The Internet of Medical Things (IoMT)

Name: The Internet of Medical Things (IoMT) | Healthcare Transformation
Brand: Wiley
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Healthcare Transformation

R. J. Hemalatha D. Akila D. Balaganesh Anand Paul(Herausgeber*in)

Wiley (Verlag)

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Erschienen am 11. Februar 2022

336 Seiten

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Beschreibung

INTERNET OF MEDICAL THINGS (IOMT)

Providing an essential addition to the reference material available in the field of IoMT, this timely publication covers a range of applied research on healthcare, biomedical data mining, and the security and privacy of health records.

With their ability to collect, analyze and transmit health data, IoMT tools are rapidly changing healthcare delivery. For patients and clinicians, these applications are playing a central part in tracking and preventing chronic illnesses - and they are poised to evolve the future of care.

In this book, the authors explore the potential applications of a wave of sensor-based tools-including wearables and stand-alone devices for remote patient monitoring-and the marriage of internet-connected medical devices with patient information that ultimately sets the IoMT ecosystem apart.

This book demonstrates the connectivity between medical devices and sensors is streamlining clinical workflow management and leading to an overall improvement in patient care, both inside care facilities and in remote locations.

Audience

This book will be suitable for a wide range of researchers who are interested in acquiring in-depth knowledge on the latest IoMT-based solutions for healthcare-related problems. The book is specifically for those in artificial intelligence, cyber-physical systems, robotics, information technology, safety-critical systems, digital forensics, and application domain communities such as critical infrastructures, smart healthcare, manufacturing, and smart cities.

R.J. Hemalatha, PhD in Electronics Engineering from Sathyabama University, India. She is currently the Head of the Department of Biomedical Engineering, in Vels Institute of Science, Technology & Advanced Studies (VISTAS), Chennai, Tamil Nadu, India. She has published more than 50 research papers in various international journals.

D. Akila, PhD received his degree in Computer Science from Bharathiar University, Tamilnadu, India. She is an associate professor in the Department of Information Technology, School of Computing Sciences, Vels Institute of Science, Technology & Advanced Studies (VISTAS), Chennai, Tamil Nadu, India. She has published more than 25 research papers in various international journals.

D. Balaganesh, PhD is a Dean of Faculty Computer Science and Multimedia, Lincoln University College, Malaysia.

Anand Paul, PhD is an associate professor in the School of Computer Science and Engineering, Kyungpook National University, South Korea. He received his PhD degree in Electrical Engineering from National Cheng Kung University, Taiwan, R.O.C. in 2010.

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Inhalt

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
Also of Interest
EULA

Preface

It is a pleasure for us to put forth this book, The Internet of Medical Things (IoMT): Healthcare Transformation. Digital technologies have come into effect in various sectors of our daily lives and it has been successful in influencing and conceptualizing our day-to-day activities. The Internet of Medical Things is one such discipline which seeks a lot of interest as it combines various medical devices and allows these devices to have a conversation among themselves over a network to form a connection of advanced smart devices. This book helps to know about IoMT in the health care sector that involves the latest technological implementation in diagnostic level as well as therapeutic level. The security and privacy of maintaining the health records is a major concern and several solutions for the same has been discussed in this book. It provides significant advantages for the wellbeing of people by increasing the quality of life and reducing medical expenses. IoMT plays a major role in maintaining smart healthcare system as the security and privacy of the health records further leads to help the health care sector to be more secure and reliable. Artificial Intelligence is the other enabling technology that helps IoMT in building smart defensive mechanisms for a variety of applications like providing assistance for doctors in almost every area of their proficiencies such as clinical decision-making. Through Machine Learning and Deep Learning techniques, the system can learn normal and abnormal decisions using the data generated by the health worker/professionals and the patient feedback. This book demonstrates the connectivity between medical devices and sensors is streamlining clinical workflow management and leading to an overall improvement in patient care, both inside care facility walls and in remote locations. This book would be a good collection of state-of-the-art approaches for applications of IoMT in various health care sectors. It will be very beneficial for the new researchers and practitioners working in the field to quickly know the best methods for IoMT.

