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Intelligent Pervasive Computing Systems for Smarter Healthcare

Sangaiah, Arun Kumar / Shantharajah, S. P. / Theagarajan, Padma

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1. Auflage November 2019
448 Seiten, Hardcover
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ISBN: 978-1-119-43896-0
John Wiley & Sons

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A guide to intelligent decision and pervasive computing paradigms for healthcare analytics systems with a focus on the use of bio-sensors

Intelligent Pervasive Computing Systems for Smarter Healthcare describes the innovations in healthcare made possible by computing through bio-sensors. The pervasive computing paradigm offers tremendous advantages in diversified areas of healthcare research and technology. The authors--noted experts in the field--provide the state-of-the-art intelligence paradigm that enables optimization of medical assessment for a healthy, authentic, safer, and more productive environment.

Today's computers are integrated through bio-sensors and generate a huge amount of information that can enhance our ability to process enormous bio-informatics data that can be transformed into meaningful medical knowledge and help with diagnosis, monitoring and tracking health issues, clinical decision making, early detection of infectious disease prevention, and rapid analysis of health hazards. The text examines a wealth of topics such as the design and development of pervasive healthcare technologies, data modeling and information management, wearable biosensors and their systems, and more. This important resource:
* Explores the recent trends and developments in computing through bio-sensors and its technological applications
* Contains a review of biosensors and sensor systems and networks for mobile health monitoring
* Offers an opportunity for readers to examine the concepts and future outlook of intelligence on healthcare systems incorporating biosensor applications
* Includes information on privacy and security issues on wireless body area network for remote healthcare monitoring

Written for scientists and application developers and professionals in related fields, Intelligent Pervasive Computing Systems for Smarter Healthcare is a guide to the most recent developments in intelligent computer systems that are applicable to the healthcare industry.

List of Contributors xvii

1 Intelligent Sensing and Ubiquitous Systems (ISUS) for Smarter and Safer Home Healthcare 1
Rui Silva Moreira, José Torres, Pedro Sobral, and Christophe Soares

1.1 Introduction to Ubicomp for Home Healthcare 1

1.2 Processing and Sensing Issues 3

1.2.1 Remote Patient Monitoring in Home Environments 4

1.2.1.1 Hardware Device 5

1.2.1.2 Sensed Data Processing and Analysis 6

1.2.2 Indoor Location Using Bluetooth Low Energy Beacons 8

1.2.2.1 Bluetooth Low Energy 9

1.2.2.2 Distance Estimation 9

1.3 Integration and Management Issues 14

1.3.1 Cloud-Based Integration of Personal Healthcare Systems 15

1.3.2 SNMP-Based Integration and Interference Free Approach to Personal Healthcare 17

1.4 Communication and Networking Issues 19

1.4.1 Wireless Sensor Network for Home Healthcare 21

1.4.1.1 Home Healthcare System Architecture 21

1.4.1.2 Wireless Sensor Network Evaluation 25

1.5 Intelligence and Reasoning Issues 26

1.5.1 Intelligent Monitoring and Automation in Home Healthcare 26

1.5.2 Personal Activity Detection During Daily Living 30

1.6 Conclusion 32

Bibliography 33

2 PeMo-EC: An Intelligent, Pervasive and Mobile Platform for ECG Signal Acquisition, Processing, and Pre-Diagnostic Extraction 37
Angelo Brayner, José Maria Monteiro, and João Paulo Madeiro

2.1 Electrical System of the Heart 37

2.2 The Electrocardiogram Signal: A Gold Standard for Monitoring People Suffering from Heart Diseases 38

2.3 Pervasive and Mobile Computing: Basic Concepts 40

2.4 Ubiquitous Computing and Healthcare Applications: State of the Art 42

2.5 PeMo-EC: Description of the Proposed Framework 44

2.5.1 Acquisition Module: Biosensors and ECG Data Conditioning 44

2.5.2 Patient's Smartphone Application: ECG Signal Processing Module 49

2.5.3 Physician's Smartphone Application: Query/Alarm Module 54

2.5.4 The Collaborative Database: Data Integration Module 55

2.5.4.1 Motivation 55

2.5.4.2 The Design of the Collaborative Database 57

2.5.4.3 Data Mining and Pattern Recognition 59

2.6 Conclusions 61

Acknowledgements 61

Bibliography 62

3 The Impact of Implantable Sensors in Biomedical Technology on the Future of Healthcare Systems 67
Ashraf Darwish, Gehad Ismail Sayed, and Aboul Ella Hassanien

