This document presents a proposal for a final year project that uses convolutional neural networks for hand gesture recognition to control devices in a home automation system. The proposal outlines introducing CNNs and home automation, the problem of accurately recognizing hand gestures, and the aims to develop an accurate and user-friendly gesture recognition system to control devices. The methodology describes collecting and preprocessing training data, configuring and training the CNN model in Python using common libraries, and deploying the trained model. Expected results are for the system to be highly accurate, fast, robust, user-friendly, and efficient to run on low-power IoT devices. A project cost estimate and timeline are also provided.

1. FINAL YEAR
PROJECT PROPOSAL
USE OF CONVOLUTIONAL
NEURAL NETWORK (CNN) IN
HAND GESTURE
RECOGNITION FOR DEVICE
CONTROL IN HOME
AUTOMATION
BY
ONYENEKE ANTHONY CHIDUBEM
MATRIC NUMBER:
200353
DEPARTMENT:
ELECTRICAL & ELECTRONIC
ENGINEERING
SUPERVISOR:
DR. OLUYEMI E. ADETOYI
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2. PRESENTATION OUTLINE
 Introduction
 Problem Statement
 Aims & Objectives
 Methodology
 Expected Result
 Project Cost Estimate
 Timeline of the Project
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3. INTRODUCTION
 Convolutional neural network (CNN) is a type of feed-forward neural network
designed for processing structured arrays of data such as images.
 Applications: image analysis & classification, natural language processing etc.
 Home automation is the automatic control of electronic devices in homes.
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An Automated Home

4. PROBLEM STATEMENT
Accurately recognizing hand gestures can be a challenging task due to:
 Variations in hand shape
 Variations in orientation
 Movement
 Presence of noise and background distractions in the visual input
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5. AIMS & OBJECTIVES
 To develop a system that accurately detects and classifies hand gestures.
 To develop a system that controls devices using hand gestures.
 To design of a user-friendly interface for the hand gesture recognition system.
 Optimizing the use of CNN for hand gesture recognition in terms of
computational efficiency and power consumption, with the goal of making the
system suitable for deployment on low-power IoT devices.
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6. METHODOLOGY
Block Diagram
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7. METHODOLOGY
Flowchart
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8. METHODOLOGY
IMAGE PROCESSING TRAINING PROCEDURES
1. Collection of Data
2. Pre-processing of Data
3. Configuration of the CNN
4. Training the CNN and building of the model
Image Processing Training Procedures Flowchart
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9. METHODOLOGY
BUILDING OF THE MODEL
Python will be used for building of the model. The following libraries will be used:
 Keras – Creation of the CNN classifier
 Sklearn – Confusion matrix calculation
 OpenCV – Image processing
 Numpy – Array operations
 Matplotlib – Visualization of confusion matrix, model accuracy, and loss values
NOTE: Model will be trained on a laptop before it is deployed to a virtual machine on the
AWS cloud.
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10. EXPECTED RESULT
The hand gesture recognition system would be:
 highly accurate, fast and robust to variations in hand gestures and background
noise.
 user-friendly, with a simple and intuitive interface.
 efficient and lightweight, with algorithms that are designed to run on low-power
IoT devices.
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11. PROJECT COST ESTIMATE
S/N COMPONENT QUANTITY COST
(NAIRA)
1. Liquid Crystal Display (LCD) 1 2000
2. Raspberry Pi 4 1 89000
3. Dupont Lines 1 pack 1000
4. Soldering Lead 1 1000
5. 1k ohm Resistor 1 pack 500
6. Push Button 1 600
7. Socket Box 3 6000
8. Plug 1 2000
9. 3-Channel Relay Module 1 9000
10. Pi Camera 1 3500
Total Project Cost Estimate = 114,600 naira 11

12. TIMELINE OF THE PROJECT
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13. THANK YOU !
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