Chapter 1 concentrates on the study of the three-dimensional (3-D) models of lung cancer cell line proteins (epidermal growth factor (EGFR), K-Ras oncogene protein and tumor suppressor (TP53)). The generation and their binding affinities with curcumins, ellagic acid and quercetin through local docking were assessed.
Chapter 2 focuses on cloud computing and electronic health record system service EHR used to protect the confidentiality of patient sensitive information and must be encrypted before outsourcing information. This chapter focuses on the effective use of cloud data such as search keywords and data sharing and the challenging problem associated with the concept of soft computing.
Chapter 3 elucidates the study of cloud computing concepts, security concerns in clouds and data centers, live migration and its importance for cloud computing, and the role of virtual machine (VM) migration in cloud computing. It provides a holistic approach towards the pre-copy migration technique thereby explore the way for reducing the downtime and migration time. This chapter compares different pre-copy algorithms and evaluates its parameters for providing a better solution.
Chapter 4 concentrates on Deep Learning that has gained more interest in various fields like image classification, self-driven cars, natural language processing and healthcare applications. The chapter focuses on solving the complex problems in a more effective and efficient manner. It elaborates for the reader how deep learning techniques are useful for predicting and classification of the brain tumor cells. Datasets are trained using pre-trained neural networks such as Alexnet, Googlenet and Resnet 101 and performance of these networks are analysed in detail. Resnet 101 networks have achieved highest accuracy.
Chapter 5 illustrates an intelligent healthcare monitoring system for coma patients that examines the coma patient's vital signs on a continuous basis, detects the movement happening in the patient, and updates the information to the doctor and central station through IoMT. Consistent tracking and observation of these health issues improves medical assurance and allows for tracking coma events.
Chapter 6 details the Deep Learning process that resembles the human functions in processing and defining patterns used for decision-making. Deep learning algorithms are mainly designed and developed using neural networks performing unsupervised data that are unstructured. Biomedical data possess time and frequency domain features for analysis and classification. Thus, deep learning algorithms are used for interpretation and classification of biomedical big data.
Chapter 7 discusses how the electronic health records automates and streamlines the clinician's workflow and makes the process easy. It has the ability to generate the complete history of the patient and also help in assisting for the further treatment which helps in the recovery of the patient in a more effective way. The electronic health records are designed according to the convenience depending on the sector it is being implemented. The main aim of electronic health records was to make it available to the concerned person wherever they are, to reduce the work load to maintain clinical book records and use the details for research purposes with the concerned persons acknowledgement.
Chapter 8 elaborates technical architecture of IoMT in relation to biomedical applications. These ideologies are widely used to educate people regarding the medical applications using IoMT. It also gives a detailed study about the future scope of IoMT in healthcare.
Chapter 9 provides knowledge on the different performance assessment techniques and types of protocols that suits best data transfer and increases safety. The chapter provides the best protocol which helps in saving energy and is useful for the customer. It will help the researchers to select the best IoT protocol for healthcare applications. Testing tools and frameworks provide knowledge to assess the protocols.
Chapter 10 addresses the issue of a Health Monitoring Centre (HMC) in rural areas. The HMC monitors and records continuously the physiological parameters of the patients in care using wearable biosensors. The elderly suffering from chronic diseases is monitored periodically or continuously under the care of the physician. To enhance the performance of the system a smart and intelligent mesh backbone is integrated for fast transmission of the critical medical data to a remote health IOT cloud server.
Chapter 11 concentrates on Diabetes Mellitus (DM) which is one of the most widely recognized perilous illnesses for all age groups in the world. The patients need to settle on the best-individualized choices about day-by-day management of their diabetes. Noninvasive glucose sensor used to find out the glucose value of patients from its fingertip and other sensors also connected to the patient to get relevant data. A completely useful IoT-based eHealth stage that wires humanoid robot help with diabetes and planned successfully. The created platform encourages a constant coupled network among patients and their caretakers over physical separation and, in this manner, improving patient's commitment with their caretakers while limiting the cost, time, and exertion of the conventional occasional clinic visits.
Chapter 12 explores the concepts of wearable health monitoring systems using IoMT technology. Additionally, this chapter also provides a brief review about challenges and applications of customized wearable healthcare system that are trending these days. The basic idea is to have a detailed study about the recent developments in IoMT technologies and the drawbacks, as well as future advancements related to it. The recent innovations, implications and key issues are discussed in the context of the framework.
Chapter 13 provides knowledge on biomedical big data analysis which plays a huge impact in personalized medicine. Some challenges in big data analysis like data acquisition, data accuracy, data security are discussed. Huge volume of data in healthcare can be managed by integrating biomedical data management. This chapter will provide brief information on different software that are used to manage data in healthcare domain. Impact of big data and IoMT in healthcare will enhance data analytics research.
Chapter 14 concentrates on blockchain which is a highly secure and decentralized networking platform of multiple computers called nodes. Predictive analysis, soft computing (SC) and optimization and data science is becoming increasingly important. In this chapter, the authors investigate privacy issues around large cloud medical data in the remote cloud. Their proposed framework ensures data privacy, integrity, and access control over the shared data with better efficiency. It reduces the turnaround time for data sharing, improves the decision-making process, and reduces the overall cost while providing better security of electronic medical records.
Chapter 15 discusses the evolution of electronic health record starting with the history and evolution of the health record system in the Egyptian era when the first health record was written, all the way to the modern computerized health record system. This chapter also includes various documentation procedures for the health records that were followed from the...

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