The Internet of Medical Things (IoMT): Healthcare Transformation (Advances in Learning Analytics for Intelligent Cloud-IoT Systems)

Cover
Half-Title Page
Series Page
Title Page
Copyright Page
Contents
Preface
1 In Silico Molecular Modeling and Docking Analysis in Lung Cancer Cell Proteins
	1.1 Introduction
	1.2 Methodology
		1.2.1 Sequence of Protein
		1.2.2 Homology Modeling
		1.2.3 Physiochemical Characterization
		1.2.4 Determination of Secondary Models
		1.2.5 Determination of Stability of Protein Structures
		1.2.6 Identification of Active Site
		1.2.7 Preparation of Ligand Model
		1.2.8 Docking of Target Protein and Phytocompound
	1.3 Results and Discussion
		1.3.1 Determination of Physiochemical Characters
		1.3.2 Prediction of Secondary Structures
		1.3.3 Verification of Stability of Protein Structures
		1.3.4 Identification of Active Sites
		1.3.5 Target Protein-Ligand Docking
	1.4 Conclusion
	References
2 Medical Data Classification in Cloud Computing Using Soft Computing With Voting Classifier: A Review
	2.1 Introduction
		2.1.1 Security in Medical Big Data Analytics
			2.1.1.1 Capture
			2.1.1.2 Cleaning
			2.1.1.3 Storage
			2.1.1.4 Security
			2.1.1.5 Stewardship
	2.2 Access Control–Based Security
		2.2.1 Authentication
			2.2.1.1 User Password Authentication
			2.2.1.2 Windows-Based User Authentication
			2.2.1.3 Directory-Based Authentication
			2.2.1.4 Certificate-Based Authentication
			2.2.1.5 Smart Card–Based Authentication
			2.2.1.6 Biometrics
			2.2.1.7 Grid-Based Authentication
			2.2.1.8 Knowledge-Based Authentication
			2.2.1.9 Machine Authentication
			2.2.1.10 One-Time Password (OTP)
			2.2.1.11 Authority
			2.2.1.12 Global Authorization
	2.3 System Model
		2.3.1 Role and Purpose of Design
			2.3.1.1 Patients
			2.3.1.2 Cloud Server
			2.3.1.3 Doctor
	2.4 Data Classification
		2.4.1 Access Control
		2.4.2 Content
		2.4.3 Storage
		2.4.4 Soft Computing Techniques for Data Classification
	2.5 Related Work
	2.6 Conclusion
	References
3 Research Challenges in Pre-Copy Virtual Machine Migration in Cloud Environment
	3.1 Introduction
		3.1.1 Cloud Computing
			3.1.1.1 Cloud Service Provider
			3.1.1.2 Data Storage and Security
		3.1.2 Virtualization
			3.1.2.1 Virtualization Terminology
		3.1.3 Approach to Virtualization
		3.1.4 Processor Issues
		3.1.5 Memory Management
		3.1.6 Benefits of Virtualization
		3.1.7 Virtual Machine Migration
			3.1.7.1 Pre-Copy
			3.1.7.2 Post-Copy
			3.1.7.3 Stop and Copy
	3.2 Existing Technology and Its Review
	3.3 Research Design
		3.3.1 Basic Overview of VM Pre-Copy Live Migration
		3.3.2 Improved Pre-Copy Approach
		3.3.3 Time Series–Based Pre-Copy Approach
		3.3.4 Memory-Bound Pre-Copy Live Migration
		3.3.5 Three-Phase Optimization Method (TPO)
		3.3.6 Multiphase Pre-Copy Strategy
	3.4 Results
		3.4.1 Finding
	3.5 Discussion
		3.5.1 Limitation
		3.5.2 Future Scope
	3.6 Conclusion
	References
4 Estimation and Analysis of Prediction Rate of Pre-Trained Deep Learning Network in Classification of Brain Tumor MRI Images
	4.1 Introduction
	4.2 Classes of Brain Tumors
	4.3 Literature Survey
	4.4 Methodology
	4.5 Conclusion
	References
5 An Intelligent Healthcare Monitoring System for Coma Patients
	5.1 Introduction
	5.2 Related Works
	5.3 Materials and Methods
		5.3.1 Existing System
		5.3.2 Proposed System
		5.3.3 Working
		5.3.4 Module Description
			5.3.4.1 Pulse Sensor
			5.3.4.2 Temperature Sensor
			5.3.4.3 Spirometer
			5.3.4.4 OpenCV (Open Source Computer Vision)
