Intelligent Pervasive Computing Systems for Smarter Healthcare.
- 1st ed.
- 1 online resource (444 pages)
Cover -- Title Page -- Copyright -- Contents -- List of Contributors -- Chapter 1 Intelligent Sensing and Ubiquitous Systems (ISUS) for Smarter and Safer Home Healthcare -- 1.1 Introduction to Ubicomp for Home Healthcare -- 1.2 Processing and Sensing Issues -- 1.2.1 Remote Patient Monitoring in Home Environments -- 1.2.1.1 Hardware Device -- 1.2.1.2 Sensed Data Processing and Analysis -- 1.2.2 Indoor Location Using Bluetooth Low Energy Beacons -- 1.2.2.1 Bluetooth Low Energy -- 1.2.2.2 Distance Estimation -- 1.3 Integration and Management Issues -- 1.3.1 Cloud‐Based Integration of Personal Healthcare Systems -- 1.3.2 SNMP‐Based Integration and Interference Free Approach to Personal Healthcare -- 1.4 Communication and Networking Issues -- 1.4.1 Wireless Sensor Network for Home Healthcare -- 1.4.1.1 Home Healthcare System Architecture -- 1.4.1.2 Wireless Sensor Network Evaluation -- 1.5 Intelligence and Reasoning Issues -- 1.5.1 Intelligent Monitoring and Automation in Home Healthcare -- 1.5.2 Personal Activity Detection During Daily Living -- 1.6 Conclusion -- Bibliography -- Chapter 2 PeMo‐EC: An Intelligent, Pervasive and Mobile Platform for ECG Signal Acquisition, Processing, and Pre‐Diagnostic Extraction -- 2.1 Electrical System of the Heart -- 2.2 The Electrocardiogram Signal: A Gold Standard for Monitoring People Suffering from Heart Diseases -- 2.3 Pervasive and Mobile Computing: Basic Concepts -- 2.4 Ubiquitous Computing and Healthcare Applications: State of the Art -- 2.5 PeMo‐EC: Description of the Proposed Framework -- 2.5.1 Acquisition Module: Biosensors and ECG Data Conditioning -- 2.5.2 Patient's Smartphone Application: ECG Signal Processing Module -- 2.5.3 Physician's Smartphone Application: Query/Alarm Module -- 2.5.4 The Collaborative Database: Data Integration Module -- 2.5.4.1 Motivation. 2.5.4.2 The Design of the Collaborative Database -- 2.5.4.3 Data Mining and Pattern Recognition -- 2.6 Conclusions -- Acknowledgements -- Bibliography -- Chapter 3 The Impact of Implantable Sensors in Biomedical Technology on the Future of Healthcare Systems -- 3.1 Introduction -- 3.2 Related Work -- 3.3 Motivation and Contribution -- 3.4 Fundamentals of IBANs for Healthcare Monitoring -- 3.4.1 ISs in Biomedical Systems -- 3.4.2 Applications of ISs in Biomedical Systems -- 3.4.2.1 Brain Stimulator -- 3.4.2.2 Heart Failure Monitoring -- 3.4.2.3 Blood Glucose Level -- 3.4.3 Security in Implantable Biomedical Systems -- 3.5 Challenges and Future Trends -- 3.6 Conclusion and Recommendation -- Bibliography -- Chapter 4 Social Network's Security Related to Healthcare -- 4.1 The Use of Social Networks in Healthcare -- 4.2 The Social Media Respond to a Primary Need of Security -- 4.3 The Type of Medical Data -- 4.3.1 Security of Medical Data -- 4.4 Problematic -- 4.5 Presentation of the Honeypots -- 4.5.1 Principle of Honeypots -- 4.6 Proposal System for Detecting Malicious Profiles on the Health Sector -- 4.6.1 Proposed Solution -- 4.6.1.1 Deployment of Social Honeypots -- 4.6.1.2 Data Collection -- 4.6.1.3 