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E-raamat: Telehealth and Mobile Health [Taylor & Francis e-raamat]

Edited by (Curtin University, Bentley, Australia), Edited by (University of Wisconsin, Madison, USA)
  • Formaat: 746 pages
  • Ilmumisaeg: 26-Jul-2017
  • Kirjastus: CRC Press
  • ISBN-13: 9780429172601
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Telehealth and Mobile HealthSuurem pilt
  • Formaat: 746 pages
  • Ilmumisaeg: 26-Jul-2017
  • Kirjastus: CRC Press
  • ISBN-13: 9780429172601
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Taylor & Francis e-raamatudRohkem infot Taylor & Francis e-raamatute kohta
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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.

Telehealth and Mobile Health is the second volume of this handbook. Featuring chapters written by leading experts and researchers in their respective fields, this volume:











Discusses telesurgery, medical robotics, and image guidance as well as telenursing and remote patient care Describes the implementation of networks, data management, record management, and effective personnel training Explains how the use of new technologies brings many business, management, and service opportunities Provides examples of scientific advancements such as brain-controlled bionic human arms and hands Incorporates clinical applications throughout for practical reference





The E-Medicine, E-Health, M-Health, Telemedicine, and Telehealth Handbook

bridges the gap between scientists, engineers, and medical professionals by creating synergy in the related fields of biomedical engineering, information and communication technology, business, and healthcare.
Preface ix
Acknowledgments xiii
Editors xv
Contributors xvii
Section I Medical Robotics, Telesurgery, and Image-Guided Surgery
1 Medical Robotics
3(34)
Giancarlo Ferrigno
Alessandra Pedrocchi
Elena De Momi
Emilia Ambrosini
Elisa Beretta
1.1 Introduction to Medical Robotics
3(2)
1.1.1 Definitions and Standards
4(1)
1.1.2 Historical Perspective
4(1)
1.2 Surgical Robots
5(12)
1.2.1 General Requirements
5(1)
1.2.2 Control
6(1)
1.2.2.1 Position Control
6(1)
1.2.2.2 Shared Control
8(1)
1.2.2.3 Cooperative Control
11(1)
1.2.2.4 Teleoperation
13(3)
1.2.3 Recent Developments
16(1)
1.3 Rehabilitation Robots
17(6)
1.3.1 Introduction: Why Robots in Rehabilitation?
17(1)
1.3.2 The Mechanical Design: Exoskeleton versus End-Effector Robots-Some Examples
18(1)
1.3.3 The Problem of Control
18(3)
1.3.4 Impact on Clinical Practice and First Evidence-Based Studies of Rehabilitation Robotics
21(1)
1.3.5 Perspectives and Challenges
22(1)
1.4 Assistive Robots
23(6)
1.4.1 Introduction
23(1)
1.4.2 Physical Assistance Robots
23(1)
1.4.3 Mobility Aids
23(3)
1.4.4 Activity of Daily Living Support
26(2)
1.4.5 Future Perspectives
28(1)
References
29(8)
2 Modern Devices for Telesurgery
37(24)
Florian Gosselin
2.1 Introduction and History
37(5)
2.2 Main Components and Functionalities of a Robotic Telesurgery System
42(5)
2.2.1 General Overview
42(1)
2.2.2 Slave Surgery Robots
42(2)
2.2.3 Master Control Station
44(1)
2.2.4 Additional Equipment and Communication Means
45(1)
2.2.5 Main Functionalities
45(1)
2.2.5.1 Master-Slave Teleoperation
45(1)
2.2.5.2 Motion (and Force) Scaling
46(1)
2.2.5.3 Tremor Cancellation
46(1)
2.2.5.4 Shared Control
46(1)
2.2.5.5 Augmented Haptic Feedback
47(1)
2.3 Optimal Design of an Advanced Input Device for Telesurgery
47(9)
2.3.1 Design Guidelines
47(2)
2.3.2 Design Methodology
49(4)
2.3.3 Application to the Design of a Telesurgery Master Arm
53(3)
2.4 Conclusion
56(1)
References
57(4)
3 Microsurgery Systems
61(30)
Leonardo S. Mattos
Diego Pardo
Emidio Olivieri
Giacinto Barresi
Jesus Ortiz
Loris Fichera
Nikhil Deshpande
Veronica Penza
3.1 Introduction
62(1)
3.2 Clinical Applications
63(1)
3.2.1 Pediatric and Fetal Surgery
63(1)
3.2.2 Ophthalmology
63(1)
3.2.3 Otolaryngology
63(1)
3.2.4 Plastic Surgery
64(1)
3.2.5 Nerve Surgery
64(1)
3.2.6 Urology
64(1)
3.3 Microsurgery Systems in Clinical Use
64(3)
3.4 Robot-Assisted Microsurgery Systems
67(5)
3.5 Current Challenges for Next-Generation Microsurgery Systems
72(12)
3.5.1 Miniaturization
72(1)
3.5.1.1 Materials and Robustness
72(1)
3.5.1.2 Maneuverability
73(1)
3.5.1.3 Sensing
73(1)
3.5.1.4 Actuation
74(1)
3.5.2 Microsurgical Tools
74(1)
3.5.2.1 Sensing Tools
74(1)
3.5.2.2 Actuation Tools
75(1)
3.5.3 Visualization Methods and Systems
76(1)
3.5.3.1 Visualization Devices
76(1)
3.5.3.2 Augmented Reality
77(1)
3.5.4 Haptic Feedback
78(1)
3.5.5 Control Interfaces and Ergonomics
78(2)
3.5.6 Surgical Planning
80(1)
3.5.6.1 Preoperative Reconstruction
81(1)
3.5.6.2 Intraoperative Registration
81(1)
3.5.7 Safety
82(1)
3.5.8 Autonomous Behaviors
83(1)
3.6 Conclusion
84(1)
References
85(6)
4 Image-Guided Microsurgery
91(28)
Tom Williamson
Marco Caversaccio
Stefan Weber
Brett Bell
4.1 Introduction
91(2)
