The E-Medicine, E-Health, M-Health, Telemedicine, and Telehealth Handbook provides extensive coverage of modern telecommunication in the medical industry, from sensors on and within the body to electronic medical records and beyond. This two-volume set describes how information and communication technologies, the internet, wireless networks, databases, and telemetry permit the transmission and control of information within and between medical centers.

Table of Contents

• Preface
• Acknowledgments
• Editors
• Contributors
• VOLUME I
• Section I Integration of eMedicine, Telemedicine, eHealth, and mHealth
• 1. Integrating Telemedicine and Telehealth—Advancing Health at a Distance - Habib F. Rashvand and Kuei-Fang Hsiao
• 1.1 Introduction
• 1.1.1 Telemedicine versus Telehealth
• 1.1.1.1 Definitions
• 1.1.2 Technologies versus Services
• 1.2 Technology-Enabled Distant Health
• 1.2.1 Telemedicine Technological Requirements
• 1.2.2 Telehealth Technological Requirements
• 1.3 Typical Distant-Health Examples
• 1.3.1 Smart Medical Shirts
• 1.3.2 Haptic Platform
• 1.3.3 Overgrown Cities
• 1.3.4 Rural Health
• 1.3.5 Satellite Telehealth
• 1.4 Integrated Service Management
• 1.4.1 Telemedicine Critical Technologies
• 1.4.2 MOT: Marketing
• 1.4.2.1 Marketing Orientation
• 1.4.2.2 Holistic Marketing
• 1.4.3 MOT: Innovative Deployment
• 1.4.3.1 Integrated TLM-TLH Service Paradigm
• 1.4.3.2 Holistic Approach
• 1.4.3.3 Building Trust
• 1.5 Conclusions
• Acknowledgments
• Abbreviations and Nomenclature
• References
• 2. Readying Medical Applications for Telehealth - I-Hen Tsai
• 2.1 Introduction
• 2.2 History and Recent Developments in Telehealth and mHealth
• 2.2.1 History of Telehealth and mHealth
• 2.2.2 Recent Developments
• 2.3 Present Challenges and Benefits
• 2.3.1 Deployment Problems
• 2.3.2 Technical Challenges
• 2.3.3 Handling Data and Privacy
• 2.4 Groundwork for a Good Telehealth Application
• 2.4.1 Communicating and Understanding Needs
• 2.4.2 Build on Familiar User Experiences
• 2.5 Enabling Telehealth for Your Existing Medical Application
• 2.6 Case Study—Panic Disorder Self-Therapy System
• 2.7 Case Study—Diabetes Telehealth Framework
• 2.8 Case Study—Telehealth Support for Unit Care
• 2.9 Conclusions
• Acknowledgments
• References
• 3. Virtual Hospitals: Integration of Telemedicine, Healthcare Services, and Cloud Computing - Shaftab Ahmed and M. Yasin Akhtar Raja
• 3.1 Introduction
• 3.2 Related Work
• 3.3 Service Integration in Virtual Hospitals
• 3.3.1 Social Networking
• 3.3.2 Data Accessibility in the Cloud
• 3.3.2.1 Telemedicine for Ubiquitous Healthcare
• 3.3.3 Data Acquisition, Transmission, and Archiving
• 3.3.4 Work-Flow and Decision-Support Systems in Cloud Architecture
• 3.3.5 Agent-Based Proactive Study and Knowledge-Based Management Systems (KBMSs)
• 3.3.5.1 Agent-Based Proactive Study
• 3.3.5.2 Knowledge-Based Management Systems
• 3.3.6 Disaster Management and Emergency Response Services
• 3.3.7 Recovery Rehabilitation and Medical Tourism
• 3.4 Medical Data Storage and Presentation Standards
• 3.5 Nursing Stations for Remote Patient Monitoring
• 3.6 Cybersecurity Issues
• 3.6.1 Hierarchical Security Management
• 3.6.2 Root-Level Security for Physical Identity-Based Accessibility
• 3.6.3 Proposed Trusted Computing Protocol
• 3.6.4 Executing Client Application in Secure Environment
• 3.7 Conclusion and Discussion
• List of Acronyms and Abbreviations
• References
• 4. Intelligent Electronic Health Systems - David A. Clifton, Marco A. F. Pimentel, Katherine E. Niehaus, Lei Clifton, Timothy E. A. Peto, Derrick W. Crook, and Peter J. Watkinson
• 4.1 Introduction
• 4.1.1 Objectives
• 4.1.2 Themes Considered in This Chapter
• 4.2 Theme I: Using the Broad Range of Data Sets within the EHR
• 4.2.1 Case Study: Prediction of Bacterial Drug Susceptibility
• 4.2.2 Features
• 4.2.3 Supervised Learning Algorithms for the EHR
• 4.2.4 Feature Selection
• 4.2.5 Generalization
• 4.2.6 Summary of Theme I
• 4.3 Theme II: Augmenting the EHR with Sensor Data
• 4.3.1 Case Study: Early-Warning Systems
• 4.3.2 Estimating Vital Signs with Probabilistic Models
• 4.3.3 Learning Data Trajectories
• 4.3.4 Similarity between Vital-Signs Trajectories
• 4.3.5 Summary of Theme II
• 4.4 Theme III: EHRs in the Developing World
• 4.4.1 Fusing Data from Noisy Time Series
• 4.5 Conclusions and Future Directions
• Acknowledgments
• References
• 5. Wearable Biomedical Systems and mHealth - Sungmee Park and Sundaresan Jayaraman
• 5.1 Introduction
• 5.1.1 The Healthcare Reality
• 5.1.2 Technology, Innovation, and the Emergence of the “Patient-Consumer”
• 5.1.3 The Healthcare Bill of Rights
• 5.1.4 mHealth: Key to Enhancing Quality of Life
• 5.1.4.1 The Desired State
• 5.1.4.2 Organization of the Chapter
• 5.2 The Healthcare Delivery Model and Its Transformation
• 5.2.1 The Enablers in the Healthcare Continuum
• 5.2.2 Transformation of Healthcare
• 5.2.3 The Attributes of Patient-Centric Value-Based Care
• 5.2.4 Data-Value Transformation and mHealth
• 5.3 Big Data, mHealth, and Emerging Trends in Healthcare
• 5.3.1 mHealth and the Patient Protection and Affordable Care Act
• 5.3.1.1 Accountable Care Organization
• 5.3.1.2 Bundled Payments for Care Improvement Initiative
• 5.3.1.3 Evidence-Based Medicine
• 5.3.2 Occupational Health and mHealth
• 5.3.2.1 Role of mHealth
• 5.3.3 The Value of mHealth
• 5.3.4 The Grand Challenge: Harnessing Big Data for mHealth
• 5.4 The WOW
• 5.4.1 The Value Proposition for Wearables for mHealth
• 5.4.2 The Metawearable Paradigm for mHealth
• 5.4.3 Textiles as a Metawearable
• 5.5 The Wearable Motherboard or Smart Shirt
• 5.5.1 The Wearable Motherboard Architecture
• 5.5.2 Testing of the Smart Shirt
• 5.5.3 Realizing mHealth through the Wearable Motherboard
• 5.5.3.1 Another Example
• 5.6 Looking Ahead: WOW and the Future of mHealth
• 5.6.1 The Chain Reaction
• 5.7 Conclusions
• Acknowledgments
• References
• 6. Wireless Instrumentation and Biomedical Applications - João Paulo Carmo and José Higino Correia
• 6.1 Introduction
• 6.2 Measurement Systems
• 6.2.1 Multiplexing Structures
• 6.2.2 Wireless Instruments Seen from the Communication Protocol Point of View
• 6.3 Technology for Wireless Systems
• 6.3.1 Operational Issues
• 6.3.2 RF Interfaces
• 6.4 Networks of Wireless Instruments
• 6.5 Examples of Wireless Instruments in Biomedical Applications
• 6.5.1 Commercial Off-the-Shelf (COTS) and Customized Applications
• 6.5.2 Active Concepts for Biomedical Wireless Instruments
• References
• 7. Context-Aware Biomedical Smart Systems - François Philipp and Manfred Glesner
• 7.1 Introduction
• 7.2 Biomedical Smart Systems
• 7.2.1 Architecture
• 7.2.2 Applications of Smart Systems in Healthcare
• 7.2.3 Microelectronic Technologies Enabling Smart Systems
