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Design and Implementation of Fall Detection System Based on Deep Learning and Image Processing in Cloud Environment

Design and Implementation of Fall Detection System Based on Deep Learning and Image Processing in Cloud Environment

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DEDICATION

This research material, titled “Design and Implementation of Fall Detection System Based on Deep Learning and Image Processing in Cloud Environment” is dedicated to God for His boundless grace and guidance. It is also a tribute to all computer enthusiasts whose contributions made my research journey smoother and enriched my documentation process, making the experience truly fulfilling.



ACKNOWLEDGEMENT

I am profoundly grateful to everyone who contributed to the successful completion of this project. I am especially grateful to my Supervisor (Name), the Head of Department (Name), and the Lecturers in the Department of Computer Science (CS) for their invaluable guidance and support. I also acknowledge the contributions of authors and scholars whose works on Design and Implementation of Fall Detection System Based on Deep Learning and Image Processing in Cloud Environment provided essential insights. Special thanks go to my study area (and any funding organizations, if applicable) for their financial assistance. I am equally thankful to stakeholders, including mentors, teachers, and colleagues, for their encouragement and support. Finally, I deeply appreciate my family and friends for their patience and unwavering support throughout this journey. Your contributions have been instrumental in making this research a reality.



TABLE OF CONTENTS

PRELIMINARY PAGES

CHAPTER ONE

INTRODUCTION

  • 1.1 Introduction
  • 1.2 Background of Study
  • 1.3 Statement of Problem
  • 1.4 Aim and Objectives of the Study
  • 1.5 Significance of Study
  • 1.6 Scope of Study
  • 1.7 Limitations of the Study
  • 1.8 Definition of Terms

CHAPTER TWO

LITERATURE REVIEW

  • 2.1 Introduction
  • 2.2 Conceptual Review of Image Processing
  • 2.3 Theoretical Framework of Deep Learning in Cloud Environment
  • 2.3.1 Image Processing Cloud System Environment
  • 2.3.2 Cloud Infrastructure for Image Processing as Service
  • 2.3.3 Image-based Cloud Platform
  • 2.3.4 Image Processing Software as Service
  • 2.3.5 Image Processing Cloud Computing reviewing Factors
  • 2.3.6 Cloud Computing Impacts on Image Processing
  • 2.4 Review of Related Literature

CHAPTER THREE

SYSTEM ANALYSIS AND DESIGN

  • 3.1 Methodology Adopted
  • 3.1.1 Problem Identification Using SSADM
  • 3.2 Analysis of the Existing System
  • 3.2.1 Dataflow of the Existing System
  • 3.2.2 Disadvantages Of The Existing System
  • 3.2.3 Weakness of the existing System
  • 3.3 Feasibility Study
  • 3.3.1 Economic Feasibility
  • 3.3.2 Technical Feasibility
  • 3.3.3 Operational Feasibility
  • 3.4 Analysis of the Proposed System
  • 3.4.1 Data Flow Diagram of the Proposed System
  • 3.4.2 Advantages of the Proposed System
  • 3.4.3 Justification of the Proposed System
  • 3.5 Functional Requirements
  • 3.5.1 Use Case Diagram Of The Admin / User Privileges
  • 3.6 Data Requirements
  • 3.7 High Level Model of the Proposed System

CHAPTER FOUR

SYSTEM DESIGN AND IMPLEMENTATION

  • 4.1 Objectives of the Design
  • 4.2 Cohesion and Decomposition High level Model
  • 4.3 Control Center / Overall Dataflow Diagram
  • 4.3.1 Proposed System Operation Flowchart
  • 4.4 System Specification and Design
  • 4.4.1 Input and Output Specification
  • 4.4.2 Database Specification and Design
  • 4.4.3 Data Dictionary
  • 4.5 Choice and Justification of Programming Language
  • 4.6 Program Documentation
  • 4.7 Implementation Techniques
  • 4.8 Programming Module Specification
  • 4.8.1 Installation
  • 4.8.2 Security Design Specification
  • 4.8.3 System Architecture
  • 4.9 Computer Hardware Minimum Requirement
  • 4.10 Software Requirement
  • 4.11 Personnel / User Training

CHAPTER FIVE

SUMMARY, CONCLUSION AND RECOMMENDATION

  • 5.1 Introduction
  • 5.2 Summary
  • 5.3 Conclusion
  • 5.4 Recommendation

REFERENCES

APPENDIX A - “SOURCE CODE”

APPENDIX B - “OBJECT PROGRAM”


ABSTRACT

Deep learning is a machine learning method based on characterization of data learning. Fall detection system based on cloud environment usually embedded in the human activity area with wireless sensor networks, infrared sensors, sound sensors, pressure sensors, radar and other sensors to capture body movements and posture information. The aim of the study is to design and implement a Fall Detection System Based on Deep Learning and Image Processing in Cloud Environment. In achieving this aim, the following specific objectives were laid out as follows to develop an application software that will provides a new way and a new method for the realization and application of fall detection and survey on cloud computing infrastructure and platform for image processing as service.

The motivation that led to the implementation of the proposed system is that the deep learning based fall detection systems share the general shortcomings of the principal methods used to infer the events. Due to the lack of studies in the adoption of a cloud computing platform for image processing as a service, cloud computing environment within needed deployment supposed to allow experts to process and analyze different type of images in that concept.

The methodology adopted in this study is the object oriented analysis and design methodology (OOADM) which is a technical approach for analyzing and designing an application or system by applying object throughout the software development process. The programming language used is HTML, CSS, JAVASCRIPT, PHP, SQL and JQUERY. The reason why web programming languages was used is because, it is platform independent and it is a web based application. The significance of this study is in the evaluation of the current state of the cloud-based image processing algorithms implementations with a service level agreement requirement. The expected result is a computerized Fall Detection System Based on Deep Learning and Image Processing in Cloud Environment that will provide device support for cloud platform based image processing and evaluate the use of the cloud system environment for image processing algorithms, analysis, and storage capacity from point of view of architecture orientation and service level agreement.


Design and Implementation of Fall Detection System Based on Deep Learning and Image Processing in Cloud Environment

CHAPTER ONE

1.1 Introduction

Fall detection system based on cloud environment usually embedded in the human activity area with wireless sensor networks, infrared sensors, sound sensors, pressure sensors, radar and other sensors to capture body movements and posture information. Most environmental-based devices use pressure sensors to detect and track objects (Lee et al., 2013; Cheng et al., 2013; Feldwieser et al., 2014; Liu et al., 2013). As it senses all the pressure changes around the object, it is prone to false alarms and reduces the accuracy of fall detection. The image processing Cloud System Environment (CSE) consists of three establishing service layers: an infrastructure, platform, and software (Liu et al., 2016). All these layers will formulate the needed environment for image processing cloud model, which could be one or more of the deployment models: Public, Community, Private and Hybrid Clouds.

As a prelude to other parts of this study, this chapter will discuss the background upon which this study was initiated, the statement of problems that led to this study, the Aim and Objectives of the study. Others are Significance of the study, Scope of work, Limitations of the Study and Definition of technical terms.

…

CHAPTER TWO

2.0 Literature Review

2.1 Introduction

This chapter focuses on the review of related literature. A literature review includes the current knowledge as well as theoretical and methodological contributions to a particular topic. It documents the state of the art with respect to the topic you are writing. It surveys the literature in the topic selected. In this research work the literature review includes the conceputal review, theoretical framework, the review of related literature …

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