4.1.1 What Is Image Guidance?
92(1)
4.1.2 Why Image Guidance?
92(1)
4.2 Image Guidance Components and Workflow
93(10)
4.2.1 Image Acquisition
93(3)
4.2.2 Surgical Planning
96(1)
4.2.3 Registration
97(2)
4.2.4 Tracking
99(1)
4.2.5 Instrumentation and Instrument Guidance
100(2)
4.2.6 Information Presentation
102(1)
4.3 Image Guidance by Surgical Domain
103(6)
4.3.1 Image Guidance in Otorhinolaryngology
103(3)
4.3.2 Image Guidance in Neurosurgery
106(2)
4.3.3 Image Guidance in Ophthalmic Surgery
108(1)
4.3.4 Image Guidance in Other Surgeries
108(1)
4.4 Conclusions
109(1)
References
110(9)
Section II Telenursing, Personalized Care, Patient Care, and eEmergency Systems
5 eHealth and Telenursing
119(26)
Sinclair Wynchank
Nathanael Sabbah
5.1 Introduction
120(2)
5.2 How Telenursing Came About
122(1)
5.3 Nursing's Applications of Information and Communication Technology
122(3)
5.3.1 Computerized Decision-Support Systems
122(1)
5.3.2 Databases
122(1)
5.3.3 Telephony
123(1)
5.3.4 Videoconferencing
124(1)
5.3.5 mHealth
124(1)
5.3.6 Telenursing Services
124(1)
5.4 Telenursing's Healthcare Applications
125(5)
5.4.1 Triage
125(1)
5.4.2 Maternity and Pediatrics
125(1)
5.4.3 Posthospitalization
126(1)
5.4.4 Home Care
127(1)
5.4.5 Chronic Illnesses
128(1)
5.4.6 Human Immunodeficiency Virus/Acquired Immunodeficiency Syndrome
128(1)
5.4.7 Mental Health
129(1)
5.4.8 Geriatrics
129(1)
5.5 Nurse Migration
130(1)
5.6 Telenursing and Distance Education
130(4)
5.6.1 Bases of e-learning
130(1)
5.6.2 Educating Laypersons
131(1)
5.6.3 Formal Instruction
132(1)
5.6.4 Web-Based Education
132(1)
5.6.5 Recent Trends
133(1)
5.7 Telenursing and Ethical Questions
134(1)
5.8 Discussion
135(1)
5.9 Conclusions
136(1)
Abbreviations
137(1)
Nomenclature
137(1)
Acknowledgments
137(1)
References
138(7)
6 mHealth: Intelligent Closed-Loop Solutions for Personalized Healthcare
145(16)
Carmen C.Y. Poon
Kevin K.F. Hung
6.1 Introduction
145(1)
6.2 Historical Overview of mHealth
146(4)
6.2.1 Evolution from Telemedicine to mHealth
146(1)
6.2.2 Initial mHealth Applications
146(2)
6.2.3 Recent mHealth Applications
148(2)
6.3 Mobile Apps for mHealth
150(2)
6.3.1 Overview of mHealth Apps
151(1)
6.3.2 Regulation of mHealth Apps
151(1)
6.4 Cloud Computing
152(2)
6.4.1 Definitions
152(1)
6.4.2 Selected Applications
153(1)
6.5 Closed-Loop Solutions for Personalized Health Interventions
154(3)
6.5.1 Challenges in Sensor Design and Fabrication
154(1)
6.5.2 Challenges in Mining and Managing Big Health Data
155(2)
6.6 Conclusions
157(1)
Abbreviations and Nomenclature
157(1)
Acknowledgments
158(1)
References
158(3)
7 Patient Care Sensing and Monitoring Systems
161(12)
Akihiro Kajiwara
Ryohei Nakamura
7.1 Introduction
161(1)
7.2 Stepped-Frequency Modulation Ultrawideband Scheme
162(5)
7.2.1 Ultrawideband Impulse Radio Sensor
162(1)
7.2.2 Stepped-Frequency Modulation Ultrawideband Sensor
163(2)
7.2.3 Detect-and-Avoid and Spectrum Hole Technique
165(2)
7.3 Detect-and-Avoid Technique
167(1)
7.4 Patient Care Sensing and Monitoring System
168(3)
7.4.1 Sensing and Monitoring Algorithm
168(2)
7.4.2 Measurement Results
170(1)
7.5 Conclusions
171(1)
References
172(1)
8 Mobile Health Sleep Technologies
173(14)
Anda Baharav
8.1 Introduction
173(2)
8.1.1 Background about Sleep
173(1)
8.1.2 Sleep Problems and Their Implications
174(1)
8.2 Sleep and Technology
175(1)
8.2.1 The Role of Technology in General and Mobile Technology in Particular in Inducing Sleep Disorders
175(1)
8.2.2 Why Mobile Interface Is Most Suitable for Sleep
175(1)
8.3 Methods for Evaluating Sleep
176(5)