• 7.2.4 Considerations on Hardware-Software Codesign
• 7.3 Building a Smart System for Activity Tracking
• 7.3.1 Context Sensing
• 7.3.2 Implementation
• 7.4 Development Tools for Smart Systems
• 7.5 Conclusion
• References
• Further Reading
• Section II Wireless Technologies and Networks
• 8. Technologies for mHealth - Jinman Kim, Christopher Lemon, Tanya Baldacchino, Mohamed Khadra, and Dagan (David) Feng
• 8.1 Introduction to mHealth
• 8.2 mHealth Technologies
• 8.2.1 Mobile Devices
• 8.2.2 Networks
• 8.2.3 Health Information (Data) Exchange
• 8.3 User Perspective and Usability
• 8.3.1 User Perspective of mHealth
• 8.3.2 mHealth Usability
• 8.4 mHealth Implementation
• 8.5 mHealth Case Studies
• 8.5.1 Outreach Mobile Nursing
• 8.5.2 mHealth Imaging
• 8.5.3 mHealth Applications
• Acknowledgments
• References
• 9. Wireless Body Area Network Protocols - Majid Nabi, Twan Basten, and Marc Geilen
• 9.1 Introduction
• 9.2 WBAN Characteristics
• 9.2.1 Body Sensor Devices
• 9.2.2 Energy Consumption Constraints
• 9.2.3 Quality of Wireless Links
• 9.2.4 Mobility
• 9.2.5 Heterogeneity
• 9.2.6 Network Scale
• 9.3 WBAN Architectures
• 9.3.1 Preliminaries
• 9.3.2 Star Architecture
• 9.3.3 Multihop Communication
• 9.3.4 Protocol Stacks for WBANs
• 9.4 MAC Mechanisms
• 9.4.1 MAC Paradigms
• 9.4.2 Low-Duty-Cycle TDMA-Based MAC
• 9.4.3 Gossiping TDMA-Based MAC
• 9.4.4 Battery-Aware MAC
• 9.4.5 H-MAC: Heartbeat-Driven Medium Access Control
• 9.4.6 BANMAC: Body Area Network Medium Access Control
• 9.5 Communication Standards
• 9.5.1 IEEE 802.15.4 LR-WPAN Standard
• 9.5.2 IEEE 802.15.6 WBAN Standard
• 9.6 Conclusions
• References
• 10. Wireless Body Area Network Data Delivery - Majid Nabi, Marc Geilen, and Twan Basten
• 10.1 Introduction
• 10.2 Data Delivery Requirements in WBANs
• 10.2.1 Network Organization for Data Delivery
• 10.2.2 Data Generation and Transmission
• 10.2.3 Data Delivery Requirements
• 10.3 WBAN Adaptation for Efficient Data Delivery
• 10.3.1 WBAN Adaptation
• 10.3.2 Link Quality Estimation for WBANs
• 10.4 Mechanisms for Intra-WBAN Data Delivery
• 10.4.1 Location-Based Data Forwarding
• 10.4.2 Tree-Based Routing
• 10.4.3 Probabilistic Routing
• 10.4.4 Gossip-Based Data Forwarding
• 10.4.5 On-Demand Data Forwarding
• 10.5 Prioritized WBAN Data Delivery
• 10.6 Conclusions
• References
• 11. Use of Small-Cell Technologies for Telemedicine - Edward Mutafungwa and Jyri Hämäläinen
• 11.1 Introduction
• 11.1.1 Background and Motivation
• 11.1.2 Toward Connected Personal Health Systems
• 11.1.3 Mobile Technologies for Personal Health Systems
• 11.1.4 Scope and Organization of the Chapter
• 11.2 Background on Personal Health Systems
• 11.2.1 Revisiting Connectivity Needs for Personal Health Systems
• 11.2.2 General System Requirements for Connectivity Providers
• 11.2.3 Achieving Interoperability for Personal Health Systems
• 11.2.3.1 Continua Reference Architecture
• 11.2.3.2 IEEE 11073 Device Specializations
• 11.2.4 mHealth Developments within Mobile Technology Standardization
• 11.3 Overview of Small-Cell Technologies
• 11.3.1 Drivers for Small-Cell Deployment
• 11.3.2 Small-Cell Architectures
• 11.3.2.1 Universal Terrestrial Radio Access Network (UTRAN) Architecture
• 11.3.2.2 Evolved Universal Terrestrial Radio Access Network (E-UTRAN) Architecture
• 11.3.3 Access Control Mechanisms
• 11.4 Small Cells for Personal Health Systems
• 11.4.1 Brief Review of Existing Personal Health Gateway Approaches
• 11.4.2 General End-to-End Implementation
• 11.4.3 Benefits of Small Cells for Personal Health Systems
• 11.4.3.1 QoS, Charging, and Policy Control
• 11.4.3.2 Carrier-Grade Security
• 11.4.3.3 Indoor Coverage and Capacity Enhancements
• 11.4.3.4 Improved Device Energy Efficiency
• 11.4.3.5 Small-Cell Services Development
• 11.4.3.6 Mobile Traffic Offloading
• 11.5 Simulation Case Study
• 11.5.1 System Model
• 11.5.2 System Simulation Assumptions
• 11.5.3 Results and Discussions
• 11.6 Conclusions
• Abbreviations
• Acknowledgments
• References
• 12. Dynamic Coexistence of Wireless Body Area Networks - Mohammad N. Deylami and Emil Jovanov
• 12.1 Introduction
• 12.2 WBAN Architecture
• 12.2.1 Coexistence of WBANs
• 12.3 Wireless Transmissions and Interference
• 12.4 Methods for Coexistence Management
• 12.5 Support for Coexistence in Different Wireless Standards
• 12.5.1 The IEEE 802.11 (Wi-Fi)
• 12.5.2 The IEEE 802.15.1 (Bluetooth)
• 12.5.3 The IEEE 802.15.3 (Ultrawideband or UWB)
• 12.5.4 The IEEE 802.15.4 (ZigBee)
• 12.5.5 The IEEE 802.15.6
• 12.5.6 Proprietary Wireless Technologies
• 12.6 Conclusion
• References
• 13. Mobile Health and Smartphone Platforms: A Case Study - M. B. Srinivas
• 13.1 Introduction
• 13.2 Mobile Health
• 13.2.1 Smartphone Platforms
• 13.3 Mobile Operating Systems and Application Development
• 13.3.1 Development of a Prototype Mobile Health Application
• 13.3.1.1 The Hardware
• 13.3.1.2 The Software
• 13.4 Results and Implementation
• 13.5 Conclusions
• Acknowledgment
• References
• Section III Sensors, Devices, Implantables, and Signal Processing
• 14. Medical Sensors for Mobile Communication Devices - Jacob Fraden
• 14.1 Introduction
• 14.2 Requirements for MCD Sensors
• 14.3 Integration of Sensors
• 14.4 Chemical and Bacteriological Sensing
• 14.5 Blood Pressure Monitoring
• 14.6 Energy Harvesting
• References
• 15. Development of Disposable Adhesive Wearable Human-Monitoring System - Alex Chun Kit Chan, Kohei Higuchi, and Kazusuke Maenaka
• 15.1 Introduction
• 15.2 Human Activity-Monitoring System
• 15.3 System Concept
• 15.4 Concept of Adhesive-Type Wearable Sensing Device
• 15.4.1 Hybrid Implementation Model
• 15.4.2 CMOS System-on-Chip
• 15.4.2.1 Sensor Interface and ADC
• 15.4.2.2 Digital Core and MCU
• 15.4.2.3 Wishbone
• 15.4.2.4 RF Interface (400 MHz/1.2 GHz)
• 15.4.3 Energy Harvesting
• 15.4.3.1 Solar Energy
• 15.4.3.2 Electret-Based Energy Harvesting
• 15.4.3.3 Electromagnetic Energy Harvesting
• 15.4.3.4 Thermal Energy Harvesting
• 15.4.4 MEMS Technology and Single-Chip Multisensor Integration
• 15.4.4.1 Sensor Integration
• 15.4.4.2 Starting Wafer: Silicon-on-Honeycomb Insulator Wafer with Silicon-on-Nothing Machining for Pressure Sensor
• 15.4.4.3 Dynamic Acceleration Sensor: Lead Zirconate Titanate as Detection Mechanism
• 15.4.4.4 Static Acceleration Sensor: Capacitive-Type 3D Acceleration Sensor
• 15.4.4.5 Blood Pulse/SpO2 Sensor 324
• 15.4.4.6 Other Sensors and Technologies
• 15.4.5 Wireless—A 315 MHz RF Transceiver Module
• 15.4.6 The Software
• 15.5 Large Module Adhesive-Type Wearable Sensing Device
• 15.6 Conclusion
• Acknowledgments
• References
• 16. Drug-Delivery Systems in eMedicine and mHealth - Arni Ariani, Soegijardjo Soegijoko, and Hermawan Nagar Rasyid
• 16.1 Introduction
