Body Sensor Networking, Design and Algorithms (Hardcover)

· 제목 : Body Sensor Networking, Design and Algorithms (Hardcover) 
· 분류 : 외국도서 > 기술공학 > 기술공학 > 신호/신호처리
· ISBN : 9781119390022
· 쪽수 : 416쪽
· 출판일 : 2020-07-13

PREFACE

CHAPTER 1 INTRODUCTION

1.1 History of Wearable Technology

1.2 Introduction to BSN Technology

1.3 BSN Architecture

1.4 Layout of the Book

References

CHAPTER 2 PHYSICAL, PHYSIOLOGICAL, BIOLOGICAL AND BEHAVIORAL STATES OF HUMAN BODY

2.1 Physical State of Human Body

2.2 Physiological State of Human Body

2.3 Biological State of Human Body

2.4 Psychological and Behavioural State of Human Body

2.5 Summary and Conclusions

References

CHAPTER 3 PHYSICAL, PHYSIOLOGICAL, AND BIOLOGICAL MEASUREMENTS

3.1 Wearable Technology for Gait Monitoring

3.1.1 Accelerometer and its Application to Gait Monitoring

3.1.1.1 How accelerometers operate

3.1.1.2 Accelerometers in practice

3.1.2 Gyroscope and IMU

3.1.3 Force Plates

3.1.4 Goniometer

3.1.5 Electromyography

3.1.6 Sensing Fabric

3.2 Physiological Sensors

3.2.1 Multichannel Measurement of the Nerves Electric Potentials

3.2.2 Other sensors

3.3 Biological Sensors

3.3.1 The Structures of Biological Sensors – the Principles

3.3.2 Emerging Biosensor Technologies

3.4 Conclusions

References

CHAPTER 4 AMBULATORY AND POPULAR SENSOR MEASUREMENTS

4.1 Heart Rate

4.1.1. HR During Physical Exercise

4.2 Respiration

4.3 Blood Oxygen Saturation Level

4.4 Blood Pressure

4.4.1 Cuffless Blood Pressure Measurement

4.5 Blood Glucose

4.6 Body Temperature

4.7 Commercial Sensors

4.8 Conclusions

References

CHAPETR 5 POLYSOMNOGRAPHY AND SLEEP ANALYSIS

5.1 Polysomnography

5.2 Sleep Stage Classification

5.2.1. Sleep Stages

5.2.2 EEG Based Classification of Sleep Stages

5.2.2.1 Time domain features

5.2.2.2 Frequency domain features

5.2.2.3 Time-frequency domain features

5.2.2.4 Short-time Fourier transform

5.2.2.5 Wavelet transform

5.2.2.6 Matching pursuit

5.2.2.7 Empirical mode decomposition

5.2.2.8 Nonlinear features

5.2.3 Classification Techniques

5.2.3.1 Using neural networks

5.2.3.2 Application of CNNs

5.2.4 Sleep Stage Scoring using CNN

5.3 Monitoring Movements and Body Position During Sleep

5.4 Conclusions

References

CHAPTER 6 NONINVASIVE, INTRUSIVE AND NONINTRUSIVE MEASUREMENTS

6.1 Noninvasive Monitoring

6.2 Contactless Monitoring

6.2.1 Remote Photoplethysmography

6.2.2 Spectral Analysis Using Autoregressive Modelling

6.2.3 Estimation of Physiological Parameters Using Remote PPG

6.2.3.1 Heart rate estimation

6.2.3.2 Respiratory rate estimation

6.2.3.3 Blood oxygen saturation level estimation

6.2.3.4 Pulse transmit time estimation

6.2.3.5 Video pre-processing

6.2.3.6 Selection of ROI

6.2.3.7 Derivation of rPPG signal

6.2.3.8 Processing of rPPG signals

6.2.3.9 Calculation of rPPT/dPPT

6.3 Implantable Sensor Systems

6.4 Conclusions

References

CHAPTER 7 SINGLE AND MULTIPLE SENSOR NETWROKING FOR GAIT ANALYSIS

7.1 Gait Events and Parameters

7.1.1 Gait Events

7.1.2 Gait Parameters

7.1.2.1 Temporal gait parameters

7.1.2.2 Spatial gait parameters

7.1.2.3 Kinetic gait parameters

7.1.2.4 Kinematic gait parameters

7.2 Standard Gait Measurement Systems

7.2.1 Foot Plantar Pressure System

7.2.2 Force-Plate Measurement System

7.2.3 Optical Motion Capture Systems

7.2.4 Microsoft Kinect Image and Depth Sensors

7.3. Wearable Sensors for Gait Analysis

