Intelligent Pervasive Computing Systems for Smarter Healthcare

Inbunden, Engelska, 2019

AvArun Kumar Sangaiah,S.P. Shantharajah,Padma Theagarajan

1 929 kr

Beställningsvara. Skickas inom 5-8 vardagar. Fri frakt för medlemmar vid köp för minst 249 kr.

A guide to intelligent decision and pervasive computing paradigms for healthcare analytics systems with a focus on the use of bio-sensorsIntelligent 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 applicationsContains a review of biosensors and sensor systems and networks for mobile health monitoringOffers an opportunity for readers to examine the concepts and future outlook of intelligence on healthcare systems incorporating biosensor applicationsIncludes information on privacy and security issues on wireless body area network for remote healthcare monitoringWritten 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.

Produktinformation

Utgivningsdatum2019-10-08
Mått158 x 231 x 25 mm
Vikt862 g
FormatInbunden
SpråkEngelska
Antal sidor448
FörlagJohn Wiley & Sons Inc
ISBN9781119438960

Tillhör följande kategorier

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.

List of Contributors xvii1 Intelligent Sensing and Ubiquitous Systems (ISUS) for Smarter and Safer Home Healthcare 1Rui Silva Moreira, José Torres, Pedro Sobral, and Christophe Soares1.1 Introduction to Ubicomp for Home Healthcare 11.2 Processing and Sensing Issues 31.2.1 Remote Patient Monitoring in Home Environments 41.2.1.1 Hardware Device 51.2.1.2 Sensed Data Processing and Analysis 61.2.2 Indoor Location Using Bluetooth Low Energy Beacons 81.2.2.1 Bluetooth Low Energy 91.2.2.2 Distance Estimation 91.3 Integration and Management Issues 141.3.1 Cloud-Based Integration of Personal Healthcare Systems 151.3.2 SNMP-Based Integration and Interference Free Approach to Personal Healthcare 171.4 Communication and Networking Issues 191.4.1 Wireless Sensor Network for Home Healthcare 211.4.1.1 Home Healthcare System Architecture 211.4.1.2 Wireless Sensor Network Evaluation 251.5 Intelligence and Reasoning Issues 261.5.1 Intelligent Monitoring and Automation in Home Healthcare 261.5.2 Personal Activity Detection During Daily Living 301.6 Conclusion 32Bibliography 332 PeMo-EC: An Intelligent, Pervasive and Mobile Platform for ECG Signal Acquisition, Processing, and Pre-Diagnostic Extraction 37Angelo Brayner, José Maria Monteiro, and João Paulo Madeiro2.1 Electrical System of the Heart 372.2 The Electrocardiogram Signal: A Gold Standard for Monitoring People Suffering from Heart Diseases 382.3 Pervasive and Mobile Computing: Basic Concepts 402.4 Ubiquitous Computing and Healthcare Applications: State of the Art 422.5 PeMo-EC: Description of the Proposed Framework 442.5.1 Acquisition Module: Biosensors and ECG Data Conditioning 442.5.2 Patient’s Smartphone Application: ECG Signal Processing Module 492.5.3 Physician’s Smartphone Application: Query/Alarm Module 542.5.4 The Collaborative Database: Data Integration Module 552.5.4.1 Motivation 552.5.4.2 The Design of the Collaborative Database 572.5.4.3 Data Mining and Pattern Recognition 592.6 Conclusions 61Acknowledgements 61Bibliography 623 The Impact of Implantable Sensors in Biomedical Technology on