• 16.2 Implantable Drug-Delivery Systems (IDDSs)
• 16.2.1 Implantable Pump Systems
• 16.2.1.1 Osmotic Pumps
• 16.2.1.2 Infusion Pumps
• 16.2.2 Micro-/Nanofabricated IDDSs
• 16.2.3 Implantable Microfluidic Devices
• 16.2.4 Ceramic Drug-Delivery Systems (CDDSs)
• 16.2.5 PMMA Beads
• 16.2.6 Discussion
• 16.3 Dermal Drug-Delivery Systems
• 16.3.1 Microneedle Syringes
• 16.3.2 Microneedle Patches
• 16.3.3 Discussion
• 16.3.3.1 Pain
• 16.3.3.2 The Irritation of the Skin
• 16.3.3.3 The Infection of the Skin
• 16.4 Human Body Modeling for Study of EMF Interaction
• 16.5 Power
• 16.6 Data Telemetry
• 16.6.1 Near-Field Resonant Inductive Coupling
• 16.6.2 Wireless Communication
• 16.7 mHealth Solutions for Drug-Delivery System
• 16.7.1 Automatic Drug-Delivery System
• 16.7.2 Disposable Patch Pump
• 16.7.3 Wireless Drug Dosage Monitoring
• 16.8 Conclusion
• References
• 17. Implantable Systems - Vincenzo Luciano, Emilio Sardini, Alessandro Dionisi, Mauro Serpelloni, and Andrea Cadei
• 17.1 Introduction
• 17.2 Implantable System Architecture
• 17.2.1 Telemetric Systems
• 17.2.2 Power-Harvesting Systems
• 17.3 Force Measurement inside Knee Prosthesis
• 17.3.1 Experimental Results
• 17.4 Power Harvesting in Implantable Human Total Knee Prosthesis
• 17.5 Conclusions
• References
• 18. Signal Processing in Implantable Neural Recording Microsystems - Sedigheh Razmpour, Mohammad Ali Shaeri, Hossein Hosseini-Nejad, and Amir M. Sodagar
• 18.1 Neurophysiological Background
• 18.1.1 Introduction
• 18.1.2 The Intracellular Neural Signal
• 18.1.2.1 Resting Potential
• 18.1.2.2 Action Potential
• 18.1.3 The Extracellular Neural Signal
• 18.2 Neural Recording Microsystems
• 18.2.1 Neural Recording Approaches
• 18.2.2 Neural Recording Microsystems
• 18.2.2.1 Recording Front-End
• 18.2.2.2 Wireless Interfacing Module
• 18.2.2.3 Neural Processing Module
• 18.3 High-Density Neural Recording
• 18.3.1 System-Level Approaches
• 18.3.2 Signal-Level Approaches
• 18.4 Neural Signal Processing in the Time Domain
• 18.4.1 Spike Detection
• 18.4.2 Spike Extraction
• 18.4.3 Feature Extraction and Spike Sorting
• 18.4.4 Delta Compression
• 18.4.5 Nonlinear Quantization
• 18.5 Transform-Based Neural Signal Compression
• 18.5.1 Performance Measures
• 18.5.2 The Discrete Wavelet Transform
• 18.5.3 The Walsh-Hadamard Transform
• 18.5.4 The Discrete Cosine Transform
• 18.6 Data Framing
• 18.7 Concluding Remarks
• Acknowledgments
• References
• 19. Electronic Health Signal Processing - Mohit Kumar, Norbert Stoll, Kerstin Thurow, and Regina Stoll
• 19.1 Introduction
• 19.2 Background
• 19.2.1 A Takagi-Sugeno Fuzzy Filter
• 19.2.1.1 Triangular Membership Functions
• 19.2.2 Stochastic Fuzzy Modeling of Biomedical Signals
• 19.2.3 A Stochastic Mixture of Signal Data Fuzzy Models
• 19.3 Variational Bayesian Inference of Stochastic Mixture of Signal Data Models
• 19.3.1 Optimization with respect to q(π)
• 19.3.2 Optimization with respect to q(αi)
• 19.3.3 Optimization with respect to q(φi)
• 19.3.4 Optimization with respect to q(s)
• 19.3.5 Summary
• 19.4 A Case Study
• 19.5 Concluding Remarks
• References
• Section IV Implementation of eMedicine and Telemedicine
• 20. Telecardiology - Rajarshi Gupta
• 20.1 Introduction
• 20.2 Components of a Telecardiology System and Functioning
• 20.3 Cardiovascular Signal Acquisition
• 20.4 Signal Compression in Telecardiology
• 20.5 Role of Information and Communication Technology in Telecardiology
• 20.6 Electronic Health Records, Medical Information System, and Interface with Medical Professionals
• 20.7 Data Security and Privacy Issues in Telecardiology
• 20.8 Medical Signal Analysis in Telecardiology Systems
• 20.9 Trends in Telecardiology Systems
• Acknowledgments
• References
• Further Reading
• 21. Telecardiology Tools and Devices - Axel Müller, Jörg Otto Schwab, Christian Zugck, Johannes Schweizer, and Thomas M. Helms
• 21.1 Introduction
• 21.2 Telemedical ECG Monitoring
• 21.3 Telemonitoring in Patients with CIEDs
• 21.3.1 Introduction
• 21.3.2 Telemonitoring Methods in Patients with CIEDs
• 21.3.3 Clinical Data for Telemedical Monitoring of Patients with CIEDs
• 21.4 Telemonitoring in Patients with Chronic Heart Failure
• 21.4.1 Goals of Telemonitoring in Patients with Chronic Heart Failure
• 21.4.2 Telemonitoring Method in Patients with Chronic Heart Failure
• 21.4.2.1 Support Concept with Heart Failure Nurses
• 21.4.2.2 Care Concepts with Telemonitoring
• 21.4.3 Current Study Status
• 21.5 Telemonitoring in Patients with Arterial Hypertension
• 21.6 Conclusions
• References
• 22. Teleradiology - Liam Caffery
• 22.1 Introduction
• 22.2 Background
• 22.2.1 Definitions
• 22.2.2 Clinical Teleradiology
• 22.2.3 Technology Considerations
• 22.2.3.1 DICOM
• 22.3 The Commoditization War
• 22.4 Mobile Teleradiology
• 22.4.1 Digital Image Fundamentals
• 22.4.2 Monitor Characteristics
• 22.4.3 Workstation Characteristics
• 22.5 Teleradiology on Handheld Devices
• 22.5.1 Handheld Device Characteristics
• 22.5.1.1 Hardware
• 22.5.2 Clinical Efficacy
• 22.6 Conclusions
• Abbreviations and Glossary
• References
• Further Reading
• Technical Guidelines
• 23. Teledermatology - Soegijardjo Soegijoko, Arni Ariani, and Sugiyantini
• 23.1 Introduction
• 23.2 Structure of the Skin
• 23.3 Common Skin Diseases
• 23.3.1 Eczema
• 23.3.2 Fungal/Yeast Infections
• 23.3.3 Bacterial Infections
• 23.3.4 Viral Infections
• 23.3.5 Parasitic Infections
• 23.3.6 Autoimmune Disease
• 23.3.7 Miscellaneous Skin Diseases
• 23.4 Teledermatology Models
• 23.4.1 Store-and-Forward Teledermatology
• 23.4.2 Live-Interactive Teledermatology
• 23.4.3 Hybrid Teledermatology
• 23.5 The Implementation of Teledermatology Models (Existing Applications)
• 23.6 Technical Aspects
• 23.6.1 Acquisition of Images
• 23.6.1.1 Digital Camera
• 23.6.1.2 Digital Video Camera
• 23.6.2 Data Display
• 23.6.3 Data Storage, Retrieval, and Transmission
• 23.7 Teledermatology Evaluation
• 23.7.1 Diagnostic Reliability
• 23.7.2 Diagnostic Accuracy
• 23.7.3 Outcomes
• 23.7.4 Cost Effectiveness
• 23.7.5 End Users'Satisfaction
• 23.7.5.1 Patients'Satisfaction
• 23.7.5.2 Referring Clinicians’ Satisfaction
• 23.7.5.3 Consultants’ Satisfaction
• 23.8 Future Developments
• References
• 24. Teleaudiology - Robert H. Eikelboom and De Wet Swanepoel
• 24.1 Introduction
• 24.1.1 The Demand for Hearing Health Services
• 24.1.2 Prevalence of Hearing Loss and Incidence of Ear Disease
• 24.1.3 Number and Distribution of Ear and Hearing Health Professionals
• 24.1.4 The Role of Telehealth in Audiology
• 24.2 Synchronous and Asynchronous Teleaudiology
• 24.3 Requirements for Providing Primary through Tertiary Ear and Hearing Health Services
• 24.3.1 Barriers to Success and Planning for Success
• 24.3.2 Local Personnel
• 24.3.3 Training and Support