			5.3.4.5 Raspberry Pi
			5.3.4.6 USB Camera
			5.3.4.7 AVR Module
			5.3.4.8 Power Supply
			5.3.4.9 USB to TTL Converter
			5.3.4.10 EEG of Comatose Patients
	5.4 Results and Discussion
	5.5 Conclusion
	References
6 Deep Learning Interpretation of Biomedical Data
	6.1 Introduction
	6.2 Deep Learning Models
		6.2.1 Recurrent Neural Networks
		6.2.2 LSTM/GRU Networks
		6.2.3 Convolutional Neural Networks
		6.2.4 Deep Belief Networks
		6.2.5 Deep Stacking Networks
	6.3 Interpretation of Deep Learning With Biomedical Data
	6.4 Conclusion
	References
7 Evolution of Electronic Health Records
	7.1 Introduction
	7.2 Traditional Paper Method
	7.3 IoMT
	7.4 Telemedicine and IoMT
		7.4.1 Advantages of Telemedicine
		7.4.2 Drawbacks
		7.4.3 IoMT Advantages with Telemedicine
		7.4.4 Limitations of IoMT With Telemedicine
	7.5 Cyber Security
	7.6 Materials and Methods
		7.6.1 General Method
		7.6.2 Data Security
	7.7 Literature Review
	7.8 Applications of Electronic Health Records
		7.8.1 Clinical Research
			7.8.1.1 Introduction
			7.8.1.2 Data Significance and Evaluation
			7.8.1.3 Conclusion
		7.8.2 Diagnosis and Monitoring
			7.8.2.1 Introduction
			7.8.2.2 Contributions
			7.8.2.3 Applications
		7.8.3 Track Medical Progression
			7.8.3.1 Introduction
			7.8.3.2 Method Used
			7.8.3.3 Conclusion
		7.8.4 Wearable Devices
			7.8.4.1 Introduction
			7.8.4.2 Proposed Method
			7.8.4.3 Conclusion
	7.9 Results and Discussion
	7.10 Challenges Ahead
	7.11 Conclusion
	References
8 Architecture of IoMT in Healthcare
	8.1 Introduction
		8.1.1 On-Body Segment
		8.1.2 In-Home Segment
		8.1.3 Network Segment Layer
		8.1.4 In-Clinic Segment
		8.1.5 In-Hospital Segment
		8.1.6 Future of IoMT?
	8.2 Preferences of the Internet of Things
		8.2.1 Cost Decrease
		8.2.2 Proficiency and Efficiency
		8.2.3 Business Openings
		8.2.4 Client Experience
		8.2.5 Portability and Nimbleness
	8.3 IoMT Progress in COVID-19 Situations: Presentation
		8.3.1 The IoMT Environment
		8.3.2 IoMT Pandemic Alleviation Design
		8.3.3 Man-Made Consciousness and Large Information Innovation in IoMT
	8.4 Major Applications of IoMT
	References
9 Performance Assessment of IoMT Services and Protocols
	9.1 Introduction
	9.2 IoMT Architecture and Platform
		9.2.1 Architecture
		9.2.2 Devices Integration Layer
	9.3 Types of Protocols
		9.3.1 Internet Protocol for Medical IoT Smart Devices
			9.3.1.1 HTTP
			9.3.1.2 Message Queue Telemetry Transport (MQTT)
			9.3.1.3 Constrained Application Protocol (CoAP)
			9.3.1.4 AMQP: Advanced Message Queuing Protocol (AMQP)
			9.3.1.5 Extensible Message and Presence Protocol (XMPP)
			9.3.1.6 DDS
	9.4 Testing Process in IoMT
	9.5 Issues and Challenges
	9.6 Conclusion
	References
10 Performance Evaluation of Wearable IoT-Enabled Mesh Network for Rural Health Monitoring
	10.1 Introduction
	10.2 Proposed System Framework
		10.2.1 System Description
		10.2.2 Health Monitoring Center
			10.2.2.1 Body Sensor
			10.2.2.2 Wireless Sensor Coordinator/Transceiver
			10.2.2.3 Ontology Information Center
			10.2.2.4 Mesh Backbone-Placement and Routing
	10.3 Experimental Evaluation
	10.4 Performance Evaluation
		10.4.1 Energy Consumption
		10.4.2 Survival Rate
		10.4.3 End-to-End Delay
	10.5 Conclusion
	References
11 Management of Diabetes Mellitus (DM) for Children and Adults Based on Internet of Things (IoT)
	11.1 Introduction
		11.1.1 Prevalence
		11.1.2 Management of Diabetes
		11.1.3 Blood Glucose Monitoring
		11.1.4 Continuous Glucose Monitors