the Future of Healthcare Systems 67Ashraf Darwish, Gehad Ismail Sayed, and Aboul Ella Hassanien3.1 Introduction 673.2 Related Work 713.3 Motivation and Contribution 743.4 Fundamentals of IBANs for Healthcare Monitoring 753.4.1 ISs in Biomedical Systems 753.4.2 Applications of ISs in Biomedical Systems 783.4.2.1 Brain Stimulator 783.4.2.2 Heart Failure Monitoring 783.4.2.3 Blood Glucose Level 803.4.3 Security in Implantable Biomedical Systems 803.5 Challenges and Future Trends 823.6 Conclusion and Recommendation 85Bibliography 864 Social Network’s Security Related to Healthcare 91Fatna Elmendili, Habiba Chaoui, and Younés El Bouzekri El Idrissi4.1 The Use of Social Networks in Healthcare 914.2 The Social Media Respond to a Primary Need of Security 924.3 The Type of Medical Data 954.3.1 Security of Medical Data 964.4 Problematic 974.5 Presentation of the Honeypots 984.5.1 Principle of Honeypots 984.6 Proposal System for Detecting Malicious Profiles on the Health Sector 994.6.1 Proposed Solution 1004.6.1.1 Deployment of Social Honeypots 1004.6.1.2 Data Collection 1034.6.1.3 Classification of Users 1044.7 Results and Discussion 1084.8 Conclusion 111Bibliography 1115 Multi-Sensor Fusion for Context-Aware Applications 115Veeramuthu Venkatesh, Ponnuraman Balakrishnan, and Pethru Raj5.1 Introduction 1155.1.1 What Is an Intelligent Pervasive System? 1155.1.2 The Significance of Context Awareness for Next-Generation Smarter Environments 1175.1.2.1 Context-Aware Characteristics 1185.1.2.2 Context Types and Categorization Schemes 1195.1.2.3 Context Awareness Management Design Principles 1215.1.2.4 Context Life Cycle 1225.1.2.5 Interval (Called Occasionally) 1245.1.3 Pervasive Healthcare-Enabling Technologies 1255.1.3.1 Bio-Signal Acquisition 1265.1.3.2 Communication Technologies 1265.1.3.3 Data Classification 1285.1.3.4 Intelligent Agents 1285.1.3.5 Location-Based Technologies 1285.1.4 Pervasive Healthcare Challenges 1285.2 Ambient Methods Used for E-Health 1305.2.1 Body Area Networks (BANs) 1305.2.2 Home M2M Sensor Networks 1315.2.3 Microelectromechanical System (MEMS) 1325.2.4 Cloud-Based Intelligent Healthcare 1325.3 Algorithms and Methods 1335.3.1 Behavioral Pattern Discovery 1335.3.2 Decision Support System 1345.4 Intelligent Pervasive Healthcare Applications 1345.4.1 Health Information Management 1345.4.2 Location and Context-Aware Services 1365.4.3 Remote Patient Monitoring 1365.4.4 Waze: Community-Based Navigation App 1385.5 Conclusion 138Bibliography 1396 IoT-Based Noninvasive Wearable and Remote Intelligent Pervasive Healthcare Monitoring Systems for the Elderly People 141Stela Vitorino Sampaio6.1 Introduction 1416.2 Internet of Things (IoT) and Remote Health Monitoring 1416.3 Wearable Health Monitoring 1436.3.1 Wearable Sensors 1436.4 Related Work 1456.4.1 Existing Status 1466.5 Architectural Prototype 1476.5.1 Data Acquisition and Processing 1506.5.2 Pervasive and Intelligence Monitoring 1516.5.3 Communication 1536.5.4 Predictive Analytics 1536.5.5 Edge Analytics 1546.5.6 Ambient Intelligence 1556.5.7 Privacy and Security 1556.6 Summary 156Bibliography 1567 Pervasive Healthcare System Based on Environmental Monitoring 159Sangeetha Archunan and Amudha Thangavel7.1 