7.3.1 Single Sensor Platforms

7.3.1 Multiple Sensor Platforms

7.4 Gait Analysis Algorithms Based on Accelerometer/Gyroscope

7.4.1 Estimation of Gait Events

7.4.2 Estimation of Gait Parameters

7.4.2.1 Estimation of orientation

7.4.2.2 Estimating angles using accelerometers

7.4.2.3 Estimating angles using gyroscopes

7.4.2.4 Fusing accelerometer and gyroscope data

7.4.2.5 Quaternion based estimation of orientation

7.4.2.6 Step length estimation

7.5 Conclusion

References

CHAPTER 8 POPULAR HEALTH MONITORING SYSTEMS

8.1 Technology for Data Acquisition

8.2 Physiological Health Monitoring Technologies

8.2.1 Predicting Patient Deterioration

8.2.2 Ambient Assisted Living; Monitoring Daily Living Activities

8.2.3 Monitoring chronic obstructive pulmonary disease patients

8.2.4 Movement Tracking and Fall Detection/Prevention

8.2.5 Monitoring Patients with Dementia

8.2.6 Monitoring Patients with Parkinson’s Disease

8.2.7 Odor Sensitivity Measurement

8.3 Conclusions

References

CHAPTER 9 MACHINE LEARNING FOR SENSOR NETWORKS

9.1 Introduction

9.2 Clustering Approaches

9.2.1 k-means Clustering Algorithm

9.2.2 Iterative Self-organising Data Analysis Technique

9.2.3 Gap Statistics

9.2.4 Density based Clustering

9.2.5 Affinity based Clustering

9.2.6 Deep Clustering

9.2.7 Semi-supervised Clustering

9.2.7.1 Basic Semi-supervised techniques

9.2.7.2 Deep semi-supervised techniques

9.2.8 Fuzzy Clustering

9.3 Classification Algorithms

9.3.1 Decision Trees

9.3.2 Random Forest

9.3.3 Linear Discriminant Analysis

9.3.4 Support Vector Machines

9.3.5 K-Nearest Neighbour

9.3.6 Gaussian Mixture Model

9.3.7 Logistic Regression

9.3.8 Reinforcement Learning

9.3.9 Artificial Neural Networks

9.3.9.1 Deep neural networks

9.3.9.2 Convolutional neural networks

9.3.9.3 Recent DNN approaches

9.3.10 Gaussian Processes

9.3.11 Neural Processes

9.3.12 Graph Convolutional Networks

9.3.13 Naïve Bayes Classifier

9.3.14 Hidden Markov Model

9.3.14.1 Forward algorithm

9.3.14.2 Backward algorithm

9.3.14.3 HMM design

9.4 Common Spatial Patterns

9.5 Applications of Machine Learning in BSNs and WSNs

9.5.1 Human Activity Detection

9.5.2 Scoring Sleep Stages

9.5.3 Fault Detection

9.5.4 Gas Pipeline Leakage Detection

9.5.5 Measuring Pollution Level

9.5.6 Fatigue-Tracking and Classification System

9.6 Conclusions

References

CHAPTER 10 SIGNAL PROCESSING FOR SENSOR NETWORKS

10.1 Signal Processing Problems for Sensor Networks

10.2 Fundamental Concepts in Signal Processing

10.2.1 Nonlinearity of the Medium

10.2.2 Nonstationarity

10.2.3 Signal Segmentation

10.2.4 Signal Filtering

10.3 Mathematical Data Models

10.3.1 Linear Models

10.3.1.1 Prediction method

10.3.1.2 Prony’s method

10.3.1.3 Singular spectrum analysis

10.3.2 Nonlinear Modelling

10.3.3 Gaussian Mixture Model

10.4 Transform Domain Signal Analysis

10.5 Time – Frequency Domain Transforms

10.5.2 Wavelet Transform

10.5.2.1 Continuous wavelet transform

10.5.2.2 Examples of continuous wavelets

10.5.2.3 Discrete time wavelet transform

10.5.3 Multiresolution Analysis

10.5.4 Synchro-squeezing WT

10.6 Adaptive Filtering

10.7 Cooperative Adaptive Filtering

 10.8.1 Diffusion Adaptation

10.8 Multichannel Signal Processing

10.8.1 Instantaneous and Convolutive BSS Problems

10.8.2 Array Processing

10.9 Signal Processing Platforms for BAN

10.10 Conclusions

References

CHAPTER 11 COMMUNICATION SYSTEMS FOR BODY AREA NETWORKS