Classification of Users -- 4.7 Results and Discussion -- 4.8 Conclusion -- Bibliography -- Chapter 5 Multi‐Sensor Fusion for Context‐Aware Applications -- 5.1 Introduction -- 5.1.1 What Is an Intelligent Pervasive System? -- 5.1.2 The Significance of Context Awareness for Next‐Generation Smarter Environments -- 5.1.2.1 Context‐Aware Characteristics -- 5.1.2.2 Context Types and Categorization Schemes -- 5.1.2.3 Context Awareness Management Design Principles -- 5.1.2.4 Context Life Cycle -- 5.1.2.5 Interval (Called Occasionally) -- 5.1.3 Pervasive Healthcare‐Enabling Technologies -- 5.1.3.1 Bio‐Signal Acquisition -- 5.1.3.2 Communication Technologies. 5.1.3.3 Data Classification -- 5.1.3.4 Intelligent Agents -- 5.1.3.5 Location‐Based Technologies -- 5.1.4 Pervasive Healthcare Challenges -- 5.2 Ambient Methods Used for E‐Health -- 5.2.1 Body Area Networks (BANs) -- 5.2.2 Home M2M Sensor Networks -- 5.2.3 Microelectromechanical System (MEMS) -- 5.2.4 Cloud‐Based Intelligent Healthcare -- 5.3 Algorithms and Methods -- 5.3.1 Behavioral Pattern Discovery -- 5.3.2 Decision Support System -- 5.4 Intelligent Pervasive Healthcare Applications -- 5.4.1 Health Information Management -- 5.4.2 Location and Context‐Aware Services -- 5.4.3 Remote Patient Monitoring -- 5.4.4 Waze: Community‐Based Navigation App -- 5.5 Conclusion -- Bibliography -- Chapter 6 IoT‐Based Noninvasive Wearable and Remote Intelligent Pervasive Healthcare Monitoring Systems for the Elderly People -- 6.1 Introduction -- 6.2 Internet of Things (IoT) and Remote Health Monitoring -- 6.3 Wearable Health Monitoring -- 6.3.1 Wearable Sensors -- 6.4 Related Work -- 6.4.1 Existing Status -- 6.5 Architectural Prototype -- 6.5.1 Data Acquisition and Processing -- 6.5.2 Pervasive and Intelligence Monitoring -- 6.5.3 Communication -- 6.5.4 Predictive Analytics -- 6.5.5 Edge Analytics -- 6.5.6 Ambient Intelligence -- 6.5.7 Privacy and Security -- 6.6 Summary -- Bibliography -- Chapter 7 Pervasive Healthcare System Based on Environmental Monitoring -- 7.1 Introduction -- 7.2 Intelligent Pervasive Computing System -- 7.2.1 Applications of Pervasive Computing -- 7.3 Biosensors for Environmental Monitoring -- 7.3.1 Environmental Monitoring -- 7.3.1.1 Influence of Environmental Factors on Health -- 7.4 IPCS for Healthcare -- 7.4.1 Healthcare System Architecture Based on Environmental Monitoring -- 7.5 Conclusion -- Bibliography -- Chapter 8 Secure Pervasive Healthcare System and Diabetes Prediction Using Heuristic Algorithm -- 8.1 Introduction. 8.2 Related Work -- 8.3 System Design -- 8.3.1 Data Collector -- 8.3.2 Security Manager -- 8.3.2.1 Proxy Re‐encryption Algorithm -- 8.3.2.2 Key Generator -- 8.3.2.3 Patient -- 8.3.2.4 Proxy Server -- 8.3.2.5 Healthcare Professional -- 8.3.3 Clusterer -- 8.3.3.1 Hybrid Particle Swarm Optimization K‐Means (HPSO‐K) Algorithm -- 8.3.4 Predictor -- 8.3.4.1 Hidden Markov Model‐Based Viterbi Algorithm (HMM‐VA) -- 8.4 Implementation -- 8.5 Results and Discussions -- 8.5.1 Analyzing the Performance of PRA -- 8.5.1.1 Time Taken for Encryption -- 8.5.1.2 Storage Space for Re‐encrypted Data -- 8.5.1.3 Time Take for Decryption -- 8.5.2 