to interact with smart devices.<br>
🔹 Greater Convenience – Eliminates the need for phones or voice assistants, offering faster and more natural smart home control.<br>
🔹 Improved Security – Enables gesture-based authentication for secure control of smart locks and home automation systems.<br>
🔹 Energy Efficiency – Reduces power consumption by allowing users to control lights and appliances with simple gestures.<br>
🔹 Scalability & Adaptability – Supports multiple environments, making it ideal for homes, offices, and industrial applications.
</p>
<p>
By combining AI-driven gesture recognition, IoT connectivity, and cloud-based automation, this project is redefining the future of smart living with a highly responsive, secure, and inclusive solution.
</p>

<ul class="actions special">
<li><a href="generic.html" class="button large">Learn More</a></li>
</ul>
</article>
</div>

<!-- Posts -->
<section class="posts">
Expand All @@ -60,25 +96,67 @@ <h2>Introducing FlickNest<br />
<h2><a href="#">System Architecture</a></h2>
</header>
<a href="#" class="image fit"><img src="images/system_architecture.jpg" alt="System Architecture" /></a>
<p>From capturing positional data to executing commands, discover how FlickNest seamlessly integrates hardware and software for a smarter future.</p>
<p>The system leverages Edge Impulse's ML capabilities to classify gestures
locally on the ESP32, reducing latency and reliance on cloud processing.
Using MQTT as the primary communication protocol, the ESP32 efficiently
transmits gesture data to AWS IoT Core, ensuring seamless integration with
cloud services. AWS Lambda functions process incoming data and update Firebase,
enabling real-time synchronization with the Flutter frontend. The home automation
devices, such as smart locks, lights, and appliances, act as MQTT subscribers,
allowing immediate response to recognized gestures. This architecture ensures
a scalable, low-latency, and secure IoT ecosystem for home automation.</p>
<ul class="actions special">
<li><a href="generic.html" class="button">Explore</a></li>
</ul>
</article>
<article>
<header>
<h2><a href="#">Gesture Recognition</a></h2>
<h2><a href="#">Data Path</a></h2>
</header>
<a href="#" class="image fit"><img src="images/Gesture_Control.png" alt="Gesture Recognition" /></a>
<p>Advanced algorithms ensure accurate mapping of gestures to control signals, making automation intuitive and reliable.</p>
<p>The data flow begins with MPU6050 capturing motion data, which the ESP32 processes using
Edge Impulse's TinyML model to classify gestures. Once a valid gesture is detected, the ESP32 publishes
the processed data to an MQTT broker, where multiple subscribers, including AWS IoT Core and smart devices,
receive updates. AWS IoT Core routes the data to an AWS Lambda function, which updates Firebase in real time.
The Flutter app listens for Firebase updates, ensuring UI synchronization with device states. Meanwhile,
smart devices subscribed to the MQTT broker react instantly, enabling responsive home automation.</p>
<ul class="actions special">
<li><a href="generic.html" class="button">Learn More</a></li>
</ul>
</article>
</section>
</div>


<!-- Testing Section -->
<article class="post featured">
<h2 style="text-align: left;">Testing</h2>
<p>
We conducted hardware, software, manual, and integration tests to ensure the reliability of our gesture-controlled home automation system.
</p>

<!-- Hardware Testing -->
<h3 style="text-align: left;">1. Hardware Testing</h3>
<p>• Verified gesture motion capture using MPU6050 & ESP32.</p>
<p>• Ensured high accuracy (>85%) in gesture recognition with Edge Impulse.</p>
<p>• Measured response time for real-time device control.</p>

<!-- Software Testing -->
<h3 style="text-align: left;">2. Software Testing</h3>
<p>• Used Flutter Fix to resolve UI issues and tested real-time Firebase synchronization.</p>
<p>• Validated MQTT communication using test clients, ensuring stable, low-latency messaging.</p>
<p>• Tested AWS Lambda function, confirming efficient data processing and Firebase updates.</p>

<!-- Manual & Integration Tests -->
<h3 style="text-align: left;">3. Manual & Integration Tests</h3>
<p>• Performed 100+ gesture recognition tests to validate ML accuracy.</p>
<p>• Conducted end-to-end system tests, ensuring seamless interaction between ESP32, MQTT, AWS, Firebase, and Flutter.</p>
<p>• Tested network stability, multi-user scenarios, and smart device compatibility.</p>

<ul class="actions special">
<li><a href="generic.html" class="button large">Learn More</a></li>
</ul>
</article>


<!-- Copyright -->
<div id="copyright">
<ul><li>&copy; FlickNest</li><li>Design: Team Byte crafters</li></ul>
Expand All @@ -96,4 +174,4 @@ <h2><a href="#">Gesture Recognition</a></h2>
<script src="assets/js/main.js"></script>

</body>
</html>
</html>
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