Intelligent Internet of Things for Smart Healthcare Systems

✍ Scribed by Durgesh Srivastava, Neha Sharma, Deepak Sinwar, Jabar H. Yousif, Hari Prabhat Gupta

• Publisher: CRC Press
• Year: 2022
• Tongue: English
• Leaves: 269
• Series: Advances in Smart Healthcare Technologies
• Category: Library

✦ Synopsis

The book focuses on developments in artificial intelligence (AI) and internet of things (IoT) integration for smart healthcare, with an emphasis on current methodologies and frameworks for the design, growth, implementation, and creative use of such convergence technologies to provide insight into smart healthcare service demands. Concepts like signal recognition, computation, internet of health stuff, and so forth and their applications are covered. Development in connectivity and intelligent networks allowing for social adoption of ambient intelligence is also included.

Features
•Introduces Intelligent IoT as applicable to the key areas of smart healthcare.
•Discusses computational intelligence and IoT-based optimizations of smart healthcare systems
•Explores effective management of healthcare systems using dedicated IoT-based infrastructures
•Includes dedicated chapters on securing patient’s confidential data
•Reviews diagnosis of critical diseases from medical imaging using advanced deep learning-based approaches

This book is aimed at researchers, professionals, and graduate students in intelligent systems, big data, cloud computing, information security, and healthcare systems.

