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E-raamat: Internet of Medical Things (IoMT): Healthcare Transformation

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Internet of Medical Things (IoMT): Healthcare TransformationSuurem pilt
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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 toolsincluding wearables and stand-alone devices for remote patient monitoringand 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.
Preface xv
1 In Silico Molecular Modeling and Docking Analysis in Lung Cancer Cell Proteins
1(1)
Manisha Sritharan
Asita Elengoe
1.1 Introduction
2(2)
1.2 Methodology
4(1)
1.2.1 Sequence of Protein
4(1)
1.2.2 Homology Modeling
4(1)
1.2.3 Physiochemical Characterization
4(1)
1.2.4 Determination of Secondary Models
4(1)
1.2.5 Determination of Stability of Protein Structures
4(1)
1.2.6 Identification of Active Site
4(1)
1.2.7 Preparation of Ligand Model
5(1)
1.2.8 Docking of Target Protein and Phytocompound
5(1)
1.3 Results and Discussion
5(13)
1.3.1 Determination of Physiochemical Characters
5(2)
1.3.2 Prediction of Secondary Structures
7(1)
1.3.3 Verification of Stability of Protein Structures
7(7)
1.3.4 Identification of Active Sites
14(1)
1.3.5 Target Protein-Ligand Docking
14(4)
1.4 Conclusion
18(5)
References
18(5)
2 Medical Data Classification in Cloud Computing Using Soft Computing With Voting Classifier: A Review
23(22)
Saurabh Sharma
Harish K. Shakya
Ashish Mishra
2.1 Introduction
24(3)
2.1.1 Security in Medical Big Data Analytics
24(1)
2.1.1.1 Capture
24(1)
2.1.1.2 Cleaning
25(1)
2.1.1.3 Storage
25(1)
2.1.1.4 Security
26(1)
2.1.1.5 Stewardship
26(1)
2.2 Access Control-Based Security
27(3)
2.2.1 Authentication
27(1)
2.2.1.1 User Password Authentication
28(1)
2.2.1.2 Windows-Based User Authentication
28(1)
2.2.1.3 Directory-Based Authentication
28(1)
2.2.1.4 Certificate-Based Authentication
28(1)
2.2.1.5 Smart Card-Based Authentication
29(1)
2.2.1.6 Biometrics
29(1)
2.2.1.7 Grid-Based Authentication
29(1)
2.2.1.8 Knowledge-Based Authentication
29(1)
2.2.1.9 Machine Authentication
29(1)
2.2.1.10 One-Time Password (OTP)
30(1)
2.2.1.11 Authority
30(1)
2.2.1.12 Global Authorization
30(1)
2.3 System Model
30(2)
2.3.1 Role and Purpose of Design
31(1)
2.3.1.1 Patients
31(1)
2.3.1.2 Cloud Server
31(1)
2.3.1.3 Doctor
31(1)
2.4 Data Classification
32(4)
2.4.1 Access Control
32(1)
2.4.2 Content
33(1)
2.4.3 Storage
33(1)
2.4.4 Soft Computing Techniques for Data Classification
34(2)
2.5 Related Work
36(6)
2.6 Conclusion
42(3)
References
43(2)
3 Research Challenges in Pre-Copy Virtual Machine Migration in Cloud Environment
45(28)
N. Nirmala Devi
S. Vengatesh Kumar
3.1 Introduction
46(8)
3.1.1 Cloud Computing
46(1)
3.1.1.1 Cloud Service Provider
47(1)
3.1.1.2 Data Storage and Security
47(1)
3.1.2 Virtualization
48(1)
3.1.2.1 Virtualization Terminology
49(1)
3.1.3 Approach to Virtualization
50(1)
3.1.4 Processor Issues
51(1)
3.1.5 Memory Management
51(1)
3.1.6 Benefits of Virtualization
51(1)
3.1.7 Virtual Machine Migration
51(1)
3.1.7.1 Pre-Copy
52(1)
3.1.7.2 Post-Copy
52(1)
3.1.7.3 Stop and Copy
53(1)
3.2 Existing Technology and Its Review
54(2)
3.3 Research Design
56(9)
3.3.1 Basic Overview of VM Pre-Copy Live Migration
57(1)
3.3.2 Improved Pre-Copy Approach
58(2)