Introduction 1597.2 Intelligent Pervasive Computing System 1607.2.1 Applications of Pervasive Computing 1637.3 Biosensors for Environmental Monitoring 1637.3.1 Environmental Monitoring 1657.3.1.1 Influence of Environmental Factors on Health 1677.4 IPCS for Healthcare 1677.4.1 Healthcare System Architecture Based on Environmental Monitoring 1717.5 Conclusion 174Bibliography 1748 Secure Pervasive Healthcare System and Diabetes Prediction Using Heuristic Algorithm 179Patitha Parameswaran and Rajalakshmi Shenbaga Moorthy8.1 Introduction 1798.2 Related Work 1818.3 System Design 1828.3.1 Data Collector 1838.3.2 Security Manager 1838.3.2.1 Proxy Re-encryption Algorithm 1838.3.2.2 Key Generator 1848.3.2.3 Patient 1858.3.2.4 Proxy Server 1858.3.2.5 Healthcare Professional 1858.3.3 Clusterer 1868.3.3.1 Hybrid Particle Swarm Optimization K-Means (HPSO-K) Algorithm 1868.3.4 Predictor 1918.3.4.1 Hidden Markov Model-Based Viterbi Algorithm (HMM-VA) 1918.4 Implementation 1938.5 Results and Discussions 1968.5.1 Analyzing the Performance of PRA 1968.5.1.1 Time Taken for Encryption 1968.5.1.2 Storage Space for Re-encrypted Data 1968.5.1.3 Time Take for Decryption 1968.5.2 Analyzing the Performance of HPSO-K Algorithm 1978.5.2.1 Number of Iterations (Generations) to Cluster Patients 1988.5.2.2 Comparison of Intra-cluster Distance 1988.5.2.3 Comparison of Inter-cluster Distance 1998.5.2.4 Number of Patients in Cluster 2008.5.2.5 Comparison of Time Complexity 2018.5.3 Analyzing the Performance of HMM-VA 2018.5.3.1 Forecasting Diabetes 2018.5.3.2 Comparison of Error Rate 2038.6 Conclusion 203Nomenclatures Used 203Bibliography 2049 Threshold-Based Energy-Efficient Routing Protocol for Critical Data Transmission to Increase Lifetime in Heterogeneous Wireless Body Area Sensor Network 207Deepalakshmi Perumalsamy and Navya Venkatamari9.1 Introduction 2079.2 Related Works 2099.3 Proposed Protocol: Threshold-Based Energy-Efficient Routing Protocol for Critical Data Transmission (EERPCDT) 2139.3.1 Background and Motivation 2139.3.2 Basic Communication Radio Model 2149.4 System Model 2159.4.1 Initialization Phase 2169.4.2 Routing Phase Selection of Forwarder Node 2179.4.3 Scheduling Phase 2179.4.4 Data Transmission Phase 2189.5 Analysis of Energy Consumption 2189.6 Simulation Results and Discussions 2199.6.1 Network Lifetime and Stability Period 2199.6.2 Residual Energy 2209.6.3 Throughput 2219.7 Conclusion and Future Work 222Bibliography 22310 Privacy and Security Issues on Wireless Body Area and IoT for Remote Healthcare Monitoring 227Prabha Selvaraj and Sumathi Doraikannan10.1 Introduction 22710.2 Healthcare Monitoring System 22710.2.1 Evolution of Healthcare Monitoring System 22710.3 Healthcare Monitoring System 22810.3.1 Sensor Network 23010.3.2 Wireless Sensor Network 23010.3.3 Wireless Body Area Network 23010.4 Privacy and Security 23310.4.1 Privacy and Security Issues in Wireless Body Area Network 23410.5 Attacks and Measures 23710.5.1 Security Models for Various Levels 24110.5.1.1 Security Models for Data Collection Level 24110.5.1.2 Security Models for Data Transmission Level 24210.5.1.3 Security Models for Data Storage and Access Level 24210.5.2 Privacy and Security Issues Pertained