✦ Table of Contents

Cover
Half Title
Series Page
Title Page
Copyright Page
Table of Contents
Editors
List of Contributors
Preface
Chapter 1 Internet of Things in Smart and Intelligent Healthcare Systems
1.1 Introduction
1.2 Implementation of IoT in Smart Healthcare
1.2.1 Recognition Technology
1.2.2 Transmission Technology
1.2.3 Location Technology
1.3 Role of IoT in Smart Healthcare
1.3.1 Services Performed by IoT in Smart Healthcare
1.3.2 Applications of IoT in Smart Healthcare
1.3.2.1 Applications Related to Resource Allocation and Tracking
1.3.2.2 Applications Based on Remote Patient Monitoring Systems
1.3.2.3 Robotics-Based Smart Health Applications
1.3.2.4 Smart Hospital-Based Applications
1.3.2.5 Hospital Information Management System
1.4 Challenges and Observations Made
1.5 Future Scope
1.5.1 What Is the Real Scope of the IoMT in the Future?
1.5.2 What Does the Future Hold for Smart Healthcare?
1.6 Conclusion
References
Chapter 2 Applications of i-IoT for Smart Healthcare Systems
2.1 Introduction
2.2 i-IoT in the Healthcare Industry
2.3 i-IoT in COVID-19
2.4 Core Elements and Sensors Used in i-IoT for Remote Monitoring
2.5 i-IoT Applications in the Healthcare Sector
2.5.1 Diabetes
2.5.2 Electrocardiogram (ECG)
2.5.3 Internal Body Temperature
2.5.4 Global Positioning System (GPS)
2.6 Network Architecture of i-IoT
2.7 Conclusion
References
Chapter 3 Technological Shift of the Healthcare System Using IoT
3.1 Introduction
3.2 Research Methodology
3.3 Background of an IoT Technology
3.4 Emergence of IoT-Based Healthcare during the COVID-19 Pandemic
3.5 Healthcare Architecture
3.5.1 The Three-Layer Architecture
3.5.2 The Five-Layer Architecture
3.6 Applications
3.6.1 Single Condition Application
3.6.1.1 Glucose-Level Sensing
3.6.1.2 Blood Pressure Monitor
3.6.1.3 Body Temperature Monitoring
3.6.1.4 Oxygen Saturation Monitoring
3.6.1.5 Electrocardiogram Monitoring
3.6.2 Cluster Condition Application
3.6.2.1 Wheelchair Management System
3.6.2.2 Rehabilitation System
3.6.2.3 Healthcare Solutions Using Smartphones
3.6.2.4 Healthcare Solutions Using Music as a Therapy
3.7 Conclusion
References
Chapter 4 IoT-Based Healthcare System: Cloud Data Governance
4.1 Introduction
4.1.1 Technologies for IoT SHCS
4.1.1.1 Big Data Management
4.1.1.2 Information Security
4.1.1.3 Cloud Data Governance
4.1.2 Issues and Challenges
4.1.3 Summary
4.2 Data Management
4.2.1 Why Data Management for IoT SHCS?
4.2.2 Sources of IoT Data
4.2.3 Design Primitives for Data Management
4.2.3.1 IoT Data Collection
4.2.3.2 IoT Database System Design
4.2.3.3 IoT Data Processing
4.2.3.4 IoT with Big Data Characteristics
4.2.4 IoT Data Management Reference Model
4.2.4.1 Data Cleaning Layer
4.2.4.2 Event-Processing Layer
4.2.4.3 Storage and Analysis Layer
4.2.4.4 Issues and Challenges
4.2.5 Summary
4.3 Information Security
4.3.1 Importance of Security in IoT SHCS
4.3.2 Information Security Life Cycle
4.3.2.1 Identity
4.3.2.2 Protect
4.3.2.3 Detect
4.3.2.4 Respond
4.3.2.5 Recover
4.3.2.6 Issues and Challenges
4.3.3 Summary
4.4 IoT SHCS Model
4.4.1 Significance of the IoT SHCS Model
4.4.2 WBAN for IoT SHCSs
4.4.2.1 Tier 1: Intra-BAN Communication
4.4.2.2 Tier 2: Inter-BAN Communication
4.4.2.3 Tiers 3 and 4: Beyond BAN Communication
4.4.2.4 Issues and Challenges
4.4.3 Summary
4.5 Conclusion
References
Chapter 5 Information Security and Data Management for IoT Smart Healthcare
5.1 Introduction
5.2 Healthcare Systems
5.2.1 Patient Care
5.3 Smart Healthcare Systems
5.3.1 Healthcare Team (Medical Robots)
5.3.2 Community Resources Providers
5.4 IoT-Enabled Smart Healthcare Systems
5.4.1 Internet of Things (IoT)
5.4.2 IoT-Enabled Healthcare System
5.5 Information Security for IoT-Enabled Smart Healthcare Systems
5.5.1 Information Security Threats
5.5.2 Information Security Technologies Applied in Healthcare
5.5.3 Healthcare Cybersecurity Solutions
5.6 Data Management for IoT-Enabled Smart Healthcare Systems
5.7 Conclusion
References
Chapter 6 AI-Assisted Big Data Analytics for Smart Healthcare Systems
6.1 Introduction
6.2 Related Works.
6.3 AI and Big Data Background
6.3.1 Big Data in Healthcare
6.3.2 AI in Healthcare
6.4 Importance of AI and BDA in SHCSs
6.5 Common Methodologies and Their Implications
6.6 Types of AI-BDA for SHCSs
6.6.1 Recommendation Systems
6.6.2 Prediction Systems
6.6.3 Systems That Aggregate Data
6.6.4 Systems That Provide Living Assistance
6.7 Emerging Trends in Big Data and AI in Terms of SHCSs
6.8 Challenges and Open Issues
6.8.1 Security
6.8.2 Scarcity of Data
6.8.3 Big Data Analytics
6.9 Conclusion
References
Chapter 7 Cloud Computing-Assisted Real-Time Health Monitoring and Tracking
7.1 Introduction
7.2 Cloud Computing for Healthcare
7.2.1 An Introduction to Cloud Computing
7.2.1.1 Infrastructure-as-a-Service
7.2.1.2 Platform-as-a-Service
7.2.1.3 Software-as-a-Service
7.2.2 Benefits of Cloud Computing for Healthcare
7.2.2.1 Collaboration and Data Sharing
7.2.2.2 Scalability and Elasticity
7.2.2.3 Security, Availability, and Reliability
7.2.2.4 Cost and Speed
7.3 Cloud Computing-Assisted Real-Time Health Monitoring and Tracking
7.3.1 Healthcare Applications with IoT and Fog Computing