3.1 Introduction 67

3.2 Related Work 71

3.3 Motivation and Contribution 74

3.4 Fundamentals of IBANs for Healthcare Monitoring 75

3.4.1 ISs in Biomedical Systems 75

3.4.2 Applications of ISs in Biomedical Systems 78

3.4.2.1 Brain Stimulator 78

3.4.2.2 Heart Failure Monitoring 78

3.4.2.3 Blood Glucose Level 80

3.4.3 Security in Implantable Biomedical Systems 80

3.5 Challenges and Future Trends 82

3.6 Conclusion and Recommendation 85

Bibliography 86

4 Social Network's Security Related to Healthcare 91
Fatna Elmendili, Habiba Chaoui, and Younés El Bouzekri El Idrissi

4.1 The Use of Social Networks in Healthcare 91

4.2 The Social Media Respond to a Primary Need of Security 92

4.3 The Type of Medical Data 95

4.3.1 Security of Medical Data 96

4.4 Problematic 97

4.5 Presentation of the Honeypots 98

4.5.1 Principle of Honeypots 98

4.6 Proposal System for Detecting Malicious Profiles on the Health Sector 99

4.6.1 Proposed Solution 100

4.6.1.1 Deployment of Social Honeypots 100

4.6.1.2 Data Collection 103

4.6.1.3 Classification of Users 104

4.7 Results and Discussion 108

4.8 Conclusion 111

Bibliography 111

5 Multi-Sensor Fusion for Context-Aware Applications 115
Veeramuthu Venkatesh, Ponnuraman Balakrishnan, and Pethru Raj

5.1 Introduction 115

5.1.1 What Is an Intelligent Pervasive System? 115

5.1.2 The Significance of Context Awareness for Next-Generation Smarter Environments 117

5.1.2.1 Context-Aware Characteristics 118

5.1.2.2 Context Types and Categorization Schemes 119

5.1.2.3 Context Awareness Management Design Principles 121

5.1.2.4 Context Life Cycle 122

5.1.2.5 Interval (Called Occasionally) 124

5.1.3 Pervasive Healthcare-Enabling Technologies 125

5.1.3.1 Bio-Signal Acquisition 126

5.1.3.2 Communication Technologies 126

5.1.3.3 Data Classification 128

5.1.3.4 Intelligent Agents 128

5.1.3.5 Location-Based Technologies 128

5.1.4 Pervasive Healthcare Challenges 128

5.2 Ambient Methods Used for E-Health 130

5.2.1 Body Area Networks (BANs) 130

5.2.2 Home M2M Sensor Networks 131

5.2.3 Microelectromechanical System (MEMS) 132

5.2.4 Cloud-Based Intelligent Healthcare 132

5.3 Algorithms and Methods 133

5.3.1 Behavioral Pattern Discovery 133

5.3.2 Decision Support System 134

5.4 Intelligent Pervasive Healthcare Applications 134

5.4.1 Health Information Management 134

5.4.2 Location and Context-Aware Services 136

5.4.3 Remote Patient Monitoring 136

5.4.4 Waze: Community-Based Navigation App 138

5.5 Conclusion 138

Bibliography 139

6 IoT-Based Noninvasive Wearable and Remote Intelligent Pervasive Healthcare Monitoring Systems for the Elderly People 141
Stela Vitorino Sampaio

6.1 Introduction 141

6.2 Internet of Things (IoT) and Remote Health Monitoring 141

6.3 Wearable Health Monitoring 143

6.3.1 Wearable Sensors 143

6.4 Related Work 145

6.4.1 Existing Status 146

6.5 Architectural Prototype 147

6.5.1 Data Acquisition and Processing 150

6.5.2 Pervasive and Intelligence Monitoring 151

6.5.3 Communication 153

6.5.4 Predictive Analytics 153

6.5.5 Edge Analytics 154

6.5.6 Ambient Intelligence 155

6.5.7 Privacy and Security 155

6.6 Summary 156

Bibliography 156

7 Pervasive Healthcare System Based on Environmental Monitoring 159
Sangeetha Archunan and Amudha Thangavel

7.1 Introduction 159

7.2 Intelligent Pervasive Computing System 160

7.2.1 Applications of Pervasive Computing 163

7.3 Biosensors for Environmental Monitoring 163

7.3.1 Environmental Monitoring 165

7.3.1.1 Influence of Environmental Factors on Health 167

7.4 IPCS for Healthcare 167

7.4.1 Healthcare System Architecture Based on Environmental Monitoring 171

7.5 Conclusion 174

Bibliography 174

8 Secure Pervasive Healthcare System and Diabetes Prediction Using Heuristic Algorithm 179
Patitha Parameswaran and Rajalakshmi Shenbaga Moorthy