to Healthcare Applications 24310.5.3 Issues Related to Health Information Held by an Individual Organization 24310.5.4 Categorization of Organizational Threats 24410.6 Internet of Things 24810.6.1 WBAN Using IoT 24810.7 Projects and Related Works in Healthcare Monitoring System 24910.8 Summary 251Bibliography 25111 Remote Patient Monitoring: A Key Management and Authentication Framework for Wireless Body Area Networks 255Padma Theagarajan and Jayashree Nair11.1 Introduction 25511.2 RelatedWork 25611.3 Proposed Framework for Secure Remote Patient Monitoring 25811.3.1 Proposed Security Framework 25911.3.2 Key Generation Algorithm: PQSG 26011.3.3 Key Establishment in NetAMS: KEAMS 26211.3.3.1 Initiation of Communication by HPA 26211.3.3.2 Establishment of Key by HMS 26311.3.3.3 Authentication of HMS 26311.3.4 Key Establishment in NetSHA: KESHA 26511.3.4.1 Initiation of Communication by WSH 26511.3.4.2 Establishment of Key by the HPA 26611.3.4.3 Acknowledgment by HPA 26611.4 Performance Analysis 26711.4.1 Randomness 26711.4.2 Distinctiveness 26811.4.3 Complexity 26911.5 Discussion 27111.6 Conclusion 272Bibliography 27312 Image Analysis Using Smartphones for Medical Applications: A Survey 275Rajeswari Rajendran and Jothilakshmi Rajendiran12.1 Introduction 27512.2 Pervasive Healthcare Using Image-Based Smartphone Applications 27612.3 Smartphone-Based Image Diagnosis 27712.3.1 Diagnosis Using Built-In Camera 27812.3.2 Diagnosis Using External Sensors/Devices 28012.4 Libraries and Tools for Smartphone-Based Image Analysis 28412.4.1 Open-Source Libraries for Image Analysis in Smartphones 28412.4.2 Tools for Cross-Platform Smartphone Application Development 28612.5 Challenges and Future Perspectives 28612.6 Conclusion 288Bibliography 28813 Bounds of Spreading Rate of Virus for a Network Through an Intuitionistic Fuzzy Graph 291Deepa Ganesan, Praba Bashyam, Chandrasekaran Vellankoil Marappan, Rajakumar Krishnan, and Krishnamoorthy Venkatesan13.1 Intuitionistic Fuzzy Matrices Using Incoming and Outgoing Links 29213.2 Virus Spreading Rate Between Outgoing and Incoming Links 30213.3 Numerical Examples 305Bibliography 31014 Data Mining Techniques for the Detection of the Risk in Cardiovascular Diseases 313Dinakaran Karunakaran, Vishnu Priya, and Valarmathie Palanisamy14.1 Introduction 31314.2 PPG Signal Analysis 31514.2.1 Pulse Width 31514.2.2 Pulse Area 31514.2.3 Peak-to-Peak Interval 31614.2.4 Pulse Interval 31614.2.5 Augmentation Index 31714.2.6 Large Artery Stiffness Index 31714.2.7 Types of Photoplethysmography 31914.3 Related Works 31914.4 Methodology 32214.4.1 PPG Design and Recording Setup 32214.5 Preprocessing in PPG Signal 32314.6 Results and Discussion 32514.7 Conclusion 327Bibliography 32815 Smart Sensing System for Cardio Pulmonary Sound Signals 331Nersisson Ruban and A.Mary Mekala15.1 Introduction 33115.2 Background Theory 33215.2.1 Human Heart 33315.2.2 Heart Sounds 33415.2.3 Origin of Sounds 33415.2.4 Significance of Detection 33415.3 Heart Sound Detection 33515.3.1 Stethoscope 33515.4 Polyvinylidene Fluoride (PVDF) 33615.4.1 Properties of PVDF 33715.4.2 PVDF as Thin Film Piezoelectric Sensor 33715.4.3 Placement of the Sensor 33815.4.4 Development of PVDF Sensor 