11.1 Short Range Communication Techniques

11.1.1 Bluetooth

11.1.2 Wi-Fi

11.1.3 ZigBee

11.1.4 RFID

11.1.5 Ultrawideband

11.1.6 Other Short-Range Communication Methods

11.1.7 RF Modules Available in Market

11.2 Limitations, Interferences, Noise, and Artefacts

11.3 Channel Modelling

11.3.1 BAN Propagation Scenarios

11.3.1.1 On-Body Channel

11.3.1.2 In-Body Channel

11.3.1.3 Off-Body Channel

11.3.1.4 Body-to-Body (or Interference) Channel

11.3.2 Recent Approaches to BAN Channel Modelling

11.3.3 Propagation Models

11.3.4 Standards and Guidelines

11.4 BAN-WSN Communications

11.5 Routing in WBAN

11.5.1 Posture-based Routing

11.5.2 Temperature-based Routing

11.5.3 Cross-layer Routing

11.5.4 Cluster-based Routing

11.5.5 QoS-based Routing

11.6 BAN-Building Network Integration

11.7 Cooperative BANs

11.8 BAN Security

11.9 Conclusions

References

CHAPTER 12 ENERGY HARVESTING ENABLED BODY SENSOR NETWORKS

12.1 Energy Conservation

12.2 Network Capacity

12.3 Energy Harvesting

12.4 Challenges in Energy Harvesting

12.5 Types of Energy Harvesting

12.5.1 Harvesting Energy from Kinetic Sources

12.5.2 Energy Sources from Radiant Sources

12.5.3 Energy Harvesting from Thermal Sources

12.5.4 Energy Harvesting from Biochemical and Chemical Sources

12.6 Topology Control

12.7 Typical Energy Harvesters for BSNs

12.8 Predicting Availability of Energy

12.9 Reliability of Energy Storage

12.10 Conclusions

References

CHAPTER 13 QUALITY OF SERVICE, SECURITY, AND PRIVACY FOR WEARABLE SENSOR DATA

13.1 Threats to a BAN

13.1.1 Denial-of-Service

13.1.2 Man-in-the-Middle Attack

13.1.3 Phishing and Spear Phishing Attacks

13.1.4 Drive-by Attack

13.1.5 Password Attack

13.1.6 SQL Injection Attack

13.1.7 Cross-site Scripting Attack

13.1.8 Eavesdropping

13.1.9 Birthday Attack

13.1.10 Malware Attack

13.2 Data Security and Most Common Encryption Methods

13.2.1 DES

13.2.2 Triple DES

13.2.3 RSA

13.2.4 Advanced Encryption Standard

13.2.5 Twofish

13.3 Quality of Service

13.3.1 Quantification of QoS

13.3.1.1 Data quality metrics

13.3.1.2 Network quality related metrics

13.4 System Security

13.5 Privacy

13.6 Conclusions

References

CHAPTER 14 EXISTING PROJECTS AND PLATFORMS

14.1 Introduction

14.2 Existing Wearable Devices

14.3 BAN Programming Framework

14.4 Commercial Sensor Node Hardware Platforms

14.4.1 Mica2/MicaZ Motes

14.4.2 TelosB Mote

14.4.3 Indriya-Zigbee Based Platform

14.4.4 IRIS

14.4.5 iSense Core Wireless Module

14.4.6 Preon32-Wireless Module

14.4.7 Wasp Mote

14.4.8 WiSense Mote

14.4.9 panStamp NRG Mote

14.4.10 Jennic JN5139

14.5 BAN Software Platforms

14.5.1 Titan

14.5.2 CodeBlue

14.5.3 RehabSPOT

14.5.4 SPINE and SPINE2

14.5.5 C-SPINE

14.5.6 MAPS

14.5.7 DexterNet

14.6 Popular BAN Application Domains

14.7 Conclusions

References

CHAPTER 15 CONCLUSIONS AND SUGGESTIONS FOR FUTURE RESEARCH

15.1 Summary

15.2 Future Directions in BSN Research

15.2.1 Smart Sensors; Intelligent, Biocompatible, and Wearable

15.2.2 Big Data Problem

15.2.3 Data Processing and Machine Learning

15.2.4 Decentralised and Cooperative Networks

15.2.5 Personalised Medicine through Personalised Technology

15.2.6 Fitting BSN to 4G and 5G Communication Systems

15.2.7 Emerging Assistive Technology Applications

15.2.8 Solving Problems with Energy Harvesting

15.2.9 Virtual World

15.3 Conclusions

References

Index