8.3.1 Subjective Information and Questionnaires
177(1)
8.3.2 Diaries
178(1)
8.3.3 Gold-Standard Polysomnography
178(1)
8.3.4 Electroencephalography
179(1)
8.3.5 Heart-Rate Variability
179(1)
8.3.6 Movement Actigraphy
180(1)
8.3.7 Behavioral: Audio-Video Monitoring
180(1)
8.4 Adding Treatment
181(1)
8.5 Players on the Market
182(2)
8.6 Advantages
184(1)
8.7 Next Steps
185(1)
Abbreviations
186(1)
References
186(1)
9 Cardiovascular Disease Management via Electronic Health
187(16)
Aimilia Gastounioti
Spyretta Golemati
Ioannis Andreadis
Vasileios Kolias
Konstantina S. Nikita
9.1 Introduction
187(1)
9.2 Computer-Aided Diagnosis
188(4)
9.2.1 Analysis of Cardiovascular Signals and Images
189(1)
9.2.2 Generating a Diagnostic Decision
190(2)
9.3 Telehealth Systems
192(2)
9.4 Mobile Applications
194(1)
9.5 Web-Based Telemedicine
195(1)
9.6 Semantic Interoperability and Ontologies
196(1)
9.7 Future Trends
197(1)
9.8 Conclusions
198(1)
References
199(4)
10 mHealth eEmergency Systems
203(30)
Efthyvoulos Kyriacou
Andreas Panayides
Panayiotis Constantinides
10.1 Introduction
203(1)
10.2 Enabling Technologies
204(4)
10.2.1 Wireless Transmission Technologies
204(1)
10.2.2 Mobile Computing Platforms
205(1)
10.2.3 Biosignals
205(1)
10.2.4 Transmission of Digital Images
206(1)
10.2.5 Transmission of Digital Video
207(1)
10.3 Protocols and Processes for eEmergency Management and Response
208(4)
10.3.1 Emergency Management and Response: The Challenge of Coordination
208(2)
10.3.2 Computer-Aided Medical Dispatch Systems
210(2)
10.4 mHealth eEmergency Systems
212(13)
10.4.1 Overview
215(1)
10.4.2 Case Studies
215(1)
10.4.2.1 Case Study 1: Emergency Telemedicine-The AMBULANCE and Emergency 112 Projects
215(1)
10.4.2.2 Case Study 2: Diagnostically Robust Ultrasound Video Transmission over Emerging Wireless Networks
221(4)
References
225(8)
Section III Networks and Databases, Informatics, Record Management, Education, and Training
11 Global and Local Health Information, Databases, and Networks
233(18)
Kostas Giokas
Yiannis Makris
Anna Paidi
Marios Prasinos
Dimitra Iliopoulou
Dimitris Koutsouris
11.1 Introduction
233(1)
11.2 Local Health Data
234(3)
11.2.1 Collection of Local Health Data
234(1)
11.2.2 Warehousing of Local Health Data
235(1)
11.2.3 Analysis of Local Health Data
235(1)
11.2.4 Local Health Data Network
236(1)
11.2.5 Challenges and Inefficiencies Associated with a Local Health Data Network
237(1)
11.2.5.1 Data Complexity and Integration
237(1)
11.2.5.2 Privacy, Security, and Patients' Consent
237(1)
11.3 Databases
237(3)
11.3.1 Introduction
237(1)
11.3.2 Database Architectures
238(1)
11.3.2.1 Traditional Architectures
238(1)
11.3.2.2 Server System Architectures
238(1)
11.3.3 Parallel Systems
238(1)
11.3.4 Distributed Systems
239(1)
11.4 Database System Concepts in Healthcare
240(1)
11.4.1 World Health Organization Classifications
240(1)
11.4.2 General Online Health Databases
240(1)
11.4.2.1 European Health for All Database
240(1)
11.4.2.2 The National Institutes of Health Intramural Database
240(1)
11.4.2.3 Other European Online Health Databases
241(1)
11.5 Data Curation
241(3)
11.6 Interpretation of Health and Epidemiological Data-Biostatistics
244(2)
11.7 Global Health Data Management and Interpretation
246(3)
References
249(2)
12 Electronic Medical Records: Management and Implementation
251(26)
Liping Liu
12.1 Introduction
251(1)
12.2 Detailed Functional and Data Requirements
252(9)
12.2.1 Functional Requirements
253(2)
12.2.2 Data Requirements
255(6)
12.3 Implementation Issues and Solutions
261(5)
12.3.1 Implementation Issues
261(2)
