-
Browse Book Series
- Adhesion and Adhesives: Fundamental and Applied Aspects
- Advances in Additive Manufacturing Technologies
- Advances in Antenna, Microwave and Communication Engineering
- Advances in Biofeedstocks and Biofuels
- Advances in Cyber Security
- Advances in Data Engineering and Machine Learning
- Advances in E-Mobility
- Advances in Hydrogen Production and Storage
- Advances in Intelligent and Scientific Computing (AISC)
- Advances in Learning Analytics for Intelligent Cloud-IoT Systems
- Advances in Membrane Processes
- Advances in Nanotechnology and Applications
- Advances in Natural Gas Engineering
- Advances in Pipes and Pipelines
- Advances in Production Engineering
- Advances in Solar Cell Materials and Storage
- Applied Functional Materials
- Artificial Intelligence and Soft Computing for Industrial Transformation
- Astrobiology Perspectives on Life in the Universe
- Bioprocessing in Food Science
- Computational Intelligence and Biomedical Engineering
- Concise Introductions to AI and Data Science
- Cyber Systems and Quantum Engineering
- Decentralized Systems and Next-Generation Internet
- Diatoms: Biology and Applications
- Digital Convergence in Engineering Systems
- Emerging Trends in Medicinal and Pharmaceutical Chemistry
- Engineering Systems Design for Sustainable Development
- Fintech in a Sustainable Digital Society
- Industry 5.0 Transformation Applications
- Infectious and Rare Diseases
- Innovations in Materials and Manufacturing
- Machine Learning in Biomedical Science and Healthcare Informatics
- Materials Degradation and Failure
- Mathematics and Computer Science
- Next-Generation Computing and Communication Engineering
- Performability Engineering Series
- Rotating Machinery Fundamentals and Advances
- Studies in Computational Intelligence and Smart Technologies
- Sustainable Computing and Optimization
Browse Subject Areas
For Authors
Submit a ProposalInternet of Medical Things (IoMT)
 | Healthcare Transformation. Edited by R. J. Hemalatha, D. Akila, D. Balaganesh and Anand Paul Series: Advances in Learning Analytics for Intelligent Cloud-IoT Systems Copyright: 2022 | Status: Published ISBN: 9781119768838 | Hardcover | 301 pages | 76 illustrations Price: $225 USD  |
One Line Description
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.
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.
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.
Description
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.
Back to Top
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.
Back to Top
Author / Editor Details
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.
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.
Back to Top
Table of Contents
Preface
1. In Silico Molecular Modeling and Docking Analysis in Lung Cancer Cell Proteins
Manisha Sritharan and Asita Elengoe
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
Saurabh Sharma, Harish K. Shakya and Ashish Mishra
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
Nirmala Devi N. and Vengatesh Kumar S.
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
Krishnamoorthy Raghavan Narasu, Anima Nanda, Marshiana D., Bestley Joe and Vinoth Kumar
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
Bethanney Janney J., T. Sudhakar, Sindu Divakaran, Chandana H. and Caroline Chriselda L.
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
T.R. Thamizhvani, R. Chandrasekaran and T.R. Ineyathendral
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
G. Umashankar, Abinaya P., J. Premkumar, T. Sudhakar and S. Krishnakumar
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
A. Josephin Arockia Dhiyya
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 loMT 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
A. Keerthana and Karthiga
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
G. Merlin Sheeba and Y. Bevish Jinila
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)
Krishnakumar S., Umashankar G., Lumen Christy V., Vikas and Hemalatha R.J.
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
Jaya Rubi and A. Josephin Arockia Dhivya
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
Tamiziniyan G. and Keerthana A.
13.1 Introduction
13.2 Big Data and IoT in 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
Saurabh Sharma, Harish K. Shakya and Ashish Mishra
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
Srividhya G.
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
Back to Top
Preface
1. In Silico Molecular Modeling and Docking Analysis in Lung Cancer Cell Proteins
Manisha Sritharan and Asita Elengoe
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
Saurabh Sharma, Harish K. Shakya and Ashish Mishra
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
Nirmala Devi N. and Vengatesh Kumar S.
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
Krishnamoorthy Raghavan Narasu, Anima Nanda, Marshiana D., Bestley Joe and Vinoth Kumar
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
Bethanney Janney J., T. Sudhakar, Sindu Divakaran, Chandana H. and Caroline Chriselda L.
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
T.R. Thamizhvani, R. Chandrasekaran and T.R. Ineyathendral
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
G. Umashankar, Abinaya P., J. Premkumar, T. Sudhakar and S. Krishnakumar
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
A. Josephin Arockia Dhiyya
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 loMT 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
A. Keerthana and Karthiga
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
G. Merlin Sheeba and Y. Bevish Jinila
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)
Krishnakumar S., Umashankar G., Lumen Christy V., Vikas and Hemalatha R.J.
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
Jaya Rubi and A. Josephin Arockia Dhivya
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
Tamiziniyan G. and Keerthana A.
13.1 Introduction
13.2 Big Data and IoT in 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
Saurabh Sharma, Harish K. Shakya and Ashish Mishra
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
Srividhya G.
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
Back to Top