Table Of Contents
. -- Preface
. -- About The Companion Website
. -- 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
. -- 2 Physical, Physiological, Biological, And Behavioural States Of The Human Body
. -- 2.1 Introduction
. -- 2.2 Physical State Of The Human Body
. -- 2.3 Physiological State Of Human Body
. -- 2.4 Biological State Of Human Body
. -- 2.5 Psychological And Behavioural State Of The Human Body
. -- 2.6 Summary And Conclusions
. -- References
. -- 3 Physical, Physiological, And Biological Measurements
. -- 3.1 Introduction
. -- 3.2 Wearable Technology For Gait Monitoring
. -- 3.2.1 Accelerometer And Its Application To Gait Monitoring
. -- 3.2.1.1 How Accelerometers Operate
. -- 3.2.1.2 Accelerometers In Practice
. -- 3.2.2 Gyroscope And Imu
. -- 3.2.3 Force Plates
. -- 3.2.4 Goniometer
. -- 3.2.5 Electromyography
. -- 3.2.6 Sensing Fabric
. -- 3.3 Physiological Sensors
. -- 3.3.1 Multichannel Measurement Of The Nerves Electric Potentials
. -- 3.3.2 Other Sensors
. -- 3.4 Biological Sensors
. -- 3.4.1 The Structures Of Biological Sensors – The Principles
. -- 3.4.2 Emerging Biosensor Technologies
. -- 3.5 Conclusions
. -- References
. -- 4 Ambulatory And Popular Sensor Measurements 4.1 Introduction 4.2 Heart Rate 4.2.1 Hr During Physical Exercise 4.3 Respiration 4.4 Blood Oxygen Saturation Level 4.5 Blood Pressure 4.5.1 Cuffless Blood Pressure Measurement 4.6 Blood Glucose 4.7 Body Temperature 4.8 Commercial Sensors 4.9 Conclusions References 5 Polysomnography And Sleep Analysis 5.1 Introduction 5.2 Polysomnography 5.3 Sleep Stage Classification 5.3.1 Sleep Stages 5.3.2 Eeg-Based Classification Of Sleep Stages 5.3.2.1 Time Domain Features 5.3.2.2 Frequency Domain Features 5.3.2.3 Time-Frequency Domain Features 5.3.2.4 Short-Time Fourier Transform 5.3.2.5 Wavelet Transform 5.3.2.6 Matching Pursuit 5.3.2.7 Empirical Mode Decomposition 5.3.2.8 Nonlinear Features 5.3.3 Classification Techniques 5.3.3.1 Using Neural Networks 5.3.3.2 Application Of Cnns 5.3.4 Sleep Stage Scoring Using Cnn 5.4 Monitoring Movements And Body Position During Sleep 5.5 Conclusions References 6 Noninvasive, Intrusive, And Nonintrusive Measurements 6.1 Introduction 6.2 Noninvasive Monitoring 6.3 Contactless Monitoring 6.3.1 Remote Photoplethysmography 6.3.1.1 Derivation Of Remote Ppg 6.3.2 Spectral Analysis Using Autoregressive Modelling 6.3.3 Estimation Of Physiological Parameters Using Remote Ppg 6.3.3.1 Heart Rate Estimation 6.3.3.2 Respiratory Rate Estimation 6.3.3.3 Blood Oxygen Saturation Level Estimation 6.3.3.4 Pulse Transmit Time Estimation 6.3.3.5 Video Pre-Processing 6.3.3.6 Selection Of Regions Of Interest 6.3.3.7 Derivation Of The Rppg Signal 6.3.3.8 Processing Rppg Signals
. -- 6.3.3.9 Calculation Of Rptt/Dptt 6.4 Implantable Sensor Systems 6.5 Conclusions References 7 Single And Multiple Sensor Networking For Gait Analysis 7.1 Introduction 7.2 Gait Events And Parameters 7.2.1 Gait Events 7.2.2 Gait Parameters 7.2.2.1 Temporal Gait Parameters 7.2.2.2 Spatial Gait Parameters 7.2.2.3 Kinetic Gait Parameters 7.2.2.4 Kinematic Gait Parameters 7.3 Standard Gait Measurement Systems 7.3.1 Foot Plantar Pressure System 7.3.2 Force-Plate Measurement System 7.3.3 Optical Motion Capture Systems 7.3.4 Microsoft Kinect Image And Depth Sensors 7.4 Wearable Sensors For Gait Analysis 7.4.1 Single Sensor Platforms 7.4.2 Multiple Sensor Platforms 7.5 Gait Analysis Algorithms Based On Accelerometer/Gyroscope 7.5.1 Estimation Of Gait Events 7.5.2 Estimation Of Gait Parameters 7.5.2.1 Estimation Of Orientation 7.5.2.2 Estimating Angles Using Accelerometers 7.5.2.3 Estimating Angles Using Gyroscopes 7.5.2.4 Fusing Accelerometer And Gyroscope Data 7.5.2.5 Quaternion Based Estimation Of Orientation 7.5.2.6 Step Length Estimation 7.6 Conclusions 8 Popular Health Monitoring Systems 8.1 Introduction 8.2 Technology For Data Acquisition 8.3 Physiological Health Monitoring Technologies 8.3.1 Predicting Patient Deterioration 8.3.2 Ambient