8.1 Introduction 179

8.2 Related Work 181

8.3 System Design 182

8.3.1 Data Collector 183

8.3.2 Security Manager 183

8.3.2.1 Proxy Re-encryption Algorithm 183

8.3.2.2 Key Generator 184

8.3.2.3 Patient 185

8.3.2.4 Proxy Server 185

8.3.2.5 Healthcare Professional 185

8.3.3 Clusterer 186

8.3.3.1 Hybrid Particle Swarm Optimization K-Means (HPSO-K) Algorithm 186

8.3.4 Predictor 191

8.3.4.1 Hidden Markov Model-Based Viterbi Algorithm (HMM-VA) 191

8.4 Implementation 193

8.5 Results and Discussions 196

8.5.1 Analyzing the Performance of PRA 196

8.5.1.1 Time Taken for Encryption 196

8.5.1.2 Storage Space for Re-encrypted Data 196

8.5.1.3 Time Take for Decryption 196

8.5.2 Analyzing the Performance of HPSO-K Algorithm 197

8.5.2.1 Number of Iterations (Generations) to Cluster Patients 198

8.5.2.2 Comparison of Intra-cluster Distance 198

8.5.2.3 Comparison of Inter-cluster Distance 199

8.5.2.4 Number of Patients in Cluster 200

8.5.2.5 Comparison of Time Complexity 201

8.5.3 Analyzing the Performance of HMM-VA 201

8.5.3.1 Forecasting Diabetes 201

8.5.3.2 Comparison of Error Rate 203

8.6 Conclusion 203

Nomenclatures Used 203

Bibliography 204

9 Threshold-Based Energy-Efficient Routing Protocol for Critical Data Transmission to Increase Lifetime in Heterogeneous Wireless Body Area Sensor Network 207
Deepalakshmi Perumalsamy and Navya Venkatamari

9.1 Introduction 207

9.2 Related Works 209

9.3 Proposed Protocol: Threshold-Based Energy-Efficient Routing Protocol for Critical Data Transmission (EERPCDT) 213

9.3.1 Background and Motivation 213

9.3.2 Basic Communication Radio Model 214

9.4 System Model 215

9.4.1 Initialization Phase 216

9.4.2 Routing Phase Selection of Forwarder Node 217

9.4.3 Scheduling Phase 217

9.4.4 Data Transmission Phase 218

9.5 Analysis of Energy Consumption 218

9.6 Simulation Results and Discussions 219

9.6.1 Network Lifetime and Stability Period 219

9.6.2 Residual Energy 220

9.6.3 Throughput 221

9.7 Conclusion and Future Work 222

Bibliography 223

10 Privacy and Security Issues on Wireless Body Area and IoT for Remote Healthcare Monitoring 227
Prabha Selvaraj and Sumathi Doraikannan

10.1 Introduction 227

10.2 Healthcare Monitoring System 227

10.2.1 Evolution of Healthcare Monitoring System 227

10.3 Healthcare Monitoring System 228

10.3.1 Sensor Network 230

10.3.2 Wireless Sensor Network 230

10.3.3 Wireless Body Area Network 230

10.4 Privacy and Security 233

10.4.1 Privacy and Security Issues in Wireless Body Area Network 234

10.5 Attacks and Measures 237

10.5.1 Security Models for Various Levels 241

10.5.1.1 Security Models for Data Collection Level 241

10.5.1.2 Security Models for Data Transmission Level 242

10.5.1.3 Security Models for Data Storage and Access Level 242

10.5.2 Privacy and Security Issues Pertained to Healthcare Applications 243

10.5.3 Issues Related to Health Information Held by an Individual Organization 243

10.5.4 Categorization of Organizational Threats 244

10.6 Internet of Things 248

10.6.1 WBAN Using IoT 248

10.7 Projects and Related Works in Healthcare Monitoring System 249

10.8 Summary 251

Bibliography 251

11 Remote Patient Monitoring: A Key Management and Authentication Framework for Wireless Body Area Networks 255
Padma Theagarajan and Jayashree Nair

