The Internet of Medical Things (Iomt): Healthcare Transformation

ISBN-13: 9781119768838 / Angielski / Twarda / 2022 / 336 str.

R. J. Hemalatha; D. Akila; D. Balaganesh

Spis treści

Preface xv1 In Silico Molecular Modeling and Docking Analysis in Lung Cancer Cell Proteins 1Manisha Sritharan and Asita Elengoe1.1 Introduction 21.2 Methodology 41.2.1 Sequence of Protein 41.2.2 Homology Modeling 41.2.3 Physiochemical Characterization 41.2.4 Determination of Secondary Models 41.2.5 Determination of Stability of Protein Structures 41.2.6 Identification of Active Site 41.2.7 Preparation of Ligand Model 51.2.8 Docking of Target Protein and Phytocompound 51.3 Results and Discussion 51.3.1 Determination of Physiochemical Characters 51.3.2 Prediction of Secondary Structures 71.3.3 Verification of Stability of Protein Structures 71.3.4 Identification of Active Sites 141.3.5 Target Protein-Ligand Docking 141.4 Conclusion 18References 182 Medical Data Classification in Cloud Computing Using Soft Computing With Voting Classifier: A Review 23Saurabh Sharma, Harish K. Shakya and Ashish Mishra2.1 Introduction 242.1.1 Security in Medical Big Data Analytics 242.1.1.1 Capture 242.1.1.2 Cleaning 252.1.1.3 Storage 252.1.1.4 Security 262.1.1.5 Stewardship 262.2 Access Control-Based Security 272.2.1 Authentication 272.2.1.1 User Password Authentication 282.2.1.2 Windows-Based User Authentication 282.2.1.3 Directory-Based Authentication 282.2.1.4 Certificate-Based Authentication 282.2.1.5 Smart Card-Based Authentication 292.2.1.6 Biometrics 292.2.1.7 Grid-Based Authentication 292.2.1.8 Knowledge-Based Authentication 292.2.1.9 Machine Authentication 292.2.1.10 One-Time Password (OTP) 302.2.1.11 Authority 302.2.1.12 Global Authorization 302.3 System Model 302.3.1 Role and Purpose of Design 312.3.1.1 Patients 312.3.1.2 Cloud Server 312.3.1.3 Doctor 312.4 Data Classification 322.4.1 Access Control 322.4.2 Content 332.4.3 Storage 332.4.4 Soft Computing Techniques for Data Classification 342.5 Related Work 362.6 Conclusion 42References 433 Research Challenges in Pre-Copy Virtual Machine Migration in Cloud Environment 45Nirmala Devi N. and Vengatesh Kumar S.3.1 Introduction 463.1.1 Cloud Computing 463.1.1.1 Cloud Service Provider 473.1.1.2 Data Storage and Security 473.1.2 Virtualization 483.1.2.1 Virtualization Terminology 493.1.3 Approach to Virtualization 503.1.4 Processor Issues 513.1.5 Memory Management 513.1.6 Benefits of Virtualization 513.1.7 Virtual Machine Migration 513.1.7.1 Pre-Copy 523.1.7.2 Post-Copy 523.1.7.3 Stop and Copy 533.2 Existing Technology and Its Review 543.3 Research Design 563.3.1 Basic Overview of VM Pre-Copy Live Migration 573.3.2 Improved Pre-Copy Approach 583.3.3 Time Series-Based Pre-Copy Approach 603.3.4 Memory-Bound Pre-Copy Live Migration 623.3.5 Three-Phase Optimization Method (TPO) 623.3.6 Multiphase Pre-Copy Strategy 643.4 Results 653.4.1 Finding 653.5 Discussion 693.5.1 Limitation 693.5.2 Future Scope 703.6 Conclusion 70References 714 Estimation and Analysis of Prediction Rate of Pre-Trained Deep Learning Network in Classification of Brain Tumor MRI Images 73Krishnamoorthy Raghavan Narasu, Anima Nanda, Marshiana D., Bestley Joe and Vinoth Kumar4.1 Introduction 744.2 Classes of Brain Tumors 754.3 Literature Survey 764.4 Methodology 784.5 Conclusion 93References 955 An Intelligent Healthcare Monitoring System for Coma Patients 99Bethanney Janney J., T. Sudhakar, Sindu Divakaran, Chandana H. and Caroline Chriselda L.5.1 Introduction 1005.2 Related Works 1025.3 Materials and Methods 1045.3.1 Existing System 1045.3.2 Proposed System 1055.3.3 Working 1055.3.4 Module Description 1065.3.4.1 Pulse Sensor 1065.3.4.2 Temperature Sensor 1075.3.4.3 Spirometer 1075.3.4.4 OpenCV (Open Source Computer Vision) 1085.3.4.5 Raspberry Pi 1085.3.4.6 USB Camera 