3.3.3 Time Series-Based Pre-Copy Approach
60(2)
3.3.4 Memory-Bound Pre-Copy Live Migration
62(1)
3.3.5 Three-Phase Optimization Method (TPO)
62(2)
3.3.6 Multiphase Pre-Copy Strategy
64(1)
3.4 Results
65(4)
3.4.1 Finding
65(4)
3.5 Discussion
69(1)
3.5.1 Limitation
69(1)
3.5.2 Future Scope
70(1)
3.6 Conclusion
70(3)
References
71(2)
4 Estimation and Analysis of Prediction Rate of Pre-Trained Deep Learning Network in Classification of Brain Tumor MRI Images
73(26)
Krishnamoorthy Raghavan Narasu
Anima Nanda
D. Marshiana
Bestley Joe
Vinoth Kumar
4.1 Introduction
74(1)
4.2 Classes of Brain Tumors
75(1)
4.3 Literature Survey
76(2)
4.4 Methodology
78(15)
4.5 Conclusion
93(6)
References
95(4)
5 An Intelligent Healthcare Monitoring System for Coma Patients
99(22)
J. Bethanney Janney
T. Sudhakar
Sindu Divakaran
H. Chandana
L. Caroline Chriselda
5.1 Introduction
100(2)
5.2 Related Works
102(2)
5.3 Materials and Methods
104(7)
5.3.1 Existing System
104(1)
5.3.2 Proposed System
105(1)
5.3.3 Working
105(1)
5.3.4 Module Description
106(1)
5.3.4.1 Pulse Sensor
106(1)
5.3.4.2 Temperature Sensor
107(1)
5.3.4.3 Spirometer
107(1)
5.3.4.4 OpenCV (Open Source Computer Vision)
108(1)
5.3.4.5 Raspberry Pi
108(1)
5.3.4.6 USB Camera
109(1)
5.3.4.7 AVR Module
109(1)
5.3.4.8 Power Supply
109(1)
5.3.4.9 USB to TTL Converter
110(1)
5.3.4.10 EEG of Comatose Patients
110(1)
5.4 Results and Discussion
111(5)
5.5 Conclusion
116(5)
References
117(4)
6 Deep Learning Interpretation of Biomedical Data
121(22)
T.R. Thamizhvani
R. Chandrasekaran
T.R. Ineyathendral
6.1 Introduction
122(3)
6.2 Deep Learning Models
125(7)
6.2.1 Recurrent Neural Networks
125(2)
6.2.2 LSTM/GRU Networks
127(1)
6.2.3 Convolutional Neural Networks
128(2)
6.2.4 Deep Belief Networks
130(1)
6.2.5 Deep Stacking Networks
131(1)
6.3 Interpretation of Deep Learning With Biomedical Data
132(7)
6.4 Conclusion
139(4)
References
140(3)
7 Evolution of Electronic Health Records
143(18)
G. Umashankar
P. Abinaya
J. Premkumar
T. Sudhakar
S. Krishnakumar
7.1 Introduction
143(1)
7.2 Traditional Paper Method
144(1)
7.3 IoMT
144(1)
7.4 Telemedicine and IoMT
145(2)
7.4.1 Advantages of Telemedicine
145(1)
7.4.2 Drawbacks
146(1)
7.4.3 IoMT Advantages with Telemedicine
146(1)
7.4.4 Limitations of IoMT With Telemedicine
147(1)
7.5 Cyber Security
147(1)
7.6 Materials and Methods
147(1)
7.6.1 General Method
147(1)
7.6.2 Data Security
148(1)
7.7 Literature Review
148(2)
7.8 Applications of Electronic Health Records
150(5)
7.8.1 Clinical Research
150(1)
7.8.1.1 Introduction
150(1)
7.8.1.2 Data Significance and Evaluation
151(1)
7.8.1.3 Conclusion
151(1)
7.8.2 Diagnosis and Monitoring
151(1)
7.8.2.1 Introduction
151(1)
7.8.2.2 Contributions
152(1)
7.8.2.3 Applications
152(1)
7.8.3 Track Medical Progression
153(1)
7.8.3.1 Introduction
153(1)
7.8.3.2 Method Used
153(1)
7.8.3.3 Conclusion
154(1)
7.8.4 Wearable Devices
154(1)
7.8.4.1 Introduction
154(1)
7.8.4.2 Proposed Method
155(1)
7.8.4.3 Conclusion
155(1)
7.9 Results and Discussion
155(2)
7.10 Challenges Ahead
157(1)
7.11 Conclusion
158(3)
References
158(3)
8 Architecture of IoMT in Healthcare
161(12)
A. Josephin Arockia Dhiyya
8.1 Introduction
161(4)
8.1.1 On-Body Segment
162(1)
8.1.2 In-Home Segment
162(1)
8.1.3 Network Segment Layer
163(1)
8.1.4 In-Clinic Segment
163(1)
8.1.5 In-Hospital Segment
163(1)