• 24.3.4 Equipment
• 24.3.5 Protocols and Referral Pathway
• 24.4 Teleaudiology Functions
• 24.4.1 Screening
• 24.4.2 Diagnosis
• 24.4.3 Case History
• 24.4.4 Otoscopy and Video Otoscopy
• 24.4.4.1 Video Otoscope Selection
• 24.4.5 Tympanometry
• 24.4.5.1 Tympanometer Selection
• 24.4.6 Audiometry
• 24.4.6.1 Audiometer Selection
• 24.4.7 Speech Audiometry
• 24.4.8 Auditory Evoked Responses
• 24.4.9 Intraoperative Monitoring
• 24.4.10 Balance Assessment
• 24.4.11 Intervention
• 24.4.11.1 Counseling
• 24.4.11.2 Hearing Aids and Assistive Devices
• 24.4.11.3 Hearing Implants
• 24.4.12 Continued Professional Education
• 24.5 Case Studies of Teleaudiology
• 24.5.1 Teleaudiology in Witkoppen, South Africa
• 24.5.2 Teleotology in Alaska
• 24.5.3 Telepractice between Sydney and Western Samoa
• 24.6 Conclusions
• Acknowledgment
• Partial List of Manufacturers and Suppliers
• References
• 25. Teleoncology - Natalie K. Bradford and Helen Irving
• 25.1 Introduction
• 25.2 Oncology Overview
• 25.3 Teleoncology
• 25.3.1 Rationale for Teleoncology
• 25.4 Telecommunications and Models of Cancer Care
• 25.5 Traditional Models of Cancer Care for Regional or Remote Locations
• 25.6 Potential Benefits and Disadvantages of Teleoncology
• 25.7 Teleoncology and Cancer Prevention
• 25.8 Teleoncology and Cancer Diagnosis
• 25.9 Teleoncology and Cancer Treatment
• 25.9.1 Teleconsultation
• 25.9.2 Virtual Multidisciplinary Team Meetings
• 25.9.3 Clinical Trials
• 25.9.4 Integrated Decision-Support Systems
• 25.9.5 Telesurgery
• 25.9.6 Teleradiotherapy
• 25.10 Supportive Care
• 25.10.1 Discharge Planning
• 25.10.2 Access to Allied Health
• 25.10.3 Palliative Care
• 25.11 Enhancing Cancer Care in Low- and Middle-Income Nations
• 25.12 Economics
• 25.13 Medicolegal Recommendations
• 25.14 Training and Education
• 25.15 Barriers to Progress
• 25.15.1 Human Factors
• 25.15.2 Education and Support
• 25.15.3 Costs
• 25.16 Conclusions
• Acknowledgment
• References
• 26. Telepathology System Development and Implementation - Ronald S. Weinstein
• 26.1 Introduction
• 26.1.1 Telepathology Systems
• 26.1.2 Telepathology System Development
• 26.2 Telepathology System Classifications
• 26.2.1 Human Pathology Telepathology System Classification (2001)
• 26.2.2 APMIS Telepathology System Classification (2012)
• 26.2.3 American Telemedicine Association Clinical Guidelines (2014)
• 26.3 Telepathology System Classification: 2014 Practitioners
• 26.4 Conclusions
• Partial List of Manufacturers and Suppliers
• Abbreviation and Nomenclature
• Acknowledgments
• References
• Further Information
• 27. Acute Care Telemedicine - Nigel R. Armfield and Tim Donovan
• 27.1 Introduction
• 27.2 Definitions
• 27.2.1 Telemedicine
• 27.2.2 Acute Care Telemedicine
• 27.3 Rationale for Telemedicine in Acute Care
• 27.3.1 Service Availability
• 27.3.2 Service Accessibility
• 27.3.3 Health System Responses to Access Impediments
• 27.3.4 Telemedicine as an Adjunct
• 27.4 Potential Benefits of Telemedicine
• 27.4.1 Patient and Families
• 27.4.2 Clinician
• 27.4.3 Health System
• 27.4.4 Summary
• 27.5 Domains of Acute Care and Telemedicine
• 27.5.1 Trauma Care and Acute Care Surgery
• 27.5.1.1 Case Example 1: Triage and Referral of Patients with Acute Burn Injuries
• 27.5.1.2 Case Example 2: Rural Acute Trauma Care
• 27.5.2 Emergency Care
• 27.5.2.1 Case Example 3: Thrombolysis for Acute Cerebrovascular Ischemia
• 27.5.2.2 Case Example 4: Telemedicine Diagnosis and Treatment of Acute Myocardial Infarction
• 27.5.3 Urgent Care
• 27.5.3.1 Case Example 5: Primary Care Pediatric Telemedicine Consultation for Acute Illness in Child-Care and School Settings
• 27.5.3.2 Case Example 6: Community-Based Ear, Nose, and Throat (ENT) Screening with Telemedicine-Based Review and Community-Based Surgical Outreach for Children at High Risk of Ear Disease
• 27.5.4 Short-Term Stabilization
• 27.5.4.1 Case Example 7: Ambulance Dispatchers Monitoring Simulated Cardiac Arrest Calls from an Untrained Bystander with either Mobile Phone Video or Standard Call Interaction; Video Input Improved Understanding of Rescuer and Facilitated Assistance to Bystander
• 27.5.4.2 Case Example 8: Qualitative Outcome Improved in Remote Canadian Trauma Patients with Stabilization Using Telesonography
• 27.5.5 Prehospital Care
• 27.5.5.1 Case Example 9: A Well-Designed Randomized Study of Mobile Stroke Assessment and Treatment (Including Telemedicine Use) on Time from Alarm to Therapy Decision in 100 Patients
• 27.5.5.2 Case Example 10: Cost Analysis of Remote ECG Reading by Telemedicine
• 27.5.6 Critical Care
• 27.5.6.1 Case Example 11: Advice and Retrieval Management for Critically Ill Infants
• 27.5.6.2 Case Example 12: Providing Remote Oversight of Intensive Care Units (e-ICU)
• 27.5.7 Summary
• 27.6 Conclusions
• Abbreviations and Nomenclature
• References
• 28. Monitoring for Elderly Care: The Role of Wearable Sensors in Fall Detection and Fall Prediction Research - Kejia Wang, Stephen J. Redmond, and Nigel H. Lovell
• 28.1 Introduction
• 28.2 Personal Alarms
• 28.3 Fall Detection and Activity Monitoring
• 28.3.1 Unobtrusive Sensors
• 28.3.2 Wearable Sensors
• 28.4 Prevention and Fall Risk Assessments
• 28.4.1 Sensor-Based Fall Prediction
• 28.5 Measurements and Sensors
• 28.5.1 Accelerometry
• 28.5.2 Gyroscopy
• 28.5.3 Other Sensors
• 28.6 Feature Extraction and Analysis
• 28.6.1 Features in Activity Monitoring
• 28.6.2 Features in Fall Risk Estimation and Fall Prediction
• 28.7 Fall Risk Model Training and Validation Strategies
• 28.7.1 Validation by Clinical Fall Risk Assessment Scores
• 28.7.2 Validation by Fall History
• 28.7.3 Fall Risk Estimation Using Sensors on Supervised Activities
• 28.7.4 Fall Risk Estimation Based on Activities of Daily Living
• 28.8 Statistical Heterogeneity and Diversity in the Field
• 28.8.1 Clinical Heterogeneity: Health Status, Ability, and Origin of Cohort
• 28.8.2 Methodological Heterogeneity: Subject Classes, Data Collection Techniques, Analysis Methods, and Models
• 28.8.3 Sample Sizes for Training and Validation
• 28.9 Fusing Fall Detection, Daily Monitoring, and Risk Assessments
• 28.10 Conclusion
• Abbreviations
• References
• VOLUME II
• Section I Medical Robotics, Telesurgery, and Image-Guided Surgery
• Medical Robotics - Giancarlo Ferrigno, Alessandra Pedrocchi, Elena De Momi, Emilia Ambrosini, and Elisa Beretta
• 1.1 Introduction to Medical Robotics
• 1.1.1 Definitions and Standards
• 1.1.2 Historical Perspective
• 1.2 Surgical Robots
• 1.2.1 General Requirements
• 1.2.2 Control
• 1.2.2.1 Position Control
• 1.2.2.2 Shared Control
• 1.2.2.3 Cooperative Control
• 1.2.2.4 Teleoperation
• 1.2.3 Recent Developments
• 1.3 Rehabilitation Robots
• 1.3.1 Introduction: Why Robots in Rehabilitation?