12.3.2 Technological Solutions
263(3)
12.4 An Integrated e-Service Framework
266(7)
12.4.1 Justifications
268(1)
12.4.2 Implementation
269(4)
12.5 Conclusions
273(2)
References
275(2)
13 Public Health Informatics in Australia and around the World
277(22)
Kathleen Gray
Fernando Martin Sanchez
13.1 Introduction
278(4)
13.1.1 Information and Communication in Public Health
278(1)
13.1.2 Evolution of Public Health Informatics
279(1)
13.1.3 Key Concepts in Public Health Informatics
280(1)
13.1.3.1 Data Management in Public Health Informatics
281(1)
13.1.3.2 Information Management in Public Health Informatics
281(1)
13.1.3.3 Knowledge Management in Public Health Informatics
282(1)
13.2 Public Health Informatics in Australia
282(8)
13.2.1 Australia's Public Health
282(1)
13.2.2 Australian National Public Health Information Infrastructure
283(2)
13.2.3 Australian State and Territory Public Health Informatics Strategies
285(2)
13.2.4 Australian Local Government Public Health Informatics Initiatives
287(1)
13.2.4.1 Systematizing Data for Child and Family Nursing
287(1)
13.2.4.2 Immediate Information for Disaster Management
288(1)
13.2.4.3 Knowledge Translation for Obesity Prevention
289(1)
13.2.5 The Discipline and Profession of Public Health Informatics in Australia
289(1)
13.3 Current International Perspectives on Public Health Informatics
290(1)
13.3.1 Biosurveillance Methods in England and Wales
290(1)
13.3.2 Assessment of European Community Health Indicators
291(1)
13.3.3 Data Use Workshops in Tanzania
291(1)
13.3.4 The Impact of Technology on Sub-Saharan Hospitals
291(1)
13.4 Directions for Public Health Informatics
291(3)
13.4.1 Public Health 2.0
292(1)
13.4.2 Bidirectional Communication
292(1)
13.4.3 Exposome Informatics
292(1)
13.4.4 Advancing the Agenda for Public Health Informatics
293(1)
13.5 Conclusions
294(1)
References
294(5)
14 Ubiquitous Personal Health Records for Remote Regions
299(20)
H. Lee Seldon
Jacey-Lynn Minoi
Mahmud Ahsan
Ali Abdulwahab A. Al-Habsi
14.1 Introduction
300(1)
14.1.1 Scope
300(1)
14.2 Personal Health Record Data Collection and Storage: Ubiquitous or Not?
301(9)
14.2.1 Constraints in Remote Regions: Healthcare Workers and the Web Are Not Ubiquitous
301(2)
14.2.2 Personal Health Record on Paper: Not Quite Ubiquitous
303(1)
14.2.3 Web-Based Personal Health Records
303(1)
14.2.3.1 Purely Web-Based Personal Health Records Are Not Quite Ubiquitous
303(1)
14.2.3.2 Web-Based Personal Health Records with Various Inputs and Outputs
305(2)
14.2.4 Personal Health Records on Stand-Alone Mobile Devices: Not Quite Ubiquitous
307(1)
14.2.4.1 Diabetes or Hypertension Management
307(1)
14.2.4.2 Diet and Exercise
308(1)
14.2.4.3 Personal Health Records
308(1)
14.2.5 Personal Health Records on Connected Devices: Maybe Ubiquitous
309(1)
14.2.5.1 OpenMRS, OpenRosa, JavaRosa, and Sana
309(1)
14.2.5.2 EPI Life and EPI Mini
310(1)
14.3 A Ubiquitous Personal Health Record for Remote Regions Must Involve Individuals and Include Phone-Based Records
310(3)
14.3.1 Examples: Portable Personal Health Records
310(1)
14.3.1.1 Portable Personal Health Records: Mobile Records
311(1)
14.3.1.2 Portable Personal Health Records: Web-Based Records
312(1)
14.3.1.3 Portable Personal Health Records: Ubiquitous Communications
312(1)
14.4 Contextual Information: The Icing on the Personal Health Records
313(2)
14.4.1 Contextual Information on the Web
314(1)
14.4.2 Retrieval of Contextual Information
314(1)
14.4.3 Integration into a Ubiquitous Personal Health Record System
314(1)
14.5 Conclusions
315(1)
References
315(4)
