IoT in Healthcare Systems

Description:

Implementing new information technologies into the healthcare sector can provide alternatives to managing patients’ health records, systems, and improving the quality of care received. This book provides an overview of IoT technologies related to the healthcare field and covers the main advantages and disadvantages along with industry case studies.

This edited volume will include several emerging required standardization and interoperability initiatives, various AI and Machine Learning algorithms, and discuss how health technology can face the challenge of improving the quality of life regardless of social and financial consideration, gender, age, and residence. The book presents real-time applications and case studies in the field of engineering, computer science, IoT, smart cities with modern tools and technologies used in healthcare. It will focus on many examples of successful IoT projects.

The target audience for this edited volume is researchers, practitioners, students, as well as key stakeholders involved in and working on healthcare engineering solutions.

See more medical ebooks at here:

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Blockchain Technology in e-Healthcare Management

Trends of Artificial Intelligence and Big Data for E-Health

About the Editors

Piyush Kumar Shukla is Associate Professor in the Department of Computer Science and Engineering at the University Institute of Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya (Technological University of Madhya Pradesh), India. He has 15 years of experience in teaching and research. He completed a Post Doctorate Fellowship under the Information Security Education and Awareness Project Phase II funded by the Ministry of Electronics and Information Technology, SVNIT Surat, India. His research interests include white-box cryptography, information security and privacy, cyber security, dynamic wireless networks, machine learning, the Internet of Things, image processing, and Blockchain. In addition to publishing more than 15 Indian patents and around 10 papers in international journals, he was awarded Best Researcher of the Year 2019 for Outstanding Research Contribution. Shukla is a senior life member of the IEEE.

Aditya Patel is Assistant Professor in the Department of Computer Science and Engineering, Lakshmi Narain College of Technology Bhopal. He has worked on more than 50 websites and software and has four years of teaching experience in various reputed technical colleges and universities along with two years of industry experience. His current research areas are machine learning, deep learning, and the Internet of Things.

Prashant Kumar Shukla is Assistant Professor and Research Coordinator in Jagran Lakecity University, India. His research interests include machine learning, deep learning, computer vision, the Internet of Things, software engineering, computer networking, mobile computing, information security, Python, and Java programming. He has 13 patents and has published and presented more than 17 research papers in various national and international journals and conferences. He received the Innovative Teacher award from GISR Foundation and the American College of Dubai. He also was awarded Best Researcher by ESN Publications. He is also associated with two start-ups.

Prashant Parashar is an experienced information security professional with around 16 years of experience in the information security domain. He is Security Analyst at Betfair, UK. His experience includes Align IT security governance, information security management, auditing and compliance, information security metrics, development and implementation of information security policy, and procedures and ISMS implementation in line with ISO 27001 requirements.

Basant Tiwari is Assistant Professor in the Computer Science and Engineering Department at Hawassa University, Ethiopia. His area of research is pervasive computing in healthcare and the Internet of Things and information and network security. He is a senior member of IEEE, a senior member of ACM, and a life member of CSI and IACSIT. He also chairs at iMPLab situated at Bhopal, India. Tiwari has organized various national and international conferences, delivered talks, and chaired technical sessions.

