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E-raamat: Wireless Sensor Networks for Healthcare Applications

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  • Formaat: 262 pages
  • Ilmumisaeg: 31-Jan-2009
  • Kirjastus: Artech House Publishers
  • ISBN-13: 9781596933064
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Wireless Sensor Networks for Healthcare ApplicationsSuurem pilt
  • Formaat: 262 pages
  • Ilmumisaeg: 31-Jan-2009
  • Kirjastus: Artech House Publishers
  • ISBN-13: 9781596933064
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Unlike other books on wireless sensors networks that are geared to the healthcare industry, this unique reference focuses on methods of application, validation, and testing based on real deployments of sensor networks in the clinical and home environments. Moreover, this insightful resource looks at future trends in healthcare and the potential impact of body sensor networks to those trends. Written by industry experts, this volume gives professionals the practical knowledge they need to design and develop wireless sensor networks that meet today's demanding industry challenges and that have the potential to improve healthcare systems in the years to come. Electrical engineers, researchers, application developers and designers whose work involves sensor networks, as well as graduate and postdoctoral students.
Acknowledgments xi
Healthcare and the Wireless Sensor Network
1(12)
Introduction
1(1)
Structure
1(1)
The Demographic Context
2(3)
The Potential of Technology
5(4)
Sensor Networks for At-Home Care
6(1)
Wireless Biomedical Sensor Networks
7(1)
Value to Clinicians and Caregivers
8(1)
Benefits of At-Home WSNs
8(1)
General Approach to WSN in Healthcare
9(2)
Key Principles
9(1)
Methodology
9(2)
Conclusion
11(2)
References
11(2)
Sensor Network Technologies
13(32)
Introduction
13(1)
Wireless Sensor Networks
13(3)
Network Architectures
14(2)
TCP/IP and WSNs
16(1)
WSN Technologies
16(20)
Motes
18(1)
MICA
19(1)
iMote
20(1)
Microcontrollers
20(4)
Radio Transceivers
24(1)
Radios for WSN Applications
25(4)
System-on-Chip
29(1)
Antenna Designs for Wireless Sensors
29(4)
Operating Systems
33(1)
Sensors and Actuators for Healthcare WSNs
34(2)
Conclusion
36(9)
References
39(6)
Informing Your Design
45(18)
Introduction
45(1)
Clinician Requirements
46(3)
Data to Be Collected
46(1)
Information Reporting
46(1)
Subject Interaction
47(1)
Environment
47(1)
Sample CRD Contents
48(1)
End User Modeling
49(3)
User Definition: The Role of Ethnography
49(1)
Ethnographic Modeling
50(1)
Ethnography: Conclusion
51(1)
Usage Modeling
52(2)
The Usage Modeling Process
52(2)
Benefits of Usage Modeling
54(1)
Requirements
54(1)
Use Cases
54(1)
Failure Modes and Effects Analysis
55(4)
FMEA Example #1
56(1)
FMEA Example #2
56(3)
Conclusion
59(1)
Field Experience: Furniture Cruising
60(3)
References
61(1)
Select Bibliography
61(2)
Technology Selection
63(32)
Introduction
63(1)
Practical Guidelines for Architecting WSN Solutions for Healthcare
63(4)
Generalized WSN Architecture for Healthcare
64(1)
Literature Highlights: Architectural Models
65(2)
From Requirements Statement to Technology Selection
67(1)
Hardware Choices
68(8)
Selection Criteria
68(3)
Relevant Clinical Research
71(1)
Off-the-Shelf, or Bespoke?
72(1)
Two-Chip or Single-Chip?
72(1)
Documentation Is Essential: The PDRD
73(2)
Hardware Prototyping and Design Review
75(1)
Firmware Choices
76(6)
RTOS or Simple Scheduler?