33915.4.4.1 Steps Involved in the Development of Sensor 34015.5 Hardware Implementation 34115.5.1 Charge Amplifier 34115.5.2 Signal Conditioning Circuits for PVDF Sensor 34215.5.3 Hardware Circuits 34315.5.3.1 Design of Charge Amplifier 34315.5.3.2 Filter Design 34415.6 LabVIEW Design 34615.6.1 Signal Acquisition 34615.6.1.1 Data Acquisition with LabVIEW 34715.6.2 Fixing of the Threshold Value 34815.6.3 Fixing the Threshold for Real-Time Signal 34915.6.4 Fixing the Threshold in Time Scale 35015.6.5 Separation of Peaks from Resultant Signal (Sample 1) 35115.6.6 Separation of Peaks from Resultant Signal (Sample 2) 35115.7 Heart Sound Segmentation 35315.7.1 Algorithm for Signal Separation 35415.7.1.1 Case Structure Algorithm 35415.7.2 Segmented S1 and S2 Sounds 35415.8 Conclusion 356Bibliography 35716 Anomaly Detection and Pattern Matching Algorithm for Healthcare Application: Identifying Ambulance Siren in Traffic 361Gowthambabu Karthikeyan, Sasikala Ramasamy, and Suresh Kumar Nagarajan16.1 Introduction 36116.2 Related Work 36416.2.1 Role of Sound Detection in Existing Systems 36616.2.2 Input and Output Parameters 36716.2.3 Features of Pattern Matching 36716.3 Pattern Matching Algorithm for Ambulance Siren Detection 36816.3.1 Sensors 36816.3.2 Sensor Deviations 36816.3.3 Traffic Signal 36916.3.3.1 How Do Traffic Signals Work? 36916.3.3.2 Traffic Signal 37016.3.3.3 Sound-Detecting Sensor 37016.3.4 Pattern Matching Algorithm: Anomaly Detection 37216.3.4.1 Algorithm and Implementation 37416.3.4.2 Sound Detection Module 37516.4 Results and Conclusion 375Bibliography 37617 Detecting Diabetic Retinopathy from Retinal Images Using CUDA Deep Neural Network 379Ricky Parmar, Ramanathan Lakshmanan, Swarnalatha Purushotham, and Rajkumar Soundrapandiyan17.1 Introduction 37917.2 Proposed Method 38117.2.1 Preprocessing 38217.2.2 Architecture 38317.2.3 Digital Artifacts 38617.2.4 Pseudo-classification 38717.3 Experimental Results 38717.3.1 Dataset 38717.3.2 Performance Evaluation Measures 38817.3.3 Validation of Datasets Using Exponential Power Distribution 38817.3.4 Ensemble 39017.3.5 Accuracy and Stats 39017.4 Conclusion and Future Work 393Bibliography 39418 An Energy-Efficient Wireless Body Area Network Design in Health Monitoring Scenarios 397Kannan Shanmugam and Karthik Subburathinam18.1 Wireless Body Area Network 39718.1.1 Overview 39718.1.2 Architectures of Wireless Body Area Network 39818.1.2.1 Tier 1: Intra-WBAN Communication 39818.1.2.2 Tier 2: Inter-WBAN Communication 39818.1.2.3 Tier 3: Beyond-WBAN Communication 39918.1.3 Challenges Faced in System Design 39918.1.3.1 Energy Constraint 40118.1.3.2 Interference in Communication 40118.1.3.3 Security 40118.1.4 Research Problems 40118.2 Proposed Opportunistic Scheduling 40218.2.1 Introduction 40218.2.2 System Model and Problem Formulation 40318.2.2.1 System Model 40318.2.2.2 Problem Formulation 40418.2.3 Heuristic Scheduling 40418.2.4 Dynamic Super-Frame Length Adjustment 40718.2.4.1 Problem Formulation 40718.3 Performance Analysis Environment and Metrics 40818.3.1 Heuristic Scheduling with Fixed Super-Frame Length 40918.3.2 Heuristic Scheduling with Dynamic Super-Frame Length 41018.4 Summary 410Bibliography 411Index 413