7.3.2 Real-Time Health Monitoring and Tracking
7.4 The Need for Cloud Computing and Healthcare in the COVID-19 Era
7.5 Cloud Computing in Real-Time Healthcare Challenges
7.6 Conclusions
References
Chapter 8 Supervision of Worldwide Healthcare through an IoT-Based System
8.1 Introduction
8.2 Background
8.2.1 Expansion of the Healthcare System
8.2.2 Healthcare and IoT by Using IT Infrastructure
8.3 A Model for IoT-Based Healthcare Systems in the Future
8.3.1 Central Nodes and Wearable Sensors
8.3.2 Communications within a Short Distance
8.3.3 Long-Distance Communications
8.3.4 Protected Cloud Storage Architecture and Machine Learning
8.4 Healthcare Systems That Can Be Worn
8.4.1 Pulse Sensors
8.4.2 Sensors for Respiratory Rate
8.4.3 Body Temperature Sensors
8.4.4 Body Temperature Sensors
8.4.5 Pulse Oximetry Sensors
8.5 Analysis and Data Processing
8.6 Conclusion and Future Scope
References
Chapter 9 Semantic Similarity Based on Association Measurement
9.1 Introduction
9.2 Biomedical Ontologies
9.2.1 Unified Medical Language System (UMLS)
9.2.1.1 Medical Subject Headings (MeSH)
9.2.1.2 SNOMED-CT
9.2.2 ICD
9.2.3 The Semantic Web
9.2.3.1 Ontologies
9.2.3.2 Structure of Ontologies
9.3 Proposed Semantic Similarity Measurement
9.3.1 Research Questions
9.3.2 Research Hypothesis
9.4 Ontology Evaluation
9.5 Semantic Similarity Measures
9.5.1 Semantic Similarity Measure Classification
9.5.2 Semantic Similarity Measures for Cross-Ontology
9.5.3 Ontology-Based Semantic Similarity
9.6 Results and Discussion
9.7 Conclusion
References
Chapter 10 IoT: Emerging Approach for Pharmaceutical and Healthcare Systems
10.1 Introduction
10.2 Electronic Health Records
10.3 Internet Approaches in the Pharmaceutical Sector
10.4 Medical Imaging
10.5 Artificial Intelligence (AI)
10.6 Creating an IoT Platform for Disorder Evaluation, Diagnosis, Prevention, and Therapy
10.7 Healthcare IoT Related to Wearable Electronics
10.8 Wireless Communication or Mobile Computing in Healthcare System
10.9 Impact of Mobile Computing
10.9.1 Collaborative Effects on the Development of Mobile Computing Systems
10.9.2 Mobile Computing Power and Big Data Technologies
10.9.3 Mobile Computing-Based Security Monitoring
10.9.4 Computing and HealthCare
10.10 Applications of Mobile Computing in Healthcare
10.10.1 Physiological Function Monitoring-Based Applications
10.10.2 Patient Communication and Support-Based Application
10.11 Healthy Conversation Modes for Healthcare Professionals and Service Seekers
10.12 Advantages and Disadvantages of Mobile Computing in Healthcare
10.13 Various Areas of Mobile Computing in the Healthcare Industry
10.14 Conclusion
References
Chapter 11 Blockchain Technology Effects on Healthcare Systems Using the IoT
11.1 Introduction
11.2 Problem Statement
11.3 Research Methods
11.4 Results and Discussion
11.5 Conclusion
References
Chapter 12 Deep Learning Approach for Classification of Alzheimer’s Disease
12.1 Introduction
12.2 Deep Learning
12.3 DL Building Block
12.4 Convolutional Neural Network (CNN)
12.4.1 Basic Building Blocks of CNNs
12.4.1.1 Convolutional Layer
12.4.1.2 Pooling Layer
12.4.1.3 Activation Layer
12.4.1.5 Dropout Layer
12.4.1.6 Fully Connected Layer
12.4.1.4 Batch Normalization Layer
12.4.2 Training CNN
12.4.3 Basic CNN Architecture
12.4.3.1 LeNet-5
12.4.3.2 AlexNet
12.4.3.4 VGG
12.4.3.3 ZFNet
12.4.3.5 GoogLeNet
12.4.3.6 ResNet
12.5 Proposed Framework
12.5.1 Data Collection Stage
12.5.2 Data Preparation Stage
12.5.2.1 Convert to RGB
12.5.2.2 Resize
12.5.2.3 Augmentation
12.5.2.4 Splitting
12.5.2.5 Shuffling
12.5.3 Model Selection Stage
12.5.3.1 Algorithm Selection
12.5.3.2 Hyperparameter Tuning
12.5.5 Validation Stage
12.5.4 Training Stage
12.6 Evaluation Metrics
12.6.1 Confusion Matrix
12.6.2 Accuracy
12.6.3 Recall
12.6.5 F1-Score
12.6.4 Precision
12.7 Experimental Results
12.8 Conclusion
References
Chapter 13 Plant Disease Identification Using Convolution Neural Networks
13.1 Introduction
13.2 DL-Based Plant Disease Detection Methods
13.3 Result and Discussion
13.4 Conclusion
References
Chapter 14 Study of Health Quality Coinciding with the Handwriting Process
14.1 Introduction
14.2 Background
14.3 History
14.4 Literature Review
14.5 Application
14.5.1 Recruiting
14.5.2 Criminal Investigation
14.5.3 Handwriting Identification
14.5.4 Diagnosis of Diseases
14.5.5 Depression
14.6 Major Contribution
14.6.1 Margin
14.6.2 Baseline
14.6.3 Slant
14.6.4 Pressure
14.6.5 Size
14.6.6 Spacing
14.6.7 Zones
14.6.8 Strokes
14.6.9 Letters
14.6.10 Signature
14.7 Conclusion and Future Research Direction
References
Chapter 15 E-Nose Sensor Applications and Challenges in the Health Sector
15.1 Introduction
15.2 E-Nose and Gas Sensors
15.2.1 Sensor Technologies
15.2.2 Sensor Response
15.3 Applications
15.3.1 Machine Olfaction
15.3.2 Health and Environment Monitoring
15.3.3 Smart Sensing
15.4 Issues and Challenges
15.4.1 Learning in the Presence of Noise and Drift
15.4.2 Gas Quantification and Detection Using an e-Nose
15.4.3 Feature Extraction for an e-Nose in a Dynamic Environment and Information Maximization
15.5 Conclusions
References
Index