• 1.3.2 The Mechanical Design: Exoskeleton versus End-Effector Robots—Some Examples
• 1.3.3 The Problem of Control
• 1.3.4 Impact on Clinical Practice and First Evidence-Based Studies of Rehabilitation Robotics
• 1.3.5 Perspectives and Challenges
• 1.4 Assistive Robots
• 1.4.1 Introduction
• 1.4.2 Physical Assistance Robots
• 1.4.3 Mobility Aids
• 1.4.4 Activity of Daily Living Support
• 1.4.5 Future Perspectives
• References
• Modern Devices for Telesurgery - Florian Gosselin
• 2.1 Introduction and History
• 2.2 Main Components and Functionalities of a Robotic Telesurgery System
• 2.2.1 General Overview
• 2.2.2 Slave Surgery Robots
• 2.2.3 Master Control Station
• 2.2.4 Additional Equipment and Communication Means
• 2.2.5 Main Functionalities
• 2.2.5.1 Master-Slave Teleoperation
• 2.2.5.2 Motion (and Force) Scaling
• 2.2.5.3 Tremor Cancellation
• 2.2.5.4 Shared Control
• 2.2.5.5 Augmented Haptic Feedback
• 2.3 Optimal Design of an Advanced Input Device for Telesurgery
• 2.3.1 Design Guidelines
• 2.3.2 Design Methodology
• 2.3.3 Application to the Design of a Telesurgery Master Arm
• 2.4 Conclusion
• References
• Microsurgery Systems - Leonardo S. Mattos, Diego Pardo, Emidio Olivieri, Giacinto Barresi, Jesus Ortiz, Loris Fichera, Nikhil Deshpande, and Veronica Penza
• 3.1 Introduction
• 3.2 Clinical Applications
• 3.2.1 Pediatric and Fetal Surgery
• 3.2.2 Ophthalmology
• 3.2.3 Otolaryngology
• 3.2.4 Plastic Surgery
• 3.2.5 Nerve Surgery
• 3.2.6 Urology
• 3.3 Microsurgery Systems in Clinical Use
• 3.4 Robot-Assisted Microsurgery Systems
• 3.5 Current Challenges for Next-Generation Microsurgery Systems
• 3.5.1 Miniaturization
• 3.5.1.1 Materials and Robustness
• 3.5.1.2 Maneuverability
• 3.5.1.3 Sensing
• 3.5.1.4 Actuation
• 3.5.2 Microsurgical Tools
• 3.5.2.1 Sensing Tools
• 3.5.2.2 Actuation Tools
• 3.5.3 Visualization Methods and Systems
• 3.5.3.1 Visualization Devices
• 3.5.3.2 Augmented Reality
• 3.5.4 Haptic Feedback
• 3.5.5 Control Interfaces and Ergonomics
• 3.5.6 Surgical Planning
• 3.5.6.1 Preoperative Reconstruction
• 3.5.6.2 Intraoperative Registration
• 3.5.7 Safety
• 3.5.8 Autonomous Behaviors
• 3.6 Conclusion
• References
• Image-Guided Microsurgery - Tom Williamson, Marco Caversaccio, Stefan Weber, and Brett Bell
• 4.1 Introduction
• 4.1.1 What Is Image Guidance?
• 4.1.2 Why Image Guidance?
• 4.2 Image Guidance Components and Workflow
• 4.2.1 Image Acquisition
• 4.2.2 Surgical Planning
• 4.2.3 Registration
• 4.2.4 Tracking
• 4.2.5 Instrumentation and Instrument Guidance
• 4.2.6 Information Presentation
• 4.3 Image Guidance by Surgical Domain
• 4.3.1 Image Guidance in Otorhinolaryngology
• 4.3.2 Image Guidance in Neurosurgery
• 4.3.3 Image Guidance in Ophthalmic Surgery
• 4.3.4 Image Guidance in Other Surgeries
• 4.4 Conclusions
• References
• Section II Telenursing, Personalized Care, Patient Care, and eEmergency Systems
• eHealth and Telenursing - Sinclair Wynchank and Nathanael Sabbah
• 5.1 Introduction
• 5.2 How Telenursing Came About
• 5.3 Nursing's Applications of Information and Communication Technology
• 5.3.1 Computerized Decision-Support Systems
• 5.3.2 Databases
• 5.3.3 Telephony
• 5.3.4 Videoconferencing
• 5.3.5 mHealth
• 5.3.6 Telenursing Services
• 5.4 Telenursing's Healthcare Applications
• 5.4.1 Triage
• 5.4.2 Maternity and Pediatrics
• 5.4.3 Posthospitalization
• 5.4.4 Home Care
• 5.4.5 Chronic Illnesses
• 5.4.6 Human Immunodeficiency Virus/Acquired Immunodeficiency Syndrome
• 5.4.7 Mental Health
• 5.4.8 Geriatrics
• 5.5 Nurse Migration
• 5.6 Telenursing and Distance Education
• 5.6.1 Bases of e-learning
• 5.6.2 Educating Laypersons
• 5.6.3 Formal Instruction
• 5.6.4 Web-Based Education
• 5.6.5 Recent Trends
• 5.7 Telenursing and Ethical Questions
• 5.8 Discussion
• 5.9 Conclusions
• Abbreviations
• Nomenclature
• Acknowledgments
• References
• mHealth: Intelligent Closed-Loop Solutions for Personalized Healthcare - Carmen C. Y. Poon and Kevin K. F. Hung
• 6.1 Introduction
• 6.2 Historical Overview of mHealth
• 6.2.1 Evolution from Telemedicine to mHealth
• 6.2.2 Initial mHealth Applications
• 6.2.3 Recent mHealth Applications
• 6.3 Mobile Apps for mHealth
• 6.3.1 Overview of mHealth Apps
• 6.3.2 Regulation of mHealth Apps
• 6.4 Cloud Computing
• 6.4.1 Definitions
• 6.4.2 Selected Applications
• 6.5 Closed-Loop Solutions for Personalized Health Interventions
• 6.5.1 Challenges in Sensor Design and Fabrication
• 6.5.2 Challenges in Mining and Managing Big Health Data
• 6.6 Conclusions
• Abbreviations and Nomenclature
• Acknowledgments
• References
• Patient Care Sensing and Monitoring Systems - Akihiro Kajiwara and Ryohei Nakamura
• 7.1 Introduction
• 7.2 Stepped-Frequency Modulation Ultrawideband Scheme
• 7.2.1 Ultrawideband Impulse Radio Sensor
• 7.2.2 Stepped-Frequency Modulation Ultrawideband Sensor
• 7.2.3 Detect-and-Avoid and Spectrum Hole Technique
• 7.3 Detect-and-Avoid Technique
• 7.4 Patient Care Sensing and Monitoring System
• 7.4.1 Sensing and Monitoring Algorithm
• 7.4.2 Measurement Results
• 7.5 Conclusions
• References
• Mobile Health Sleep Technologies - Anda Baharav
• 8.1 Introduction
• 8.1.1 Background about Sleep
• 8.1.2 Sleep Problems and Their Implications
• 8.2 Sleep and Technology
• 8.2.1 The Role of Technology in General and Mobile Technology in Particular in Inducing Sleep Disorders