15 Education and Training for Supporting General Practitioners in the Use of Clinical Telehealth: A Needs Analysis
319(12)
Sisira Edirippulige
Nigel R. Armfield
Liam Caffery
Anthony C. Smith
15.1 Introduction
319(1)
15.2 Methods
320(2)
15.2.1 Participants
320(1)
15.2.2 Survey Questions
321(1)
15.2.3 Analysis
321(1)
15.2.4 Ethics
322(1)
15.3 Results
322(4)
15.3.1 Characteristics of the Responding Practices
322(1)
15.3.2 Current and Planned Use of Telehealth
322(1)
15.3.3 Education and Training
322(4)
15.4 Discussions
326(1)
15.4.1 Telehealth Training
326(1)
15.5 Conclusions
327(1)
References
327(1)
Further Reading
328(3)
Section IV Business Opportunities, Management and Services, and Web Applications
16 Delivering eHealthcare: Opportunities and Challenges
331(32)
Deborah A. Heiman
Eric J. Addeo
N. Iwan Santoso
David W. Walters
Guy T. Helman
16.1 Introduction
332(1)
16.2 Context: The Evolution of eHealth
332(6)
16.2.1 The Multidimensional Landscape of Healthcare Delivery: Associated Driving Forces
334(1)
16.2.2 Feasible Models
334(2)
16.2.3 Physicians' and Patients' Resistance and Readiness
336(2)
16.3 Delivering eHealthcare: Practical Applications
338(3)
16.3.1 Children's National Medical Center: Specialized Services
339(1)
16.3.2 Kaiser Permanente: A Healthcare System
339(1)
16.3.3 Misfit Wearables: Start-Ups
340(1)
16.4 The Value Chain Network Business Model
341(10)
16.4.1 Value, Value Drivers, and Value Propositions
342(2)
16.4.2 Identifying Value Drivers
344(1)
16.4.3 The Value Proposition
345(2)
16.4.4 Managing in the Value Chain Network
347(2)
16.4.5 Value and the Value Chain
349(2)
16.4.6 Applying the e-Value Chain to Healthcare
351(1)
16.5 Value Drivers and Value-Led Productivity: A Network Perspective
351(4)
16.5.1 Value Drivers as Network Performance Drivers
351(2)
16.5.2 Value Drivers as Productivity Components
353(1)
16.5.3 Assessing the Productivity and Competitive Advantage of the Value Proposition
354(1)
16.6 Technology Perspective
355(5)
16.6.1 Cloud Computing
356(3)
16.6.2 Smart Healthcare Personal Assistants
359(1)
16.6.3 Security and Privacy in eHealth
360(1)
16.7 Conclusions and Future Challenges
360(1)
Abbreviations
361(1)
References
361(2)
17 Mobile Healthcare User Interface Design Application Strategies
363(20)
Ann L. Fruhling
Sharmila Raman
Scott McGrath
17.1 Introduction
363(3)
17.2 Mobile Healthcare App User Interface Design Strategies
366(4)
17.2.1 Focusing on Essential Functions in the Mobile Environment
366(1)
17.2.2 Ease of Use
367(1)
17.2.3 Intuitive Interaction
367(1)
17.2.4 Consistency within a Family of Applications
367(1)
17.2.5 Matching Routine Work Flow
368(1)
17.2.6 Limiting Menu/Layer Display Structure
369(1)
17.2.7 Minimalist Aesthetics
369(1)
17.2.7.1 Log-In/Log-Out Guidelines
369(1)
17.2.8 Leveraging Agile Development Practices
370(1)
17.3 Using Mobile Device Simulators for Testing
370(1)
17.3.1 Aiming for Quick Response Time
370(1)
17.3.2 Physical Device Selection Considerations
371(1)
17.4 Example of Applying Healthcare Mobile Development Strategies
371(8)
17.4.1 Public Health Mobile Application Background
371(2)
17.4.2 Mobile Solution
373(1)
17.4.3 Mobile User Interface and System Guidelines
374(4)
17.4.4 Mobile Thin Client
378(1)
17.4.4.1 STATPack Mobile Implementation
378(1)
17.5 Conclusion
379(1)
Acknowledgments
379(1)
References
380(3)
18 Epidemic Tracking and Disease Monitoring in Rural Areas: A Case Study in Pakistan
383(12)
Hammad Qureshi
Arshad Ali
Shamila Keyani
Atif Mumtaz
18.1 Introduction
383(2)
18.2 Jaroka Tele-Healthcare System: A System for Disease Surveillance
385(4)