1095.3.4.7 AVR Module 1095.3.4.8 Power Supply 1095.3.4.9 USB to TTL Converter 1105.3.4.10 EEG of Comatose Patients 1105.4 Results and Discussion 1115.5 Conclusion 116References 1176 Deep Learning Interpretation of Biomedical Data 121T.R. Thamizhvani, R. Chandrasekaran and T.R. Ineyathendral6.1 Introduction 1226.2 Deep Learning Models 1256.2.1 Recurrent Neural Networks 1256.2.2 LSTM/GRU Networks 1276.2.3 Convolutional Neural Networks 1286.2.4 Deep Belief Networks 1306.2.5 Deep Stacking Networks 1316.3 Interpretation of Deep Learning With Biomedical Data 1326.4 Conclusion 139References 1407 Evolution of Electronic Health Records 143G. Umashankar, Abinaya P., J. Premkumar, T. Sudhakar and S. Krishnakumar7.1 Introduction 1437.2 Traditional Paper Method 1447.3 IoMT 1447.4 Telemedicine and IoMT 1457.4.1 Advantages of Telemedicine 1457.4.2 Drawbacks 1467.4.3 IoMT Advantages with Telemedicine 1467.4.4 Limitations of IoMT With Telemedicine 1477.5 Cyber Security 1477.6 Materials and Methods 1477.6.1 General Method 1477.6.2 Data Security 1487.7 Literature Review 1487.8 Applications of Electronic Health Records 1507.8.1 Clinical Research 1507.8.1.1 Introduction 1507.8.1.2 Data Significance and Evaluation 1517.8.1.3 Conclusion 1517.8.2 Diagnosis and Monitoring 1517.8.2.1 Introduction 1517.8.2.2 Contributions 1527.8.2.3 Applications 1527.8.3 Track Medical Progression 1537.8.3.1 Introduction 1537.8.3.2 Method Used 1537.8.3.3 Conclusion 1547.8.4 Wearable Devices 1547.8.4.1 Introduction 1547.8.4.2 Proposed Method 1557.8.4.3 Conclusion 1557.9 Results and Discussion 1557.10 Challenges Ahead 1577.11 Conclusion 158References 1588 Architecture of IoMT in Healthcare 161A. Josephin Arockia Dhiyya8.1 Introduction 1618.1.1 On-Body Segment 1628.1.2 In-Home Segment 1628.1.3 Network Segment Layer 1638.1.4 In-Clinic Segment 1638.1.5 In-Hospital Segment 1638.1.6 Future of IoMT? 1648.2 Preferences of the Internet of Things 1658.2.1 Cost Decrease 1658.2.2 Proficiency and Efficiency 1658.2.3 Business Openings 1658.2.4 Client Experience 1668.2.5 Portability and Nimbleness 1668.3 loMT Progress in COVID-19 Situations: Presentation 1678.3.1 The IoMT Environment 1688.3.2 IoMT Pandemic Alleviation Design 1698.3.3 Man-Made Consciousness and Large Information Innovation in IoMT 1708.4 Major Applications of IoMT 171References 1729 Performance Assessment of IoMT Services and Protocols 173A. Keerthana and Karthiga9.1 Introduction 1749.2 IoMT Architecture and Platform 1759.2.1 Architecture 1769.2.2 Devices Integration Layer 1779.3 Types of Protocols 1779.3.1 Internet Protocol for Medical IoT Smart Devices 1779.3.1.1 HTTP 1789.3.1.2 Message Queue Telemetry Transport (MQTT) 1799.3.1.3 Constrained Application Protocol (CoAP) 1809.3.1.4 AMQP: Advanced Message Queuing Protocol (AMQP) 1819.3.1.5 Extensible Message and Presence Protocol (XMPP) 1819.3.1.6 DDS 1839.4 Testing Process in IoMT 1839.5 Issues and Challenges 1859.6 Conclusion 185References 18510 Performance Evaluation of Wearable IoT-Enabled Mesh Network for Rural Health Monitoring 187G. Merlin Sheeba and Y. Bevish Jinila10.1 Introduction 18810.2 Proposed System Framework 19010.2.1 System Description 19010.2.2 Health Monitoring Center 19210.2.2.1 Body Sensor 19210.2.2.2 Wireless Sensor Coordinator/Transceiver 19210.2.2.3 Ontology Information Center 19510.2.2.4 Mesh Backbone-Placement and Routing 19610.3 Experimental Evaluation 20010.4 Performance Evaluation 20110.4.1 Energy Consumption 20110.4.2 Survival Rate 20110.4.3 End-to-End Delay 20210.5 Conclusion 204References 20411 Management of Diabetes Mellitus (DM) for Children and Adults Based on Internet of Things (IoT) 207Krishnakumar S., Umashankar G., Lumen Christy V., Vikas and Hemalatha R.J.11.1 Introduction 20811.1.1 Prevalence 20911.1.2 Management of Diabetes 20911.1.3 Blood Glucose Monitoring 