• 8.2.2 Why Mobile Interface Is Most Suitable for Sleep
• 8.3 Methods for Evaluating Sleep
• 8.3.1 Subjective Information and Questionnaires
• 8.3.2 Diaries
• 8.3.3 Gold-Standard Polysomnography
• 8.3.4 Electroencephalography
• 8.3.5 Heart-Rate Variability
• 8.3.6 Movement Actigraphy
• 8.3.7 Behavioral: Audio-Video Monitoring
• 8.4 Adding Treatment
• 8.5 Players on the Market
• 8.6 Advantages
• 8.7 Next Steps
• Abbreviations
• References
• Cardiovascular Disease Management via Electronic Health - Aimilia Gastounioti, Spyretta Golemati, Ioannis Andreadis, Vasileios Kolias, and Konstantina S. Nikita
• 9.1 Introduction
• 9.2 Computer-Aided Diagnosis
• 9.2.1 Analysis of Cardiovascular Signals and Images
• 9.2.2 Generating a Diagnostic Decision
• 9.3 Telehealth Systems
• 9.4 Mobile Applications
• 9.5 Web-Based Telemedicine
• 9.6 Semantic Interoperability and Ontologies
• 9.7 Future Trends
• 9.8 Conclusions
• References
• mHealth eEmergency Systems - Efthyvoulos Kyriacou, Andreas Panayides, and Panayiotis Constantinides
• 10.1 Introduction
• 10.2 Enabling Technologies
• 10.2.1 Wireless Transmission Technologies
• 10.2.2 Mobile Computing Platforms
• 10.2.3 Biosignals
• 10.2.4 Transmission of Digital Images
• 10.2.5 Transmission of Digital Video
• 10.3 Protocols and Processes for eEmergency Management and Response
• 10.3.1 Emergency Management and Response: The Challenge of Coordination
• 10.3.2 Computer-Aided Medical Dispatch Systems
• 10.4 mHealth eEmergency Systems
• 10.4.1 Overview
• 10.4.2 Case Studies
• 10.4.2.1 Case Study 1: Emergency Telemedicine—The AMBULANCE and Emergency 112 Projects
• 10.4.2.2 Case Study 2: Diagnostically Robust Ultrasound Video Transmission over Emerging Wireless Networks
• References
• Section III Networks and Databases, Informatics, Record Management, Education, and Training
• Global and Local Health Information, Databases, and Networks - Kostas Giokas, Yiannis Makris, Anna Paidi, Marios Prasinos, Dimitra Iliopoulou, and Dimitris Koutsouris
• 11.1 Introduction
• 11.2 Local Health Data
• 11.2.1 Collection of Local Health Data
• 11.2.2 Warehousing of Local Health Data
• 11.2.3 Analysis of Local Health Data
• 11.2.4 Local Health Data Network
• 11.2.5 Challenges and Inefficiencies Associated with a Local Health Data Network
• 11.2.5.1 Data Complexity and Integration
• 11.2.5.2 Privacy, Security, and Patients’ Consent
• 11.3 Databases
• 11.3.1 Introduction
• 11.3.2 Database Architectures
• 11.3.2.1 Traditional Architectures
• 11.3.2.2 Server System Architectures
• 11.3.3 Parallel Systems
• 11.3.4 Distributed Systems
• 11.4 Database System Concepts in Healthcare
• 11.4.1 World Health Organization Classifications
• 11.4.2 General Online Health Databases
• 11.4.2.1 European Health for All Database
• 11.4.2.2 The National Institutes of Health Intramural Database
• 11.4.2.3 Other European Online Health Databases
• 11.5 Data Curation
• 11.6 Interpretation of Health and Epidemiological Data—Biostatistics
• 11.7 Global Health Data Management and Interpretation
• References
• Electronic Medical Records: Management and Implementation - Liping Liu
• 12.1 Introduction
• 12.2 Detailed Functional and Data Requirements
• 12.2.1 Functional Requirements
• 12.2.2 Data Requirements
• 12.3 Implementation Issues and Solutions
• 12.3.1 Implementation Issues
• 12.3.2 Technological Solutions
• 12.4 An Integrated e-Service Framework
• 12.4.1 Justifications
• 12.4.2 Implementation
• 12.5 Conclusions
• References
• Public Health Informatics in Australia and around the World - Kathleen Gray and Fernando Martin Sanchez
• 13.1 Introduction
• 13.1.1 Information and Communication in Public Health
• 13.1.2 Evolution of Public Health Informatics
• 13.1.3 Key Concepts in Public Health Informatics
• 13.1.3.1 Data Management in Public Health Informatics
• 13.1.3.2 Information Management in Public Health Informatics
• 13.1.3.3 Knowledge Management in Public Health Informatics
• 13.2 Public Health Informatics in Australia
• 13.2.1 Australia's Public Health
• 13.2.2 Australian National Public Health Information Infrastructure
• 13.2.3 Australian State and Territory Public Health Informatics Strategies
• 13.2.4 Australian Local Government Public Health Informatics Initiatives
• 13.2.4.1 Systematizing Data for Child and Family Nursing
• 13.2.4.2 Immediate Information for Disaster Management
• 13.2.4.3 Knowledge Translation for Obesity Prevention
• 13.2.5 The Discipline and Profession of Public Health Informatics in Australia
• 13.3 Current International Perspectives on Public Health Informatics
• 13.3.1 Biosurveillance Methods in England and Wales
• 13.3.2 Assessment of European Community Health Indicators
• 13.3.3 Data Use Workshops in Tanzania
• 13.3.4 The Impact of Technology on Sub-Saharan Hospitals
• 13.4 Directions for Public Health Informatics
• 13.4.1 Public Health 2.0
• 13.4.2 Bidirectional Communication
• 13.4.3 Exposome Informatics
• 13.4.4 Advancing the Agenda for Public Health Informatics
• 13.5 Conclusions
• References
• Ubiquitous Personal Health Records for Remote Regions - H. Lee Seldon, Jacey-Lynn Minoi, Mahmud Ahsan, and Ali Abdulwahab A. Al-Habsi
• 14.1 Introduction
• 14.1.1 Scope
• 14.2 Personal Health Record Data Collection and Storage: Ubiquitous or Not?