8.1.6 Future of IoMT?
164(1)
8.2 Preferences of the Internet of Things
165(2)
8.2.1 Cost Decrease
165(1)
8.2.2 Proficiency and Efficiency
165(1)
8.2.3 Business Openings
165(1)
8.2.4 Client Experience
166(1)
8.2.5 Portability and Nimbleness
166(1)
8.3 IoMT Progress in COVID-19 Situations: Presentation
167(4)
8.3.1 The IoMT Environment
168(1)
8.3.2 IoMT Pandemic Alleviation Design
169(1)
8.3.3 Man-Made Consciousness and Large Information Innovation in IoMT
170(1)
8.4 Major Applications of IoMT
171(2)
References
172(1)
9 Performance Assessment of IoMT Services and Protocols
173(14)
A. Keerthana
Karthiga
9.1 Introduction
174(1)
9.2 IoMT Architecture and Platform
175(2)
9.2.1 Architecture
176(1)
9.2.2 Devices Integration Layer
177(1)
9.3 Types of Protocols
177(6)
9.3.1 Internet Protocol for Medical IoT Smart Devices
177(1)
9.3.1.1 HTTP
178(1)
9.3.1.2 Message Queue Telemetry Transport (MQTT)
179(1)
9.3.1.3 Constrained Application Protocol (CoAP)
180(1)
9.3.1.4 AMQP: Advanced Message Queuing Protocol (AMQP)
181(1)
9.3.1.5 Extensible Message and Presence Protocol (XMPP)
181(2)
9.3.1.6 DDS
183(1)
9.4 Testing Process in IoMT
183(2)
9.5 Issues and Challenges
185(1)
9.6 Conclusion
185(2)
References
185(2)
10 Performance Evaluation of Wearable IoT-Enabled Mesh Network for Rural Health Monitoring
187(20)
G. Merlin Sheeba
Y. Bevish Jinila
10.1 Introduction
188(2)
10.2 Proposed System Framework
190(10)
10.2.1 System Description
190(2)
10.2.2 Health Monitoring Center
192(1)
10.2.2.1 Body Sensor
192(1)
10.2.2.2 Wireless Sensor Coordinator/Transceiver
192(3)
10.2.2.3 Ontology Information Center
195(1)
10.2.2.4 Mesh Backbone-Placement and Routing
196(4)
10.3 Experimental Evaluation
200(1)
10.4 Performance Evaluation
201(3)
10.4.1 Energy Consumption
201(1)
10.4.2 Survival Rate
201(1)
10.4.3 End-to-End Delay
202(2)
10.5 Conclusion
204(3)
References
204(3)
11 Management of Diabetes Mellitus (DM) for Children and Adults Based on Internet of Things (IoT)
207(18)
S. Krishnakumar
G. Umashankar
V. Lumen Christy
Vikas
R. J. Hemalatha
11.1 Introduction
208(4)
11.1.1 Prevalence
209(1)
11.1.2 Management of Diabetes
209(1)
11.1.3 Blood Glucose Monitoring
210(1)
11.1.4 Continuous Glucose Monitors
211(1)
11.1.5 Minimally Invasive Glucose Monitors
211(1)
11.1.6 Non-Invasive Glucose Monitors
211(1)
11.1.7 Existing System
211(1)
11.2 Materials and Methods
212(7)
11.2.1 Artificial Neural Network
212(1)
11.2.2 Data Acquisition
213(1)
11.2.3 Histogram Calculation
213(1)
11.2.4 IoT Cloud Computing
214(1)
11.2.5 Proposed System
215(1)
11.2.6 Advantages
215(1)
11.2.7 Disadvantages
215(1)
11.2.8 Applications
216(1)
11.2.9 Arduino Pro Mini
216(1)
11.2.10 LM78XX
217(1)
11.2.11 MAX30100
218(1)
11.2.12 LM35 Temperature Sensors
218(1)
11.3 Results and Discussion
219(3)
11.4 Summary
222(1)
11.5 Conclusion
222(3)
References
223(2)
12 Wearable Health Monitoring Systems Using IoMT
225(22)
Jaya Rubi
A. Josephin Arockia Dhivya
12.1 Introduction
225(1)
12.2 IoMT in Developing Wearable Health Surveillance System
226(3)
12.2.1 A Wearable Health Monitoring System with Multi-Parameters
227(1)
12.2.2 Wearable Input Device for Smart Glasses Based on a Wristband-Type Motion-Aware Touch Panel
228(1)