11.1 Introduction 255

11.2 RelatedWork 256

11.3 Proposed Framework for Secure Remote Patient Monitoring 258

11.3.1 Proposed Security Framework 259

11.3.2 Key Generation Algorithm: PQSG 260

11.3.3 Key Establishment in NetAMS: KEAMS 262

11.3.3.1 Initiation of Communication by HPA 262

11.3.3.2 Establishment of Key by HMS 263

11.3.3.3 Authentication of HMS 263

11.3.4 Key Establishment in NetSHA: KESHA 265

11.3.4.1 Initiation of Communication by WSH 265

11.3.4.2 Establishment of Key by the HPA 266

11.3.4.3 Acknowledgment by HPA 266

11.4 Performance Analysis 267

11.4.1 Randomness 267

11.4.2 Distinctiveness 268

11.4.3 Complexity 269

11.5 Discussion 271

11.6 Conclusion 272

Bibliography 273

12 Image Analysis Using Smartphones for Medical Applications: A Survey 275
Rajeswari Rajendran and Jothilakshmi Rajendiran

12.1 Introduction 275

12.2 Pervasive Healthcare Using Image-Based Smartphone Applications 276

12.3 Smartphone-Based Image Diagnosis 277

12.3.1 Diagnosis Using Built-In Camera 278

12.3.2 Diagnosis Using External Sensors/Devices 280

12.4 Libraries and Tools for Smartphone-Based Image Analysis 284

12.4.1 Open-Source Libraries for Image Analysis in Smartphones 284

12.4.2 Tools for Cross-Platform Smartphone Application Development 286

12.5 Challenges and Future Perspectives 286

12.6 Conclusion 288

Bibliography 288

13 Bounds of Spreading Rate of Virus for a Network Through an Intuitionistic Fuzzy Graph 291
Deepa Ganesan, Praba Bashyam, Chandrasekaran Vellankoil Marappan, Rajakumar Krishnan, and Krishnamoorthy Venkatesan

13.1 Intuitionistic Fuzzy Matrices Using Incoming and Outgoing Links 292

13.2 Virus Spreading Rate Between Outgoing and Incoming Links 302

13.3 Numerical Examples 305

Bibliography 310

14 Data Mining Techniques for the Detection of the Risk in Cardiovascular Diseases 313
Dinakaran Karunakaran, Vishnu Priya, and Valarmathie Palanisamy