18.2.1 How Does the Jaroka Tele-Healthcare System Work?
385(2)
18.2.2 Mapping
387(2)
18.3 Disease Trends
389(3)
18.4 Conclusions
392(1)
Acknowledgments
393(1)
References
393(2)
19 mHealth and Web Applications
395(22)
Javier Pindter-Medina
19.1 Introduction
395(2)
19.2 mHealth Today
397(8)
19.2.1 mHealth Based on Text Messaging
398(1)
19.2.2 mHealth and Smartphones
399(1)
19.2.3 Five Years of History of Smartphone-Based mHealth Devices
400(1)
19.2.3.1 2009
400(1)
19.2.3.2 2010
401(1)
19.2.3.3 2011
401(1)
19.2.3.4 2012
401(1)
19.2.3.5 2013
402(1)
19.2.4 mHealth and Other Technologies
402(1)
19.2.5 Emerging Trends and Areas of Interest in mHealth
403(1)
19.2.6 Health Informatics: The European Committee for Standardization ISO/IEEE 11703 Standards
404(1)
19.3 Wireless Technologies Used in mHealth
405(3)
19.4 Web Applications
408(3)
19.5 mHealth Challenges and Ethics
411(2)
19.6 Conclusions
413(1)
References
414(3)
20 Investigation and Assessment of Effectiveness of Knowledge Brokering on Web 2.0 in Health Sector in Quebec, Canada
417(18)
Moktar Lamari
Saliha Ziam
20.1 Summary
417(1)
20.2 Introduction
418(1)
20.3 General Approach to Knowledge Brokerage, Theory, and Definitions
419(2)
20.4 Public Health-Related Survey, the Data, and Data Analysis
421(8)
20.4.1 Survey Results, Findings, and Interpretations
423(1)
20.4.1.1 Instruments of Health-Related Knowledge Dissemination
424(1)
20.4.1.2 Beneficiaries of Knowledge Brokerage
424(1)
20.4.1.3 Networking and Interactions of Knowledge Brokers
425(1)
20.4.1.4 Perceived Impacts of New Knowledge on Decision-Making Process
426(1)
20.4.1.5 Determinants of the Perceived Impacts
427(2)
20.5 Conclusion
429(1)
References
430(5)
Section V Examples of Integrating Technologies: Virtual Systems, Image Processing, Biokinematics, Measurements, and VLSI
21 Virtual Doctor Systems for Medical Practices
435(32)
Hamido Fujita
Enrique Herrera-Viedma
21.1 Introduction
435(4)
21.2 Outline of Virtual Doctor System
439(10)
21.2.1 Health Symptom Estimation from Breathing Sound
439(2)
21.2.2 Avatar Screen Generation
441(1)
21.2.3 Virtual Doctor System Interaction Based on Universal Templates
442(4)
21.2.4 Transactional Analysis
446(1)
21.2.5 Patient Interaction with Virtual Doctor System Avatar
447(2)
21.3 Outline of Virtual Doctor System Diagnosis
449(2)
21.4 Review of Literature on Decision Support for Medical Diagnosis
451(2)
21.4.1 Decision-Support Systems
451(1)
21.4.2 Subjective Intelligence
452(1)
21.5 Reasoning Framework
453(1)
21.6 Fuzzy-Based Reasoning
453(8)
21.6.1 Fuzzy Linguistic Approach to Representing User Assessments
454(4)
21.6.2 Medical Reasoning in a Fuzzy Linguistic Context
458(1)
21.6.3 Medical Reasoning in a Multigranular Fuzzy Linguistic Context
459(2)
21.7 Conclusions
461(1)
References
462(5)
22 Synthetic Biometrics in Biomedical Systems
467(20)
Kenneth Lai
Steven Samoil
Svetlana N. Yanushkevich
Adrian Stoica
22.1 Introduction
467(1)
22.2 Biometric Data and Systems
468(1)
22.3 Synthetic Biometrics
469(1)
22.4 Synthetic Face
470(5)
22.4.1 Analysis by Synthesis in Face Recognition
470(1)
22.4.2 Three-Dimensional Facial Images
470(2)
22.4.3 RGB-D Technologies
472(1)
22.4.4 Two-Dimensional Facial Gesture Tracking
473(1)
22.4.5 Modeling the Aging Face
473(1)
22.4.6 Face Reconstruction from DNA
473(1)
22.4.7 Behavioral Facial Synthesis: Expressions
474(1)
22.4.8 Animation as Behavioral Facial Synthesis
475(1)
22.5 Synthetic Fingerprints
475(1)
22.6 Synthetic Iris and Retina Images
476(1)
22.7 Synthetic Signatures
477(1)
22.7.1 Voice Synthesis
477(1)
22.8 Examples of the Usage of Synthetic Biometrics
478(4)
22.8.1 Example: Facial Nerve Disorder Modeling
478(1)
22.8.2 Decision-Making Support Systems
478(1)
22.8.3 Databases of Synthetic Biometric Information
479(1)
22.8.4 Medical Personnel Training
480(1)
22.8.5 Avatar Systems
481(1)
22.8.6 Rehabilitation Applications
481(1)
22.9 Conclusions
482(1)
References
482(5)
23 Performance Analysis of Transform-Based Medical Image-Compression Methods for Telemedicine
487(24)
Sujitha Juliet
Elijah Blessing Rajsingh
23.1 Introduction to Telemedicine
487(1)
23.1.1 Store-and-Forward Telemedicine
488(1)
23.1.2 Two-Way Interactive Telemedicine
488(1)
23.1.3 Remote Monitoring
488(1)
23.2 Challenges in Telemedicine
488(1)
23.3 Challenges of Image Compression in Telemedicine
489(1)
23.4 Overview of Transform-Based Image-Compression Methods
490(1)
23.5 Quality Control in Telemedicine
490(1)
23.6 Transform-Based Medical Image Compression
491(5)