12.2.3 Smart Belt: A Wearable Device for Managing Abdominal Obesity
228(1)
12.2.4 Smart Bracelets: Automating the Personal Safety Using Wearable Smart Jewelry
228(1)
12.3 Vital Parameters That Can Be Monitored Using Wearable Devices
229(11)
12.3.1 Electrocardiogram
230(1)
12.3.2 Heart Rate
231(1)
12.3.3 Blood Pressure
232(1)
12.3.4 Respiration Rate
232(2)
12.3.5 Blood Oxygen Saturation
234(1)
12.3.6 Blood Glucose
235(1)
12.3.7 Skin Perspiration
236(2)
12.3.8 Capnography
238(1)
12.3.9 Body Temperature
239(1)
12.4 Challenges Faced in Customizing Wearable Devices
240(3)
12.4.1 Data Privacy
240(1)
12.4.2 Data Exchange
240(1)
12.4.3 Availability of Resources
241(1)
12.4.4 Storage Capacity
241(1)
12.4.5 Modeling the Relationship Between Acquired Measurement and Diseases
242(1)
12.4.6 Real-Time Processing
242(1)
12.4.7 Intelligence in Medical Care
242(1)
12.5 Conclusion
243(4)
References
244(3)
13 Future of Healthcare: Biomedical Big Data Analysis and IoMT
247(22)
G. Tamiziniyan
A. Keerthana
13.1 Introduction
248(2)
13.2 Big Data and IoT in Healthcare Industry
250(1)
13.3 Biomedical Big Data Types
251(3)
13.3.1 Electronic Health Records
252(1)
13.3.2 Administrative and Claims Data
252(1)
13.3.3 International Patient Disease Registries
252(1)
13.3.4 National Health Surveys
253(1)
13.3.5 Clinical Research and Trials Data
254(1)
13.4 Biomedical Data Acquisition Using IoT
254(2)
13.4.1 Wearable Sensor Suit
254(1)
13.4.2 Smartphones
255(1)
13.4.3 Smart Watches
255(1)
13.5 Biomedical Data Management Using IoT
256(6)
13.5.1 Apache Spark Framework
257(1)
13.5.2 MapReduce
258(1)
13.5.3 Apache Hadoop
258(1)
13.5.4 Clustering Algorithms
259(1)
13.5.5 K-Means Clustering
259(1)
13.5.6 Fuzzy C-Means Clustering
260(1)
13.5.7 DBSCAN
261(1)
13.6 Impact of Big Data and IoMT in Healthcare
262(1)
13.7 Discussions and Conclusions
263(6)
References
264(5)
14 Medical Data Security Using Blockchain With Soft Computing Techniques: A Review
269(20)
Saurabh Sharma
Harish K. Shakya
Ashish Mishra
14.1 Introduction
270(2)
14.2 Blockchain
272(5)
14.2.1 Blockchain Architecture
272(1)
14.2.2 Types of Blockchain Architecture
273(1)
14.2.3 Blockchain Applications
274(2)
14.2.4 General Applications of the Blockchain
276(1)
14.3 Blockchain as a Decentralized Security Framework
277(4)
14.3.1 Characteristics of Blockchain
278(2)
14.3.2 Limitations of Blockchain Technology
280(1)
14.4 Existing Healthcare Data Predictive Analytics Using Soft Computing Techniques in Data Science
281(1)
14.4.1 Data Science in Healthcare
281(1)
14.5 Literature Review: Medical Data Security in Cloud Storage
281(5)
14.6 Conclusion
286(3)
References
287(2)
15 Electronic Health Records: A Transitional View
289(12)
G. Srividhya
15.1 Introduction
289(1)
15.2 Ancient Medical Record, 1600 BC
290(1)
15.3 Greek Medical Record
291(1)
15.4 Islamic Medical Record
291(1)
15.5 European Civilization
292(1)
15.6 Swedish Health Record System
292(1)
15.7 French and German Contributions
293(1)
15.8 American Descriptions
293(4)
15.9 Beginning of Electronic Health Recording
297(1)
15.10 Conclusion
298(3)
References
298(3)
Index 301
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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