• 14.2.1 Constraints in Remote Regions: Healthcare Workers and the Web Are Not Ubiquitous
• 14.2.2 Personal Health Record on Paper: Not Quite Ubiquitous
• 14.2.3 Web-Based Personal Health Records
• 14.2.3.1 Purely Web-Based Personal Health Records Are Not Quite Ubiquitous
• 14.2.3.2 Web-Based Personal Health Records with Various Inputs and Outputs
• 14.2.4 Personal Health Records on Stand-Alone Mobile Devices: Not Quite Ubiquitous
• 14.2.4.1 Diabetes or Hypertension Management
• 14.2.4.2 Diet and Exercise
• 14.2.4.3 Personal Health Records
• 14.2.5 Personal Health Records on Connected Devices: Maybe Ubiquitous
• 14.2.5.1 OpenMRS, OpenRosa, JavaRosa, and Sana
• 14.2.5.2 EPI Life and EPI Mini
• 14.3 A Ubiquitous Personal Health Record for Remote Regions Must Involve Individuals and Include Phone-Based Records
• 14.3.1 Examples: Portable Personal Health Records
• 14.3.1.1 Portable Personal Health Records: Mobile Records
• 14.3.1.2 Portable Personal Health Records: Web-Based Records
• 14.3.1.3 Portable Personal Health Records: Ubiquitous Communications
• 14.4 Contextual Information: The Icing on the Personal Health Records
• 14.4.1 Contextual Information on the Web
• 14.4.2 Retrieval of Contextual Information
• 14.4.3 Integration into a Ubiquitous Personal Health Record System
• 14.5 Conclusions
• References
• Education and Training for Supporting General Practitioners in the Use of Clinical Telehealth: A Needs Analysis - Sisira Edirippulige, Nigel R. Armfield, Liam Caffery, and Anthony C. Smith
• 15.1 Introduction
• 15.2 Methods
• 15.2.1 Participants
• 15.2.2 Survey Questions
• 15.2.3 Analysis
• 15.2.4 Ethics
• 15.3 Results
• 15.3.1 Characteristics of the Responding Practices
• 15.3.2 Current and Planned Use of Telehealth
• 15.3.3 Education and Training
• 15.4 Discussions
• 15.4.1 Telehealth Training
• 15.5 Conclusions
• References
• Further Reading
• Section IV Business Opportunities, Management and Services, and Web Applications
• Delivering eHealthcare: Opportunities and Challenges - Deborah A. Helman, Eric J. Addeo, N. Iwan Santoso, David W. Walters, and Guy T. Helman
• 16.1 Introduction
• 16.2 Context: The Evolution of eHealth
• 16.2.1 The Multidimensional Landscape of Healthcare Delivery: Associated Driving Forces
• 16.2.2 Feasible Models
• 16.2.3 Physicians’ and Patients’ Resistance and Readiness
• 16.3 Delivering eHealthcare: Practical Applications
• 16.3.1 Children's National Medical Center: Specialized Services
• 16.3.2 Kaiser Permanente: A Healthcare System
• 16.3.3 Misfit Wearables: Start-Ups
• 16.4 The Value Chain Network Business Model
• 16.4.1 Value, Value Drivers, and Value Propositions
• 16.4.2 Identifying Value Drivers
• 16.4.3 The Value Proposition
• 16.4.4 Managing in the Value Chain Network
• 16.4.5 Value and the Value Chain
• 16.4.6 Applying the e-Value Chain to Healthcare
• 16.5 Value Drivers and Value-Led Productivity: A Network Perspective
• 16.5.1 Value Drivers as Network Performance Drivers
• 16.5.2 Value Drivers as Productivity Components
• 16.5.3 Assessing the Productivity and Competitive Advantage of the Value Proposition
• 16.6 Technology Perspective
• 16.6.1 Cloud Computing
• 16.6.2 Smart Healthcare Personal Assistants
• 16.6.3 Security and Privacy in eHealth
• 16.7 Conclusions and Future Challenges
• Abbreviations
• References
• Mobile Healthcare User Interface Design Application Strategies - Ann L. Fruhling, Sharmila Raman, and Scott McGrath
• 17.1 Introduction
• 17.2 Mobile Healthcare App User Interface Design Strategies
• 17.2.1 Focusing on Essential Functions in the Mobile Environment
• 17.2.2 Ease of Use
• 17.2.3 Intuitive Interaction
• 17.2.4 Consistency within a Family of Applications
• 17.2.5 Matching Routine Work Flow
• 17.2.6 Limiting Menu/Layer Display Structure
• 17.2.7 Minimalist Aesthetics
• 17.2.7.1 Log-In/Log-Out Guidelines
• 17.2.8 Leveraging Agile Development Practices
• 17.3 Using Mobile Device Simulators for Testing
• 17.3.1 Aiming for Quick Response Time
• 17.3.2 Physical Device Selection Considerations
• 17.4 Example of Applying Healthcare Mobile Development Strategies
• 17.4.1 Public Health Mobile Application Background
• 17.4.2 Mobile Solution
• 17.4.3 Mobile User Interface and System Guidelines
• 17.4.4 Mobile Thin Client
• 17.4.4.1 STATPack Mobile Implementation
• 17.5 Conclusion
• Acknowledgments
• References
• Epidemic Tracking and Disease Monitoring in Rural Areas: A Case Study in Pakistan - Hammad Qureshi, Arshad Ali, Shamila Keyani, and Atif Mumtaz
• 18.1 Introduction
• 18.2 Jaroka Tele-Healthcare System: A System for Disease Surveillance
• 18.2.1 How Does the Jaroka Tele-Healthcare System Work?
• 18.2.2 Mapping
• 18.3 Disease Trends
• 18.4 Conclusions
• Acknowledgments
• References
• mHealth and Web Applications - Javier Pindter-Medina
• 19.1 Introduction
• 19.2 mHealth Today
• 19.2.1 mHealth Based on Text Messaging
• 19.2.2 mHealth and Smartphones
• 19.2.3 Five Years of History of Smartphone-Based mHealth Devices
• 19.2.3.1 2009
• 19.2.3.2 2010
• 19.2.3.3 2011
• 19.2.3.4 2012
• 19.2.3.5 2013
• 19.2.4 mHealth and Other Technologies
• 19.2.5 Emerging Trends and Areas of Interest in mHealth
• 19.2.6 Health Informatics: The European Committee for Standardization ISO/IEEE 11703 Standards
• 19.3 Wireless Technologies Used in mHealth
• 19.4 Web Applications
• 19.5 mHealth Challenges and Ethics
• 19.6 Conclusions
• References
• Investigation and Assessment of Effectiveness of Knowledge Brokering on Web 2.0 in Health Sector in Quebec, Canada - Moktar Lamari and Saliha Ziam
• 20.1 Summary
• 20.2 Introduction
• 20.3 General Approach to Knowledge Brokerage, Theory, and Definitions
• 20.4 Public Health-Related Survey, the Data, and Data Analysis
• 20.4.1 Survey Results, Findings, and Interpretations
• 20.4.1.1 Instruments of Health-Related Knowledge Dissemination
• 20.4.1.2 Beneficiaries of Knowledge Brokerage
• 20.4.1.3 Networking and Interactions of Knowledge Brokers
• 20.4.1.4 Perceived Impacts of New Knowledge on Decision-Making Process
• 20.4.1.5 Determinants of the Perceived Impacts
• 20.5 Conclusion
• References
• Section V Examples of Integrating Technologies: Virtual Systems, Image Processing, Biokinematics, Measurements, and VLSI
• Virtual Doctor Systems for Medical Practices - Hamido Fujita and Enrique Herrera-Viedma
• 21.1 Introduction
• 21.2 Outline of Virtual Doctor System
• 21.2.1 Health Symptom Estimation from Breathing Sound
• 21.2.2 Avatar Screen Generation
• 21.2.3 Virtual Doctor System Interaction Based on Universal Templates
• 21.2.4 Transactional Analysis
• 21.2.5 Patient Interaction with Virtual Doctor System Avatar
• 21.3 Outline of Virtual Doctor System Diagnosis
• 21.4 Review of Literature on Decision Support for Medical Diagnosis
• 21.4.1 Decision-Support Systems
• 21.4.2 Subjective Intelligence
• 21.5 Reasoning Framework
• 21.6 Fuzzy-Based Reasoning
• 21.6.1 Fuzzy Linguistic Approach to Representing User Assessments
• 21.6.2 Medical Reasoning in a Fuzzy Linguistic Context
• 21.6.3 Medical Reasoning in a Multigranular Fuzzy Linguistic Context
• 21.7 Conclusions
• References
• Synthetic Biometrics in Biomedical Systems - Kenneth Lai, Steven Samoil, Svetlana N. Yanushkevich, and Adrian Stoica
• 22.1 Introduction
• 22.2 Biometric Data and Systems
• 22.3 Synthetic Biometrics
• 22.4 Synthetic Face
• 22.4.1 Analysis by Synthesis in Face Recognition
• 22.4.2 Three-Dimensional Facial Images
• 22.4.3 RGB-D Technologies
• 22.4.4 Two-Dimensional Facial Gesture Tracking
• 22.4.5 Modeling the Aging Face
• 22.4.6 Face Reconstruction from DNA
• 22.4.7 Behavioral Facial Synthesis: Expressions
• 22.4.8 Animation as Behavioral Facial Synthesis
• 22.5 Synthetic Fingerprints
• 22.6 Synthetic Iris and Retina Images