23.6.1 Ripplet Transform-Based Medical Image Compression
491(2)
23.6.2 Bandelet Transform-Based Medical Image Compression
493(1)
23.6.3 Radon Transform-Based Medical Image Compression
494(2)
23.7 Set Partitioning in Hierarchical Trees Encoder
496(1)
23.8 Results and Discussions
497(10)
23.8.1 Analysis of Image Quality Based on Peak Signal-to-Noise Ratio
497(4)
23.8.2 Analysis of Image Quality Based on Structural Similarity Index Measure
501(3)
23.8.3 Analysis of Compression Ratio
504(1)
23.8.4 Analysis of Computational Time
504(3)
23.8.5 Analysis of Subjective Assessment
507(1)
23.9 Conclusions
507(1)
23.10 Future Scope
508(1)
Acknowledgments
508(1)
References
508(3)
24 Tracking the Position and Orientation of Ultrasound Probe for Image-Guided Surgical Procedures
511(14)
Basem F. Yousef
24.1 Introduction
511(3)
24.2 Mechanism Description
514(4)
24.2.1 Stabilizer Design
515(1)
24.2.2 Tracker Design
515(3)
24.3 Tracker Calibration
518(1)
24.4 Materials and Dimensions
519(1)
24.5 Validation and Results
520(2)
24.6 Conclusions
522(1)
Acknowledgments
523(1)
References
523(2)
25 Biokinematics for Mobility: Theory, Sensors, and Wireless Measurements
525(30)
Atila Yilmaz
Tuna Orhanli
25.1 Introduction
525(5)
25.1.1 General Review
526(2)
25.1.2 Basic Definitions
528(1)
25.1.3 Anatomical Reference System
529(1)
25.2 Types of Kinematics
530(4)
25.2.1 Forward Kinematics
530(2)
25.2.2 Inverse Kinematics
532(1)
25.2.3 Joint Velocity Kinematics
533(1)
25.3 Measurements of Human Motion Kinematics
534(10)
25.3.1 Image-Based Measurement Techniques
534(1)
25.3.1.1 Cinematography
537(1)
25.3.1.2 Television-Type Systems
537(1)
25.3.1.3 Optoelectronic Measurements
538(2)
25.3.2 Direct-Measurement Systems
540(1)
25.3.2.1 Resistive Measurement Systems
540(1)
25.3.2.2 Inertial Sensors
541(1)
25.3.2.3 Electromagnetic Systems
544(1)
25.4 Wireless Measurement Systems for Biokinematics
544(3)
25.4.1 Background on Wireless Measurement Systems
544(2)
25.4.2 Applications Related to Wireless Kinematic Measurements
546(1)
25.5 Biodriven Hands, Prostheses, and Exoskeletal Ortheses
547(3)
25.6 Conclusion
550(1)
Acknowledgments
551(1)
References
551(4)
26 Biopotentials and Electrophysiology Measurements
555(20)
Nitish V. Thakor
26.1 Introduction
556(1)
26.2 The Origins of Biopotentials
556(2)
26.3 Biopotentials
558(4)
26.3.1 Electrocardiogram
559(1)
26.3.2 Electroencephalogram
559(2)
26.3.3 Electromyogram
561(1)
26.3.4 Electrooculogram
561(1)
26.4 The Principles of Biopotential Measurements
562(1)
26.5 Electrodes for Biopotential Recordings
562(2)
26.5.1 Silver-Silver Chloride Electrodes
562(1)
26.5.2 Gold Electrodes
563(1)
26.5.3 Conductive Polymer Electrodes
563(1)
26.5.4 Metal or Carbon Electrodes
564(1)
26.5.5 Needle Electrodes
564(1)
26.6 The Biopotential Amplifier
564(3)
26.6.1 The Instrumentation Amplifier
564(2)
26.6.2 The Electrocardiogram Amplifier
566(1)
26.6.3 The Electroencephalogram Amplifier
566(1)
26.6.4 The Electromyogram Amplifier
566(1)
26.6.5 The Electrooculogram Amplifier
566(1)
26.7 Circuit Enhancements
567(4)
26.7.1 Electrical Interference Reduction
567(1)
26.7.2 Filtering
567(3)
26.7.3 Artifact Reduction
570(1)
26.7.4 Electrical Isolation
570(1)
26.7.5 Defibrillation Protection
571(1)
26.8 Measurement Practices
571(2)
26.8.1 Electrode Use
571(1)
26.8.2 Skin Preparation
572(1)
26.8.3 Reduction of Environmental Interference
572(1)
26.9 Conclusions
573(1)
References
573(2)
27 Sensor Signal Conditioning for Biomedical Instrumentation
575(30)
Tomas E. Ward
27.1 Introduction
575(2)
27.2 Sensors
577(1)
27.3 Signal Conditioning
578(11)
27.3.1 The Operational Amplifier
578(1)
27.3.2 Signal Amplification with Operational Amplifiers
579(1)
27.3.2.1 Example: Piezoelectric Transducer Compensation
583(2)
27.3.3 The Instrumentation Amplifier
585(4)
27.4 The Analog-to-Digital Conversion Process
589(9)
27.4.1 The Sampling Process
590(1)
27.4.2 The Quantization Process
591(1)
27.4.3 Antialiasing Filters
592(5)
27.4.4 Oversampling and Decimation
597(1)
27.4.4.1 Oversampling
597(1)
27.4.4.2 Multisampling
597(1)
27.5 Integrated Solutions
598(2)
27.6 Isolation Circuits
600(2)
27.6.1 Methods of Isolation
600(1)
27.6.1.1 Capacitive Isolation Amplifiers
600(1)
27.6.1.2 Optical Isolation Amplifiers
600(1)
27.6.1.3 Magnetic Isolation Amplifiers
602(1)
27.6.1.4 Digital Isolation
602(1)
27.7 Conclusions
602(1)
References
603(2)
28 Sensor-Based Human Activity Recognition Techniques
605(16)
Donghai Guan
Weiwei Yuan
Sungyoung Lee
28.1 Introduction
606(1)
28.2 Video Sensor-Based Activity Recognition