Table of contents :
Cover
Half Title
Series Information
Title Page
Copyright Page
Table of Contents
About the Editors
1 Artificial Intelligence-Based Smart Medical Services
1.1 Introduction
1.2 Classification of Smart Healthcare
1.3 Artificial Intelligence
1.3.1 Process of AI
1.3.1.1 Learning Processes
1.3.1.2 Reasoning Processes
1.3.1.3 Self-Correction Processes
1.3.2 Types of AI
1.4 The Healthcare Industry
1.4.1 The Elements of Healthcare Industries
1.4.1.1 Hospitals and Clinics
1.4.1.2 The Pharmaceutical Industries
1.4.1.3 The Medical Insurance Agencies
1.4.1.4 Pathology and Lab Tests
1.4.2 The Need for AI in the Healthcare Industry
1.4.2.1 Optimizing Cost Plans and Structures
1.4.2.2 Supply Chain Efficiencies for Pharmaceutical Companies
1.4.2.3 Healthcare Insurance Market
1.4.2.4 The Fitness Industry
1.4.2.5 The Government
1.5 Applications of AI in Medicine Today
1.5.1 Diagnose Diseases
1.5.2 Faster Drug Development
1.5.2.1 Identify Targets for Intervention
1.5.2.2 Discover Drug Candidates
1.5.2.3 Speed Up Clinical Trials
1.5.2.4 Find Biomarkers for Diagnosing the Disease
1.5.3 Personalize Treatment
1.5.4 Improve Gene Editing
1.6 Clinical Applications
1.6.1 Cardiovascular
1.6.2 AI Application in Stroke
1.6.2.1 Early Diagnosis
1.6.2.2 Treatment
1.6.3 Dermatology
1.6.4 Gastroenterology
1.6.5 Infectious Diseases
1.6.6 Oncology
1.6.7 Pathology
1.6.8 Primary Care
1.6.9 Psychiatry
1.6.10 Radiology
1.7 Case Studies
1.8 Current Applications of AI in Medical Diagnostics
1.9 Conclusion
References
2 Securing IoT Devices for Healthcare Systems Using Optimization-Based Approaches
2.1 Introduction
2.2 IoT-Based Wireless Sensor Networks
2.2.1 Structure of IoT-WSNs
2.2.2 Characteristics of IoT-WSNs
2.2.3 Challenges of IoT-WSNs
2.2.4 Security Concerns in IoT-WSNs
2.3 Types of Attacks On IoT-WSNs
2.3.1 Denial of Service (DoS) Attacks
2.3.2 Cryptographic Attacks
2.3.3 Routing Attacks
2.4 Optimization Techniques and Functions
2.5 Explanation of Approaches and Optimization Methods for Node Capture Attacks
2.6 Conclusions and Future Directions
References
3 Internet of Everything-Based Advanced Big Data Journey for the Medical Industry
3.1 Introduction
3.2 Dimensions of Big Data
3.3 Applications of Medical Big Data
3.4 Patient Prediction
3.5 Electronic Health Records
3.6 Real-Time Alerting
3.7 Enhancing Patient Engagement
3.8 Informed Strategic Planning By Utilizing Health Data
3.9 Big Data Could Cure Cancer
3.10 Predictive Analytics in Healthcare Sectors
3.11 Telemedicine
3.12 Big Data and Medical Imaging
3.13 Challenges of Big Data in the Medical Industry
3.14 Missing Values
3.15 Challenges in Medical Big Data
3.16 Case Study of a Diabetic Patient
3.17 Summary
References
4 IoT in Smart Rehabilitation
4.1 Introduction
4.2 Sensors Behind Smart Rehabilitation Systems
4.3 Bio MicroElectronic Mechanical Systems (MEMS)
4.4 Internet of Things in Rehabilitation
4.5 EKSO Bionics in Rehabilitation
4.6 Mobile Health
4.7 TeleHealth
4.8 E-Health
4.9 Brain-Computer Interface
4.10 Future Challenges
4.11 Discussion
4.12 Conclusion
References
5 Internet of Things in Smart and Intelligent Healthcare Systems
5.1 Introduction
5.2 Literature Survey
5.3 Layout of a Healthcare Supervisory System
5.3.1 Sensor Data Reading
5.3.2 Data Communications Infrastructure
5.3.3 Communication With Cloud Services
5.3.4 Role of Software
5.3.5 Supervisory Health System
5.4 Conclusion
References
6 Energy Harvesting for IoT Networks in Smart and Intelligent Networks
6.1 Introduction
6.2 Literature Survey
6.3 Proposed Model
6.3.1 Energy Consumption Model
6.3.2 Measures of Energy Harvesting
6.4 Results and Discussion
6.5 Conclusion
References
7 IoT Healthcare Applications
7.1 Introduction
7.2 Objectives of Healthcare Systems
7.3 Non-Conventional Health Monitors
7.4 Remote Health Monitoring
7.4.1 For Patients
7.4.1.1 Wearables
7.4.1.2 Non-Wearables
7.4.2 For Physicians
7.4.2.1 Healthcare Charting
7.4.2.2 Wireless Patient Monitoring
7.4.2.3 Virtual Consultation (Telemedicine)
7.4.2.4 Aging in Place
7.4.2.5 VSee Team
7.4.3 For Hospitals
7.4.3.1 Device Monitoring
7.4.3.2 Asset Management in Hospitals