Assisted Living: Monitoring Daily Living Activities 8.3.3 Monitoring Chronic Obstructive Pulmonary Disease Patients 8.3.4 Movement Tracking And Fall Detection/Prevention 8.3.5 Monitoring Patients With Dementia 8.3.6 Monitoring Patients With Parkinson’S Disease 8.3.7 Odour Sensitivity Measurement 8.4 Conclusions References 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 9.5.7 Eye-Blink Artefact Removal From Eeg Signals 9.5.8 Seizure Detection 9.5.9 Bci Applications 9.6 Conclusions References 10 Signal Processing For Sensor Networks 10.1 Introduction 10.2 Signal Processing Problems For Sensor Networks 10.3 Fundamental Concepts In Signal Processing 10.3.1 Nonlinearity Of The Medium 10.3.2 Nonstationarity 10.3.3 Signal Segmentation 10.3.4 Signal Filtering 10.4 Mathematical Data Models 10.4.1 Linear Models 10.4.1.1 Prediction Method
. -- 10.4.1.2 Prony’S Method 10.4.1.3 Singular Spectrum Analysis 10.4.2 Nonlinear Modelling 10.4.3 Gaussian Mixture Model 10.5 Transform Domain Signal Analysis 10.6 Time-Frequency Domain Transforms 10.6.1 Short-Time Fourier Transform 10.6.2 Wavelet Transform 10.6.2.1 Continuous Wavelet Transform 10.6.2.2 Examples Of Continuous Wavelets 10.6.2.3 Discrete Time Wavelet Transform 10.6.3 Multiresolution Analysis 10.6.4 Synchro-Squeezing Wavelet Transform 10.7 Adaptive Filtering 10.8 Cooperative Adaptive Filtering 10.8.1 Diffusion Adaptation 10.9 Multichannel Signal Processing 10.9.1 Instantaneous And Convolutive Bss Problems 10.9.2 Array Processing 10.10 Signal Processing Platforms For Bans 10.11 Conclusions References 11 Communication Systems For Body Area Networks 11.1 Introduction 11.2 Short-Range Communication Systems 11.2.1 Bluetooth 11.2.2 Wi-Fi 11.2.3 Zigbee 11.2.4 Radio Frequency Identification Devices 11.2.5 Ultrawideband 11.2.6 Other Short-Range Communication Methods 11.2.7 Rf Modules Available In Market 11.3 Limitations, Interferences, Noise, And Artefacts 11.4 Channel Modelling 11.4.1 Ban Propagation Scenarios 11.4.1.1 On-Body Channel 11.4.1.2 In-Body Channel 11.4.1.3 Off-Body Channel 11.4.1.4 Body-To-Body (Or Interference) Channel 11.4.2 Recent Approaches To Ban Channel Modelling 11.4.3 Propagation Models 11.4.4 Standards And Guidelines 11.5 Ban-Wsn Communications 11.6 Routing In Wban 11.6.1 Posture-Based Routing 11.6.2 Temperature-Based Routing 11.6.3 Cross-Layer Routing 11.6.4 Cluster-Based Routing 11.6.5 Qos-Based Routing 11.7 Ban-Building Network Integration 11.8 Cooperative Bans
. -- 11.9 Ban Security 11.10 Conclusions References 12 Energy Harvesting Enabled Body Sensor Networks 12.1 Introduction 12.2 Energy Conservation 12.3 Network Capacity 12.4 Energy Harvesting 12.5 Challenges In Energy Harvesting 12.6 Types Of Energy Harvesting 12.6.1 Harvesting Energy From Kinetic Sources 12.6.2 Energy Sources From Radiant Sources 12.6.3 Energy Harvesting From Thermal Sources 12.6.4 Energy Harvesting From Biochemical And Chemical Sources 12.7 Topology Control 12.8 Typical Energy Harvesters For Bsns 12.9 Predicting Availability Of Energy 12.10 Reliability Of Energy Storage 12.11 Conclusions References 13 Quality Of Service, Security, And Privacy For Wearable Sensor Data 325 13.1 Introduction 13.2 Threats To a Ban 13.2.1 Denial-Of-Service 13.2.2 Man-In-The-Middle Attack 13.2.3 Phishing And Spear Phishing Attacks 13.2.4 Drive-By Attack 13.2.5 Password Attack 13.2.6 Sql Injection Attack 13.2.7 Cross-Site Scripting Attack 13.2.8 Eavesdropping 13.2.9 Birthday Attack 13.2.10 Malware Attack 13.3 Data Security And Most Common Encryption Methods 13.3.1 Data Encryption Standard (Des) 13.3.2 Triple Des 13.3.3 Rivest–Shamir–Adleman (Rsa) 13.3.4 Advanced Encryption Standard (Aes) 13.3.5 Twofish 13.4 Quality Of Service (Qos) 13.4.1 Quantification Of Qos 13.4.1.1 Data Quality Metrics 13.4.1.2 Network Quality Related Metrics 13.5 System Security 13.6 Privacy 13.7 Conclusions References 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 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