607(4)
28.2.1 Video Sensor-Based Activity Recognition Applications
608(1)
28.2.2 Feature Extraction in Video Sensor-Based Activity Recognition
608(1)
28.2.2.1 Global Features of Video Sensor-Based Activity Recognition
609(1)
28.2.2.2 Local Features of Video Sensor-Based Activity Recognition
609(1)
28.2.3 Recognition Techniques in Video Sensor-Based Activity Recognition
610(1)
28.2.3.1 Nonparametric Techniques
610(1)
28.2.3.2 Volumetric Techniques
610(1)
28.2.3.3 Temporal-Independent Techniques
610(1)
28.2.3.4 Temporal-Based Techniques
611(1)
28.3 Wearable Sensor-Based Activity Recognition
611(3)
28.3.1 Applications of Wearable Sensor-Based Activity Recognition
611(1)
28.3.2 Sensors in Wearable Sensor-Based Activity Recognition
612(2)
28.3.3 Recognition Techniques for Wearable Sensor-Based Activity Recognition
614(1)
28.3.3.1 Supervised Recognition Techniques
614(1)
28.3.3.2 Unsupervised Recognition Techniques
614(1)
28.4 Object Usage-Based Activity Recognition
614(2)
28.4.1 Sensors in Object Usage-Based Activity Recognition
615(1)
28.4.1.1 Radio Frequency Identification-Based Sensors
615(1)
28.4.1.2 Binary Sensors
615(1)
28.4.2 Recognition Algorithms
615(1)
28.5 Comparisons of Video Sensor-Based, Wearable Sensor-Based, and Object Usage-Based Activity Recognition
616(2)
28.5.1 Video Sensor-Based Activity Recognition
616(1)
28.5.2 Wearable Sensor-Based Activity Recognition
616(1)
28.5.3 Object Usage-Based Activity Recognition
616(2)
28.6 Challenges in Sensor-Based Activity Recognition
618(1)
References
619(2)
29 Very Large-Scale Integration Bioinstrumentation Circuit Design and Nanopore Applications
621(18)
Jungsuk Kim
William B. Dunbar
29.1 Introduction
621(3)
29.1.1 Nanopore Method and Measurement
621(2)
29.1.2 Design Requirements
623(1)
29.2 Very Large-Scale Integration Bioinstrumentation Circuit Design
624(7)
29.2.1 Noise Analysis
624(2)
29.2.2 Low-Noise Core-Amplifier Design
626(2)
29.2.3 Dead-Time Compensation
628(2)
29.2.4 Input Offset Voltage Cancellation
630(1)
29.3 Implementation and Experimental Results
631(3)
29.4 Scalability and Multichannel Implementation
634(3)
References
637(2)
30 Wireless Electrical Impedance Tomography: LabVIEW-Based Automatic Electrode Switching
639(28)
Tushar Kanti Bera
30.1 Introduction
640(2)
30.2 Electrical Impedance Tomography Wireless Instrumentation
642(4)
30.2.1 Constant-Current Injector
642(1)
30.2.2 Electrode Switching in Electrical Impedance Tomography
642(1)
30.2.3 Electrode Switching Module
643(1)
30.2.4 WL-DDTS with Radio-Frequency Transmitter and Receiver
644(2)
30.3 Digital Logic for Electrode Switching in LabVIEW-Based Algorithms
646(1)
30.4 Electrode Protocols and Data Generation
646(4)
30.4.1 Neighboring Method
648(1)
30.4.2 Opposite Method
649(1)
30.5 Wireless Experimental Data Collection and Image Reconstruction
650(3)
30.5.1 Advantages and Disadvantages
653(1)
30.6 Mathematical Approach and Electrode Models
653(4)
30.6.1 Continuum Model
655(1)
30.6.2 Gap Model
656(1)
30.6.3 Shunt Model
656(1)
30.6.4 Complete Electrode Model
656(1)
30.7 Results and Discussion of Results
657(5)
30.8 Conclusions
662(1)
References
662(5)
Index 667
Halit Eren received his B.Eng, M.Eng, and Ph.D degrees from the University of Sheffield, UK, and MBA from Curtin University. He has worked as an instrumentation engineer, and held positions of assistant professorship at Hacettepe University and the Middle East Technical University; associate professorship at Hong Kong Polytechnic University; visiting professorship at the University of Wisconsin; and visiting scholar at the University of Sheffield. Dr. Eren has been conducting research and teaching at Curtin University for more than 30 years. He currently holds an adjunct senior research fellowship at the university. Widely published, Dr. Eren is a senior member of the IEEE.

John G. Webster received his BEE from Cornell University, and his MSEE and a Ph.D from the University of Rochester. Dr. Webster is currently professor emeritus of biomedical engineering at the University of Wisconsin. Previously, he was a highly cited researcher at King Abdulaziz University. Widely published, Dr. Webster is a fellow of the IEEE, ISA, AIMBE, BMES, and IOP. He has been a member of the IEEE Engineering in Medicine and Biology Society Administrative Committee and the NIH Surgery and Bioengineering Study Section, and the recipient of the IEEE Engineering in Medicine and Biology Career Achievement Award.
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