		11.1.5 Minimally Invasive Glucose Monitors
		11.1.6 Non-Invasive Glucose Monitors
		11.1.7 Existing System
	11.2 Materials and Methods
		11.2.1 Artificial Neural Network
		11.2.2 Data Acquisition
		11.2.3 Histogram Calculation
		11.2.4 IoT Cloud Computing
		11.2.5 Proposed System
		11.2.6 Advantages
		11.2.7 Disadvantages
		11.2.8 Applications
		11.2.9 Arduino Pro Mini
		11.2.10 LM78XX
		11.2.11 MAX30100
		11.2.12 LM35 Temperature Sensors
	11.3 Results and Discussion
	11.4 Summary
	11.5 Conclusion
	References
12 Wearable Health Monitoring Systems Using IoMT
	12.1 Introduction
	12.2 IoMT in Developing Wearable Health Surveillance System
		12.2.1 A Wearable Health Monitoring System with Multi-Parameters
		12.2.2 Wearable Input Device for Smart Glasses Based on a Wristband-Type Motion-Aware Touch Panel
		12.2.3 Smart Belt: A Wearable Device for Managing Abdominal Obesity
		12.2.4 Smart Bracelets: Automating the Personal Safety Using Wearable Smart Jewelry
	12.3 Vital Parameters That Can Be Monitored Using Wearable Devices
		12.3.1 Electrocardiogram
		12.3.2 Heart Rate
		12.3.3 Blood Pressure
		12.3.4 Respiration Rate
		12.3.5 Blood Oxygen Saturation
		12.3.6 Blood Glucose
		12.3.7 Skin Perspiration
		12.3.8 Capnography
		12.3.9 Body Temperature
	12.4 Challenges Faced in Customizing Wearable Devices
		12.4.1 Data Privacy
		12.4.2 Data Exchange
		12.4.3 Availability of Resources
		12.4.4 Storage Capacity
		12.4.5 Modeling the Relationship Between Acquired Measurement and Diseases
		12.4.6 Real-Time Processing
		12.4.7 Intelligence in Medical Care
	12.5 Conclusion
	References
13 Future of Healthcare: Biomedical Big Data Analysis and IoMT
	13.1 Introduction
	13.2 Big Data and IoT in the Healthcare Industry
	13.3 Biomedical Big Data Types
		13.3.1 Electronic Health Records
		13.3.2 Administrative and Claims Data
		13.3.3 International Patient Disease Registries
		13.3.4 National Health Surveys
		13.3.5 Clinical Research and Trials Data
	13.4 Biomedical Data Acquisition Using IoT
		13.4.1 Wearable Sensor Suit
		13.4.2 Smartphones
		13.4.3 Smart Watches
	13.5 Biomedical Data Management Using IoT
		13.5.1 Apache Spark Framework
		13.5.2 MapReduce
		13.5.3 Apache Hadoop
		13.5.4 Clustering Algorithms
		13.5.5 K-Means Clustering
		13.5.6 Fuzzy C-Means Clustering
		13.5.7 DBSCAN
	13.6 Impact of Big Data and IoMT in Healthcare
	13.7 Discussions and Conclusions
	References
14 Medical Data Security Using Blockchain With Soft Computing Techniques: A Review
	14.1 Introduction
	14.2 Blockchain
		14.2.1 Blockchain Architecture
		14.2.2 Types of Blockchain Architecture
		14.2.3 Blockchain Applications
		14.2.4 General Applications of the Blockchain
	14.3 Blockchain as a Decentralized Security Framework
		14.3.1 Characteristics of Blockchain
		14.3.2 Limitations of Blockchain Technology
	14.4 Existing Healthcare Data Predictive Analytics Using Soft Computing Techniques in Data Science
		14.4.1 Data Science in Healthcare
	14.5 Literature Review: Medical Data Security in Cloud Storage
	14.6 Conclusion
	References
15 Electronic Health Records: A Transitional View
	15.1 Introduction
	15.2 Ancient Medical Record, 1600 BC
	15.3 Greek Medical Record
	15.4 Islamic Medical Record
	15.5 European Civilization
	15.6 Swedish Health Record System
	15.7 French and German Contributions
	15.8 American Descriptions
	15.9 Beginning of Electronic Health Recording
	15.10 Conclusion
	References
Index
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