7.4.3.3 Environment Management
7.4.3.4 Smart Wheelchairs and Stretchers
7.4.3.5 WBLC
7.4.3.6 Swasthya Slate
7.4.3.7 Information System
7.4.3.8 Smart Beds and Washable Clothing
7.5 Security Challenges
7.5.1 Computational Limitations
7.5.2 Memory Limitations
7.5.3 Energy Limitations
7.5.4 Mobility
7.5.5 Scalability
7.5.6 Communication Media
7.5.7 The Multiplicity of Devices
7.5.8 A Dynamic Topology
7.5.9 A Multi-Protocol Network
7.5.10 Data Confidentiality
7.5.11 Trust Mechanisms
7.6 Technologies Used for an IoT Healthcare System
7.6.1 Cloud Computing for Healthcare
7.6.2 Fog Computing Architecture
7.7 Conclusion
References
8 Applications, Opportunities, and Current Challenges in the Healthcare Industry
8.1 Introduction
8.2 Healthcare Data
8.3 Healthcare Research Issues
8.4 Healthcare Blockchain Systems
8.5 Healthcare Analytics
8.5.1 Types of Analytics
8.6 Healthcare Applications
8.6.1 Applications for Patients
8.6.2 Applications for Healthcare Professionals
8.6.3 Applications for Resource Management
8.7 Security and Privacy
8.8 Conclusion
References
9 Internet of Things-Healthcare System Architectures to Enhance the Healthcare Industry
9.1 Introduction
9.1.1 Contribution to AIoT Healthcare
9.2 Problem Statement
9.3 AIoT and Healthcare System Interconnection
9.3.1 AIoT for Health Insurance Companies
9.3.2 IoT for Physicians
9.3.3 IoT for Hospitals
9.3.4 IoT for Patients
9.4 Objectives and Key Issues in IoT Healthcare
9.5 AIoT Healthcare Devices
9.5.1 Blood Coagulation Testing
9.5.2 Connected Inhalers
9.5.3 Glucose Monitoring
9.5.4 Bluetooth-Enabled Blood Labs
9.5.5 Connected Cancer Treatment
9.5.6 Robotic Surgery
9.5.7 IoT-Connected Contact Lenses
9.5.8 A Smartwatch App That Monitors Depression
9.5.9 Connected Wearables
9.6 Summary of Associated Research
9.7 Applications of IoT to Build Emergency Medical Services
9.7.1 Data-Centric IoT
9.7.1.1 Collection of Data
9.7.1.2 Management and Preprocessing of Datasets
9.7.1.3 Interpretation of Datasets
9.7.2 Cloud-Centric IoT Platform
9.7.3 Network-Centric IoT
9.7.3.1 Sensing Paradigm
9.7.3.2 Addressing Scheme
9.7.3.3 Connectivity Model
9.7.3.4 Quality of Service (QoS) Mechanism
9.8 Security and Privacy Requirements of AIoT Healthcare
9.9 Conclusion and Future Work
References
10 An IoT-Based Moving Vehicle Healthcare Service
10.1 Introduction
10.2 Goal of the Research Work
10.3 Objective
10.4 Existing Work Done
10.5 Healthcare IoT Services
10.6 Amenities
10.6.1 Assisted Living in a More Natural Setting
10.6.2 Mobile Internet of Things
10.6.3 Devices That Can Be Worn On the Body
10.6.4 Providers of Community-Based Healthcare
10.6.5 The Study of the Mind
10.6.6 Unwanted Side Effects of a Medicine
10.6.7 Blockchain
10.6.8 Information for Parents Concerned About the Health of Their Children
10.7 Application of IoT
10.7.1 ECG Detection and Monitoring
10.7.2 Monitoring of Blood Glucose
10.7.3 Monitoring of Body Temperature
10.7.4 The Measurement of Blood Pressure
10.7.5 Blood Oxygen Content Measuring
10.7.6 Monitoring of Asthma
10.7.7 Mood Observation and Tracking
10.8 Conclusion
References
11 DDoS Attack Detection Using Predictive Machine Learning (ML) Algorithms in Wireless Body Area Network Environments
11.1 Background
11.2 Wireless Body Area Network (WBAN)
11.2.1 WBAN Hardware Components
11.2.2 Communication Model for WBAN
11.3 Machine Learning in DDOS Attack Detection
11.3.1 Types of Machine Learning Algorithms
11.4 Related Work
11.5 Material and Methods
11.5.1 Dataset
11.5.2 Description of the Dataset
11.5.3 Dataset Pre-Processing
11.5.4 Dataset Cleaning
11.5.5 Features Selection
11.5.5.1 Information Gain
11.5.5.2 Gain Ratio
11.6 Working Environment
11.6.1 Test Bed for Detection of a DDoS Attack
11.6.1.1 Software
11.6.1.2 Hardware
11.6.2 Performance Metrics for Evaluating Classifiers
11.6.3 Holdout Method and Random Sub Sampling
11.6.4 Cross-Validation
11.7 Proposed Algorithm
11.8 Results and Discussion
11.8.1 Results of Feature Selection
11.8.2 Confusion Matrix Result On Individual Algorithms
11.8.3 Results of Evaluation Metrics
11.8.4 Results Discussion On DDoS Attack Detection
11.9 Conclusion
References
Index