DESIGN AND IMPLEMENTATION OF FALL DETECTION SYSTEM BASED ON DEEP LEARNING AND IMAGE PROCESSING IN CLOUD ENVIRONMENT
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.
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
The review of relevant literature is the main topic of this chapter. A literature review covers theoretical and methodological contributions to a certain issue in addition to the state of knowledge at the time of writing. It provides information about the state of the art in relation to the subject you are writing about. It examines the body of work on the chosen subject. The literature evaluation in this study comprises the …
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TABLE OF CONTENTSPRELIMINARY PAGES
- Title page
- Approval page
- Dedication
- Acknowledgement
- Table of Contents
- Abstract
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
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
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
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
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”
Disclaimer for Complete Material Utilization
The displayed research work titled "Design and Implementation of Fall Detection System Based on Deep Learning and Image Processing in Cloud Environment" is stated as a research guideline towards accomplishing your assigned project / seminar research work. All the research materials on this website are ONLY for research purposes and should be used as a guideline in developing your research work. For no reason should you copy word for word as projectlist.com.ng will not be liable for any who copied the material. By ordering the complete research guideline, it signifies that you've accepted our terms of service.
PRELIMINARY PAGES
- Title page
- Approval page
- Dedication
- Acknowledgement
- Table of Contents
- Abstract
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
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
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
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
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”
Disclaimer for Complete Material Utilization
The displayed research work titled "Design and Implementation of Fall Detection System Based on Deep Learning and Image Processing in Cloud Environment" is stated as a research guideline towards accomplishing your assigned project / seminar research work. All the research materials on this website are ONLY for research purposes and should be used as a guideline in developing your research work. For no reason should you copy word for word as projectlist.com.ng will not be liable for any who copied the material. By ordering the complete research guideline, it signifies that you've accepted our terms of service.
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