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Intelligent Pervasive Computing Systems for Smarter Healthcare

1. Aufl.

von: Arun Kumar Sangaiah, S.P. Shantharajah, Padma Theagarajan
114,99 €
Verlag:	Wiley
Format:	PDF
Veröffentl.:	21.06.2019
ISBN/EAN:	9781119438991
Sprache:	englisch
Anzahl Seiten:	448

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Titelbeschreibung

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: <ul> <li>Explores the recent trends and developments in computing through bio-sensors and its technological applications</li> <li>Contains a review of biosensors and sensor systems and networks for mobile health monitoring</li> <li>Offers an opportunity for readers to examine the concepts and future outlook of intelligence on healthcare systems incorporating biosensor applications</li> <li>Includes information on privacy and security issues on wireless body area network for remote healthcare monitoring</li> </ul> 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.

Inhaltsverzeichnis

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

Autorenportrait

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.

Back cover copy

A guide to intelligent decision and pervasive computing paradigms for healthcare analytics systems with a focus on the use of biosensors Intelligent Pervasive Computing Systems for Smarter Healthcare describes the innovations in healthcare made possible by computing through biosensors. 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 biosensors 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: <ul> <li>Explores the recent trends and developments in computing through biosensors and its technological applications</li> <li>Contains a review of biosensors and sensor systems and networks for mobile health monitoring</li> <li>Offers an opportunity for readers to examine the concepts and future outlook of intelligence on healthcare systems incorporating biosensor applications</li> <li>Includes information on privacy and security issues on wireless body area network for remote healthcare monitoring</li> </ul> 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.