76(1)
Operating System
76(1)
TinyOS
76(5)
Communications Standards: ISO/IEEE 11073
81(1)
Software Choices
82(3)
Software Considerations
82(2)
Programming Languages
84(1)
IDE and Compilers
84(1)
Transparency of Source Code
84(1)
Data Management
84(1)
Conclusion
85(1)
Field Experience #1: Radio Enclosures
85(4)
Field Experience #2: Bluetooth Testing
89(6)
Introduction
89(1)
Experimental Process
89(3)
Results
92(1)
Conclusions
92(1)
References
93(1)
Useful Links
94(1)
Data Collection and Decision Making
95(12)
Introduction to Inference Modeling
95(2)
Categories of Inference Engines
96(1)
Limitations of Predictive Analytics
97(1)
Static Rules-Based Models
97(3)
Example of a Static Rules-Based Application
99(1)
Statistical Probability Models
100(1)
Bayesian and Markov Models
100(7)
Field Experiences ADL Applications
104(1)
References
105(2)
Deploying in the Field
107(18)
Introduction
107(1)
Planning
107(1)
Testing
108(5)
Bench Testing
109(1)
Lab Testing
110(1)
Friendly Environment Test
111(1)
Ethical Review and Labeling
111(2)
Premarket Testing
113(1)
Documentation
113(1)
Preinstall
113(1)
Installation
114(1)
Maintenance
115(1)
Teardown
116(1)
Field Experience
117(8)
Planning
117(1)
Choice of Radio
117(3)
Installation
120(1)
Building Materials
120(2)
Participant Tests
122(1)
Human Frailty
123(1)
Fluorescent Lamps and Infrared
123(1)
References
123(2)
Clinical Deployments of Wireless Sensor Networks: Gait
125(26)
Introduction
125(1)
Clinical Problem Statement
125(1)
Clinical Research Objective
126(1)
Technology Objective
126(1)
Clinician Requirements
127(5)
User Definitions and Permissions
127(1)
Clinical Parameters
128(2)
Data Collection and Storage
130(1)
Data Analysis and Reporting
130(2)
Subject Interaction
132(1)
Ethnography and Usage Modeling
132(1)
Environmental Issues
133(1)
Technology Selection Criteria
133(1)
Technology Selection
134(2)
Device
135(1)
Sensor Technology
135(1)
Radio
135(1)
Footfall Mapping Technology
135(1)
Video Cameras and System Layout
136(1)
Software
136(1)
Prototype Definitions Requirements Document
136(9)
Purpose of PDRD
137(1)
System Description: Footfall Sensor
137(4)
System Description: Body-Worn Sensors
141(2)
System Description: Software
143(2)
System Description: Video
145(1)
System Description: Miscellaneous Sensors
145(1)
System Validation
145(2)
Conclusion
147(4)
References
147(4)
Contextual Awareness Medication Prompting Field Trials in Homes
151(54)
Introduction
151(1)
Problem Statement
151(1)
Medication Reminders
152(1)
Research Objective
152(1)
Ethnographic Research on Medication Routines
153(1)
Probe Study: Three Existing Medication Reminders
154(6)
Probe Study Participants
156(1)
Probe Study Procedure
156(2)
Probe Study Results and Discussion
158(1)
Device Preferences
158(2)
Collaborative Design
160(2)
Ethnographic, Probe Study, and Collaborative Design Results
162(1)
Use Cases
162(1)
Use Case #1
162(1)
Use Case #2
163(1)
Use Case #3
163(1)
Technical Design
163(1)
Technology Selection
163(3)
Prototype Definition Requirements Document
166(19)
System Description: iMedTracker
166(8)
System Description: Health SPOT
174(6)
System Description: Activity Beacon
180(2)
System Description: Phone Sensor
182(1)
System Description: Bed Sensor
183(1)
System Description: Motion Sensor
184(1)
System Description: Door Sensor
185(1)
Software: The Inference Engine
185(5)
The Total Set of Activities to Be Detected or Inferred
185(1)
Activities Affecting Adherence
186(1)
Activities Affecting Ability to Respond to Prompts
186(1)
Other Significant Effects to Detect
186(1)
Some Candidate Effects Not Detected
186(1)
Sensors and Actuators to Be Deployed
187(1)
Types of Inference
187(3)
Reasoning System for Context-Aware Prompting
190(3)
Explanation of Location Tracking Using the Health SPOT Watch
193(7)
Literature Review
194(1)
RSSI and BER for Location
195(1)
Health SPOT Device Construction and Software
195(5)
Prompting Stack Pseudocode
200(1)
Conclusion
200(5)
References
201(4)
Case Study: Social Health
205(20)
Introduction
205(1)
Clinical Problem Statement
205(1)
Clinical Research Objective
206(1)
Technology Objectives
206(1)
System Architecture
207(1)
Requirements Capture and User Modeling
207(4)
Clinical Requirements
208(1)
Usage Models
209(1)
Data to Be Collected
210(1)
Subject Interaction
210(1)
Environment
211(1)
Technology Selection Criteria
211(1)
Technology Selection
212(7)
Mote
212(1)
Door Sensors
212(1)
Motion Sensors
212(1)
Location Sensors
213(2)
Presence Lamp
215(1)
Software
215(4)
Deployment
219(3)
Radio Enclosures
219(1)
Infrared Location Tracking Issues
220(1)
Building Materials
221(1)
Pets
222(1)
Every House Is Different
222(1)
Results
222(1)
Conclusion
223(2)
References
223(1)
Select Bibliography
223(2)
Future of Wireless Sensor Networks for Healthcare
225(14)
Introduction
225(1)
Noncontact Sensing: The Burnfoot Project
225(9)
Incorporation of Derivation Findings into Burnfoot Sensor Simulations
230(1)
Potential Applications
231(2)
Burnfoot Validation
233(1)
Using Radio Frequency for the Biosignals Data Collection
234(1)
Leveraging the Doppler Effect
235(1)
Movement to Standardized Radios for WSN
235(1)
Ubiquitous Displays
236(1)
Conclusion
236(3)
References
236(3)
Index 239
Terrance Dishongh is the principle engineer and lead technologist in the Digital Health Research and Innovation Group at Intel Corporation. He received his M.S. from the University of Tennessee, Knoxville and his Ph.D. from the University of Arizona. Michael McGrath is a researcher in IT Research at Intel Corporation. He received his Ph.D. in sensors and instrumentation from Dublin City University. Dr. McGrath is also a chartered chemist and chartered scientist. Benjamin Kuris is a lead hardware engineer in the Digital Health Group and Intel Corporation. He received his B.S. in electrical engineering from Yale Univeristy.
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