21011.1.4 Continuous Glucose Monitors 21111.1.5 Minimally Invasive Glucose Monitors 21111.1.6 Non-Invasive Glucose Monitors 21111.1.7 Existing System 21111.2 Materials and Methods 21211.2.1 Artificial Neural Network 21211.2.2 Data Acquisition 21311.2.3 Histogram Calculation 21311.2.4 IoT Cloud Computing 21411.2.5 Proposed System 21511.2.6 Advantages 21511.2.7 Disadvantages 21511.2.8 Applications 21611.2.9 Arduino Pro Mini 21611.2.10 LM78XX 21711.2.11 MAX30100 21811.2.12 LM35 Temperature Sensors 21811.3 Results and Discussion 21911.4 Summary 22211.5 Conclusion 222References 22312 Wearable Health Monitoring Systems Using IoMT 225Jaya Rubi and A. Josephin Arockia Dhivya12.1 Introduction 22512.2 IoMT in Developing Wearable Health Surveillance System 22612.2.1 A Wearable Health Monitoring System with Multi-Parameters 22712.2.2 Wearable Input Device for Smart Glasses Based on a Wristband-Type Motion-Aware Touch Panel 22812.2.3 Smart Belt: A Wearable Device for Managing Abdominal Obesity 22812.2.4 Smart Bracelets: Automating the Personal Safety Using Wearable Smart Jewelry 22812.3 Vital Parameters That Can Be Monitored Using Wearable Devices 22912.3.1 Electrocardiogram 23012.3.2 Heart Rate 23112.3.3 Blood Pressure 23212.3.4 Respiration Rate 23212.3.5 Blood Oxygen Saturation 23412.3.6 Blood Glucose 23512.3.7 Skin Perspiration 23612.3.8 Capnography 23812.3.9 Body Temperature 23912.4 Challenges Faced in Customizing Wearable Devices 24012.4.1 Data Privacy 24012.4.2 Data Exchange 24012.4.3 Availability of Resources 24112.4.4 Storage Capacity 24112.4.5 Modeling the Relationship Between Acquired Measurement and Diseases 24212.4.6 Real-Time Processing 24212.4.7 Intelligence in Medical Care 24212.5 Conclusion 243References 24413 Future of Healthcare: Biomedical Big Data Analysis and IoMT 247Tamiziniyan G. and Keerthana A.13.1 Introduction 24813.2 Big Data and IoT in Healthcare Industry 25013.3 Biomedical Big Data Types 25113.3.1 Electronic Health Records 25213.3.2 Administrative and Claims Data 25213.3.3 International Patient Disease Registries 25213.3.4 National Health Surveys 25313.3.5 Clinical Research and Trials Data 25413.4 Biomedical Data Acquisition Using IoT 25413.4.1 Wearable Sensor Suit 25413.4.2 Smartphones 25513.4.3 Smart Watches 25513.5 Biomedical Data Management Using IoT 25613.5.1 Apache Spark Framework 25713.5.2 MapReduce 25813.5.3 Apache Hadoop 25813.5.4 Clustering Algorithms 25913.5.5 K-Means Clustering 25913.5.6 Fuzzy C-Means Clustering 26013.5.7 DBSCAN 26113.6 Impact of Big Data and IoMT in Healthcare 26213.7 Discussions and Conclusions 263References 26414 Medical Data Security Using Blockchain With Soft Computing Techniques: A Review 269Saurabh Sharma, Harish K. Shakya and Ashish Mishra14.1 Introduction 27014.2 Blockchain 27214.2.1 Blockchain Architecture 27214.2.2 Types of Blockchain Architecture 27314.2.3 Blockchain Applications 27414.2.4 General Applications of the Blockchain 27614.3 Blockchain as a Decentralized Security Framework 27714.3.1 Characteristics of Blockchain 27814.3.2 Limitations of Blockchain Technology 28014.4 Existing Healthcare Data Predictive Analytics Using Soft Computing Techniques in Data Science 28114.4.1 Data Science in Healthcare 28114.5 Literature Review: Medical Data Security in Cloud Storage 28114.6 Conclusion 286References 28715 Electronic Health Records: A Transitional View 289Srividhya G.15.1 Introduction 28915.2 Ancient Medical Record, 1600 BC 29015.3 Greek Medical Record 29115.4 Islamic Medical Record 29115.5 European Civilization 29215.6 Swedish Health Record System 29215.7 French and German Contributions 29315.8 American Descriptions 29315.9 Beginning of Electronic Health Recording 29715.10 Conclusion 298References 298Index 301

O autorze

AudienceThis 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.