• 22.7 Synthetic Signatures
• 22.7.1 Voice Synthesis
• 22.8 Examples of the Usage of Synthetic Biometrics
• 22.8.1 Example: Facial Nerve Disorder Modeling
• 22.8.2 Decision-Making Support Systems
• 22.8.3 Databases of Synthetic Biometric Information
• 22.8.4 Medical Personnel Training
• 22.8.5 Avatar Systems
• 22.8.6 Rehabilitation Applications
• 22.9 Conclusions
• References
• Performance Analysis of Transform-Based Medical Image-Compression Methods for Telemedicine - Sujitha Juliet and Elijah Blessing Rajsingh
• 23.1 Introduction to Telemedicine
• 23.1.1 Store-and-Forward Telemedicine
• 23.1.2 Two-Way Interactive Telemedicine
• 23.1.3 Remote Monitoring
• 23.2 Challenges in Telemedicine
• 23.3 Challenges of Image Compression in Telemedicine
• 23.4 Overview of Transform-Based Image-Compression Methods
• 23.5 Quality Control in Telemedicine
• 23.6 Transform-Based Medical Image Compression
• 23.6.1 Ripplet Transform-Based Medical Image Compression
• 23.6.2 Bandelet Transform-Based Medical Image Compression
• 23.6.3 Radon Transform-Based Medical Image Compression
• 23.7 Set Partitioning in Hierarchical Trees Encoder
• 23.8 Results and Discussions
• 23.8.1 Analysis of Image Quality Based on Peak Signal-to-Noise Ratio
• 23.8.2 Analysis of Image Quality Based on Structural Similarity Index Measure
• 23.8.3 Analysis of Compression Ratio
• 23.8.4 Analysis of Computational Time
• 23.8.5 Analysis of Subjective Assessment
• 23.9 Conclusions
• 23.10 Future Scope
• Acknowledgments
• References
• Tracking the Position and Orientation of Ultrasound Probe for Image-Guided Surgical Procedures - Basem F. Yousef
• 24.1 Introduction
• 24.2 Mechanism Description
• 24.2.1 Stabilizer Design
• 24.2.2 Tracker Design
• 24.3 Tracker Calibration
• 24.4 Materials and Dimensions
• 24.5 Validation and Results
• 24.6 Conclusions
• Acknowledgments
• References
• Biokinematics for Mobility: Theory, Sensors, and Wireless Measurements - Atila Yilmaz and Tuna Orhanli
• 25.1 Introduction
• 25.1.1 General Review
• 25.1.2 Basic Definitions
• 25.1.3 Anatomical Reference System
• 25.2 Types of Kinematics
• 25.2.1 Forward Kinematics
• 25.2.2 Inverse Kinematics
• 25.2.3 Joint Velocity Kinematics
• 25.3 Measurements of Human Motion Kinematics
• 25.3.1 Image-Based Measurement Techniques
• 25.3.1.1 Cinematography
• 25.3.1.2 Television-Type Systems
• 25.3.1.3 Optoelectronic Measurements
• 25.3.2 Direct-Measurement Systems
• 25.3.2.1 Resistive Measurement Systems
• 25.3.2.2 Inertial Sensors
• 25.3.2.3 Electromagnetic Systems
• 25.4 Wireless Measurement Systems for Biokinematics
• 25.4.1 Background on Wireless Measurement Systems
• 25.4.2 Applications Related to Wireless Kinematic Measurements
• 25.5 Biodriven Hands, Prostheses, and Exoskeletal Ortheses
• 25.6 Conclusion
• Acknowledgments
• References
• Biopotentials and Electrophysiology Measurements - Nitish V. Thakor
• 26.1 Introduction
• 26.2 The Origins of Biopotentials
• 26.3 Biopotentials
• 26.3.1 Electrocardiogram
• 26.3.2 Electroencephalogram
• 26.3.3 Electromyogram
• 26.3.4 Electrooculogram
• 26.4 The Principles of Biopotential Measurements
• 26.5 Electrodes for Biopotential Recordings
• 26.5.1 Silver-Silver Chloride Electrodes
• 26.5.2 Gold Electrodes
• 26.5.3 Conductive Polymer Electrodes
• 26.5.4 Metal or Carbon Electrodes
• 26.5.5 Needle Electrodes
• 26.6 The Biopotential Amplifier
• 26.6.1 The Instrumentation Amplifier
• 26.6.2 The Electrocardiogram Amplifier
• 26.6.3 The Electroencephalogram Amplifier
• 26.6.4 The Electromyogram Amplifier
• 26.6.5 The Electrooculogram Amplifier
• 26.7 Circuit Enhancements
• 26.7.1 Electrical Interference Reduction
• 26.7.2 Filtering
• 26.7.3 Artifact Reduction
• 26.7.4 Electrical Isolation
• 26.7.5 Defibrillation Protection
• 26.8 Measurement Practices
• 26.8.1 Electrode Use
• 26.8.2 Skin Preparation
• 26.8.3 Reduction of Environmental Interference
• 26.9 Conclusions
• References
• Sensor Signal Conditioning for Biomedical Instrumentation - Tomas E. Ward
• 27.1 Introduction
• 27.2 Sensors
• 27.3 Signal Conditioning
• 27.3.1 The Operational Amplifier
• 27.3.2 Signal Amplification with Operational Amplifiers
• 27.3.2.1 Example: Piezoelectric Transducer Compensation
• 27.3.3 The Instrumentation Amplifier
• 27.4 The Analog-to-Digital Conversion Process
• 27.4.1 The Sampling Process
• 27.4.2 The Quantization Process
• 27.4.3 Antialiasing Filters
• 27.4.4 Oversampling and Decimation
• 27.4.4.1 Oversampling
• 27.4.4.2 Multisampling
• 27.5 Integrated Solutions
• 27.6 Isolation Circuits
• 27.6.1 Methods of Isolation
• 27.6.1.1 Capacitive Isolation Amplifiers
• 27.6.1.2 Optical Isolation Amplifiers
• 27.6.1.3 Magnetic Isolation Amplifiers
• 27.6.1.4 Digital Isolation
• 27.7 Conclusions
• References
• Sensor-Based Human Activity Recognition Techniques - Donghai Guan, Weiwei Yuan, and Sungyoung Lee
• 28.1 Introduction
• 28.2 Video Sensor-Based Activity Recognition
• 28.2.1 Video Sensor-Based Activity Recognition Applications
• 28.2.2 Feature Extraction in Video Sensor-Based Activity Recognition
• 28.2.2.1 Global Features of Video Sensor-Based Activity Recognition
• 28.2.2.2 Local Features of Video Sensor-Based Activity Recognition
• 28.2.3 Recognition Techniques in Video Sensor-Based Activity Recognition
• 28.2.3.1 Nonparametric Techniques
• 28.2.3.2 Volumetric Techniques
• 28.2.3.3 Temporal-Independent Techniques
• 28.2.3.4 Temporal-Based Techniques
• 28.3 Wearable Sensor-Based Activity Recognition
• 28.3.1 Applications of Wearable Sensor-Based Activity Recognition
• 28.3.2 Sensors in Wearable Sensor-Based Activity Recognition
• 28.3.3 Recognition Techniques for Wearable Sensor-Based Activity Recognition
• 28.3.3.1 Supervised Recognition Techniques
• 28.3.3.2 Unsupervised Recognition Techniques
• 28.4 Object Usage-Based Activity Recognition
• 28.4.1 Sensors in Object Usage-Based Activity Recognition
• 28.4.1.1 Radio Frequency Identification-Based Sensors
• 28.4.1.2 Binary Sensors
• 28.4.2 Recognition Algorithms
• 28.5 Comparisons of Video Sensor-Based, Wearable Sensor-Based, and Object Usage-Based Activity Recognition
• 28.5.1 Video Sensor-Based Activity Recognition
• 28.5.2 Wearable Sensor-Based Activity Recognition
• 28.5.3 Object Usage-Based Activity Recognition
• 28.6 Challenges in Sensor-Based Activity Recognition
• References
• Very Large-Scale Integration Bioinstrumentation Circuit Design and Nanopore Applications - Jungsuk Kim and William B. Dunbar
• 29.1 Introduction
• 29.1.1 Nanopore Method and Measurement
• 29.1.2 Design Requirements
• 29.2 Very Large-Scale Integration Bioinstrumentation Circuit Design
• 29.2.1 Noise Analysis
• 29.2.2 Low-Noise Core-Amplifier Design
• 29.2.3 Dead-Time Compensation
• 29.2.4 Input Offset Voltage Cancellation
• 29.3 Implementation and Experimental Results
• 29.4 Scalability and Multichannel Implementation
• References
• Wireless Electrical Impedance Tomography: LabVIEW-Based Automatic Electrode Switching - Tushar Kanti Bera
• 30.1 Introduction
• 30.2 Electrical Impedance Tomography Wireless Instrumentation
• 30.2.1 Constant-Current Injector
• 30.2.2 Electrode Switching in Electrical Impedance Tomography
• 30.2.3 Electrode Switching Module
• 30.2.4 WL-DDTS with Radio-Frequency Transmitter and Receiver
• 30.3 Digital Logic for Electrode Switching in LabVIEW-Based Algorithms
• 30.4 Electrode Protocols and Data Generation
• 30.4.1 Neighboring Method
• 30.4.2 Opposite Method
• 30.5 Wireless Experimental Data Collection and Image Reconstruction
• 30.5.1 Advantages and Disadvantages
• 30.6 Mathematical Approach and Electrode Models
• 30.6.1 Continuum Model
• 30.6.2 Gap Model
• 30.6.3 Shunt Model
• 30.6.4 Complete Electrode Model
• 30.7 Results and Discussion of Results
• 30.8 Conclusions
• References