Analyzing the Performance of HPSO‐K Algorithm -- 8.5.2.1 Number of Iterations (Generations) to Cluster Patients -- 8.5.2.2 Comparison of Intra‐cluster Distance -- 8.5.2.3 Comparison of Inter‐cluster Distance -- 8.5.2.4 Number of Patients in Cluster -- 8.5.2.5 Comparison of Time Complexity -- 8.5.3 Analyzing the Performance of HMM‐VA -- 8.5.3.1 Forecasting Diabetes -- 8.5.3.2 Comparison of Error Rate -- 8.6 Conclusion -- Nomenclatures Used -- Bibliography -- Chapter 9 Threshold‐Based Energy‐Efficient Routing Protocol for Critical Data Transmission to Increase Lifetime in Heterogeneous Wireless Body Area Sensor Network -- 9.1 Introduction -- 9.2 Related Works -- 9.3 Proposed Protocol: Threshold‐Based Energy‐Efficient Routing Protocol for Critical Data Transmission (EERPCDT) -- 9.3.1 Background and Motivation -- 9.3.2 Basic Communication Radio Model -- 9.4 System Model -- 9.4.1 Initialization Phase -- 9.4.2 Routing Phase Selection of Forwarder Node -- 9.4.3 Scheduling Phase -- 9.4.4 Data Transmission Phase -- 9.5 Analysis of Energy Consumption -- 9.6 Simulation Results and Discussions -- 9.6.1 Network Lifetime and Stability Period -- 9.6.2 Residual Energy -- 9.6.3 Throughput -- 9.7 Conclusion and Future Work -- Bibliography. Chapter 10 Privacy and Security Issues on Wireless Body Area and IoT for Remote Healthcare Monitoring -- 10.1 Introduction -- 10.2 Healthcare Monitoring System -- 10.2.1 Evolution of Healthcare Monitoring System -- 10.3 Healthcare Monitoring System -- 10.3.1 Sensor Network -- 10.3.2 Wireless Sensor Network -- 10.3.3 Wireless Body Area Network -- 10.4 Privacy and Security -- 10.4.1 Privacy and Security Issues in Wireless Body Area Network -- 10.5 Attacks and Measures -- 10.5.1 Security Models for Various Levels -- 10.5.1.1 Security Models for Data Collection Level -- 10.5.1.2 Security Models for Data Transmission Level -- 10.5.1.3 Security Models for Data Storage and Access Level -- 10.5.2 Privacy and Security Issues Pertained to Healthcare Applications -- 10.5.3 Issues Related to Health Information Held by an Individual Organization -- 10.5.4 Categorization of Organizational Threats -- 10.6 Internet of Things -- 10.6.1 WBAN Using IoT -- 10.7 Projects and Related Works in Healthcare Monitoring System -- 10.8 Summary -- Bibliography -- Chapter 11 Remote Patient Monitoring: A Key Management and Authentication Framework for Wireless Body Area Networks -- 11.1 Introduction -- 11.2 Related Work -- 11.3 Proposed Framework for Secure Remote Patient Monitoring -- 11.3.1 Proposed Security Framework -- 11.3.2 Key Generation Algorithm: PQSG -- 11.3.3 Key Establishment in NetAMS: KEAMS -- 11.3.3.1 Initiation of Communication by HPA -- 11.3.3.2 Establishment of Key by HMS -- 11.3.3.3 Authentication of HMS -- 11.3.4 Key Establishment in NetSHA: KESHA -- 11.3.4.1 Initiation of Communication by WSH -- 11.3.4.2 Establishment of Key by the HPA -- 11.3.4.3 Acknowledgment by HPA -- 11.4 Performance Analysis -- 11.4.1 Randomness -- 11.4.2 Distinctiveness -- 11.4.3 Complexity -- 11.5 Discussion -- 11.6 Conclusion -- Bibliography. Chapter 12 Image Analysis Using Smartphones for Medical Applications: A Survey.