14.1 Introduction 313

14.2 PPG Signal Analysis 315

14.2.1 Pulse Width 315

14.2.2 Pulse Area 315

14.2.3 Peak-to-Peak Interval 316

14.2.4 Pulse Interval 316

14.2.5 Augmentation Index 317

14.2.6 Large Artery Stiffness Index 317

14.2.7 Types of Photoplethysmography 319

14.3 Related Works 319

14.4 Methodology 322

14.4.1 PPG Design and Recording Setup 322

14.5 Preprocessing in PPG Signal 323

14.6 Results and Discussion 325

14.7 Conclusion 327

Bibliography 328

15 Smart Sensing System for Cardio Pulmonary Sound Signals 331
Nersisson Ruban and A.Mary Mekala

15.1 Introduction 331

15.2 Background Theory 332

15.2.1 Human Heart 333

15.2.2 Heart Sounds 334

15.2.3 Origin of Sounds 334

15.2.4 Significance of Detection 334

15.3 Heart Sound Detection 335

15.3.1 Stethoscope 335

15.4 Polyvinylidene Fluoride (PVDF) 336

15.4.1 Properties of PVDF 337

15.4.2 PVDF as Thin Film Piezoelectric Sensor 337

15.4.3 Placement of the Sensor 338

15.4.4 Development of PVDF Sensor 339

15.4.4.1 Steps Involved in the Development of Sensor 340

15.5 Hardware Implementation 341

15.5.1 Charge Amplifier 341

15.5.2 Signal Conditioning Circuits for PVDF Sensor 342

15.5.3 Hardware Circuits 343

15.5.3.1 Design of Charge Amplifier 343

15.5.3.2 Filter Design 344

15.6 LabVIEW Design 346

15.6.1 Signal Acquisition 346

15.6.1.1 Data Acquisition with LabVIEW 347

15.6.2 Fixing of the Threshold Value 348

15.6.3 Fixing the Threshold for Real-Time Signal 349

15.6.4 Fixing the Threshold in Time Scale 350

15.6.5 Separation of Peaks from Resultant Signal (Sample 1) 351

15.6.6 Separation of Peaks from Resultant Signal (Sample 2) 351

15.7 Heart Sound Segmentation 353

15.7.1 Algorithm for Signal Separation 354

15.7.1.1 Case Structure Algorithm 354

15.7.2 Segmented S1 and S2 Sounds 354

15.8 Conclusion 356

Bibliography 357

16 Anomaly Detection and Pattern Matching Algorithm for Healthcare Application: Identifying Ambulance Siren in Traffic 361
Gowthambabu Karthikeyan, Sasikala Ramasamy, and Suresh Kumar Nagarajan

16.1 Introduction 361

16.2 Related Work 364

16.2.1 Role of Sound Detection in Existing Systems 366

16.2.2 Input and Output Parameters 367

16.2.3 Features of Pattern Matching 367

16.3 Pattern Matching Algorithm for Ambulance Siren Detection 368

16.3.1 Sensors 368

16.3.2 Sensor Deviations 368

16.3.3 Traffic Signal 369

16.3.3.1 How Do Traffic Signals Work? 369

16.3.3.2 Traffic Signal 370

16.3.3.3 Sound-Detecting Sensor 370

16.3.4 Pattern Matching Algorithm: Anomaly Detection 372

16.3.4.1 Algorithm and Implementation 374

16.3.4.2 Sound Detection Module 375

16.4 Results and Conclusion 375

Bibliography 376

17 Detecting Diabetic Retinopathy from Retinal Images Using CUDA Deep Neural Network 379
Ricky Parmar, Ramanathan Lakshmanan, Swarnalatha Purushotham, and Rajkumar Soundrapandiyan

17.1 Introduction 379

17.2 Proposed Method 381

17.2.1 Preprocessing 382

17.2.2 Architecture 383

17.2.3 Digital Artifacts 386

17.2.4 Pseudo-classification 387

17.3 Experimental Results 387

17.3.1 Dataset 387

17.3.2 Performance Evaluation Measures 388

17.3.3 Validation of Datasets Using Exponential Power Distribution 388

17.3.4 Ensemble 390

17.3.5 Accuracy and Stats 390

17.4 Conclusion and Future Work 393

Bibliography 394

18 An Energy-Efficient Wireless Body Area Network Design in Health Monitoring Scenarios 397
Kannan Shanmugam and Karthik Subburathinam

18.1 Wireless Body Area Network 397

18.1.1 Overview 397

18.1.2 Architectures of Wireless Body Area Network 398

18.1.2.1 Tier 1: Intra-WBAN Communication 398

18.1.2.2 Tier 2: Inter-WBAN Communication 398

18.1.2.3 Tier 3: Beyond-WBAN Communication 399

18.1.3 Challenges Faced in System Design 399

18.1.3.1 Energy Constraint 401

18.1.3.2 Interference in Communication 401

18.1.3.3 Security 401

18.1.4 Research Problems 401

18.2 Proposed Opportunistic Scheduling 402

18.2.1 Introduction 402

18.2.2 System Model and Problem Formulation 403

18.2.2.1 System Model 403

18.2.2.2 Problem Formulation 404

18.2.3 Heuristic Scheduling 404

18.2.4 Dynamic Super-Frame Length Adjustment 407

18.2.4.1 Problem Formulation 407

18.3 Performance Analysis Environment and Metrics 408

18.3.1 Heuristic Scheduling with Fixed Super-Frame Length 409

18.3.2 Heuristic Scheduling with Dynamic Super-Frame Length 410

18.4 Summary 410

Bibliography 411

Index 413
ARUN KUMAR SANGAIAH, PHD, is currently associated with the School of Computer Science and Engineering, VIT University, Vellore, India.

S. P. SHANTHARAJAH, PHD, is currently associated with the School of Information Technology and Engineering, VIT University, Vellore, India.

PADMA THEAGARAJAN, PHD, is currently associated with the Department of Computer Applications, Sona College of Technology, Salem, India.