A complete guide to the state of the art theoretical and manufacturing developments of body sensor network, design, and algorithms

InBody Sensor Networking, Design, and Algorithms, professionals in the field of Biomedical Engineering and e-health get an in-depth look at advancements, changes, and developments. When it comes to advances in the industry, the text looks at cooperative networks, noninvasive and implantable sensor microelectronics, wireless sensor networks, platforms, and optimizationto name a few.

Each chapter provides essential information needed to understand the current landscape of technology and mechanical developments. It covers subjects including Physiological Sensors, Sleep Stage Classification, Contactless Monitoring, and much more.

Among the many topics covered, the text also includes additions such as:

Additionally, readers will learn about wearable and implantable devices, invasive and noninvasive monitoring, biocompatibility, and the tools and platforms for long-term, low-power deployment of wireless communications. Its an essential resource for understanding the applications and practical implementation of BSN when it comes to elderly care, how to manage patients with chronic illnesses and diseases, and use cases for rehabilitation.

Saeid Sanei is a Professor of Biomedical Signal Processing and Machine Learning at Nottingham Trent University and a Visiting Professor to Imperial College London, in the United Kingdom. His major contributions in advanced signal processing techniques such as tensor factorization, cooperative networking, compressive sensing, statistical signal processing, and subspace analysis have applications in physiological signal processing and sensor networks as explored in his three published monograms and over 400 publications.

Delaram Jarchi is currently a Lecturer at Essex University. She has been working intensively on sensor networks design and algorithms levels. Her research is focused on designing new algorithms and validation of commercial wearable sensors for robust estimation of physiological parameters such as heart rate, respiratory rate and blood oxygen saturation levels in very unobtrusive ways. She is a senior member of IEEE since 2018.

Anthony G. Constantinides is a Professor at Imperial College of London UK. He is an IEEE acknowledged pioneer in signal processing with research interests that span a wide range of applications of the area. Amongst these and relevant to the present book are included topics such as data analytics, acquisition, sensing, transmission, and compression.

Preface xiii

About the Companion Website xv

1 Introduction1

1.1 History of Wearable Technology 1

1.2 Introduction to BSN Technology 2

1.3 BSN Architecture 7

1.4 Layout of the Book 10

References 11

2 Physical, Physiological, Biological, and Behavioural States of the Human Body17

2.1 Introduction 17

2.2 Physical State of the Human Body 17

2.3 Physiological State of Human Body 19

2.4 Biological State of Human Body 23

2.5 Psychological and Behavioural State of the Human Body 24

2.6 Summary and Conclusions 30

References 31

3 Physical, Physiological, and Biological Measurements35

3.1 Introduction 35

3.2 Wearable Technology for Gait Monitoring 35

3.2.1 Accelerometer and Its Application to Gait Monitoring 36

3.2.1.1 How Accelerometers Operate 37

3.2.1.2 Accelerometers in Practice 39

3.2.2 Gyroscope and IMU 40

3.2.3 Force Plates 41

3.2.4 Goniometer 41

3.2.5 Electromyography 41

3.2.6 Sensing Fabric 42

3.3 Physiological Sensors 42

3.3.1 Multichannel Measurement of the Nerves Electric Potentials 42

3.3.2 Other Sensors 45

3.4 Biological Sensors 48

3.4.1 The Structures of Biological Sensors The Principles 48

3.4.2 Emerging Biosensor Technologies 51

3.5 Conclusions 51

References 53

4 Ambulatory and Popular Sensor Measurements59

4.1 Introduction 59

4.2 Heart Rate 59

4.2.1 HR During Physical Exercise 60

4.3 Respiration 62

4.4 Blood Oxygen Saturation Level 67

4.5 Blood Pressure 70

4.5.1 Cuffless Blood Pressure Measurement 71

4.6 Blood Glucose 72

4.7 Body Temperature 73

4.8 Commercial Sensors 74

4.9 Conclusions 75

References 76

5 Polysomnography and Sleep Analysis83

5.1 Introduction 83

5.2 Polysomnography 84

5.3 Sleep Stage Classification 85

5.3.1 Sleep Stages 85

5.3.2 EEG-Based Classification of Sleep Stages 86

5.3.2.1 Time Domain Features 86

5.3.2.2 Frequency Domain Features 87

5.3.2.3 Time-frequency Domain Features 87

5.3.2.4 Short-time Fourier Transform 88

5.3.2.5 Wavelet Transform 88

5.3.2.6 Matching Pursuit 88

5.3.2.7 Empirical Mode Decomposition 89

5.3.2.8 Nonlinear Features 89

5.3.3 Classification Techniques 90

5.3.3.1 Using Neural Networks 90

5.3.3.2 Application of CNNs 92

5.3.4 Sleep Stage Scoring Using CNN 94

5.4 Monitoring Movements and Body Position During Sleep 96

5.5 Conclusions 99

References 100

6 Noninvasive, Intrusive, and Nonintrusive Measurements107

6.1 Introduction 107

6.2 Noninvasive Monitoring 107

6.3 Contactless Monitoring 109

6.3.1 Remote Photoplethysmography 109

6.3.1.1 Derivation of Remote PPG 110

6.3.2 Spectral Analysis Using Autoregressive Modelling 111

6.3.3 Estimation of Physiological Parameters Using Remote PPG 114

6.3.3.1 Heart Rate Estimation 114

6.3.3.2 Respiratory Rate Estimation 116

6.3.3.3 Blood Oxygen Saturation Level Estimation 117

6.3.3.4 Pulse Transmit Time Estimation 118

6.3.3.5 Video Pre-processing 119

6.3.3.6 Selection of Regions of Interest 120

6.3.3.7 Derivation of the rPPG Signal 120

6.3.3.8 Processing rPPG Signals 120

6.3.3.9 Calculation of rPTT/dPTT 121

6.4 Implantable Sensor Systems 122

6.5 Conclusions 123

References 124

7 Single and Multiple Sensor Networking for Gait Analysis129

7.1 Introduction 129

7.2 Gait Events and Parameters 129

7.2.1 Gait Events 129

7.2.2 Gait Parameters 130

7.2.2.1 Temporal Gait Parameters 130

7.2.2.2 Spatial Gait Parameters 132

7.2.2.3 Kinetic Gait Parameters 133

7.2.2.4 Kinematic Gait Parameters 133

7.3 Standard Gait Measurement Systems 135

7.3.1 Foot Plantar Pressure System 135

7.3.2 Force-plate Measurement System 135

7.3.3 Optical Motion Capture Systems 137

7.3.4 Microsoft Kinect Image and Depth Sensors 138

7.4 Wearable Sensors for Gait Analysis 140

7.4.1 Single Sensor Platforms 140

7.4.2 Multiple Sensor Platforms 141

7.5 Gait Analysis Algorithms Based on Accelerometer/Gyroscope 143

7.5.1 Estimation of Gait Events 143

7.5.2 Estimation of Gait Parameters 144

7.5.2.1 Estimation of Orientation 144

7.5.2.2 Estimating Angles Using Accelerometers 146

7.5.2.3 Estimating Angles Using Gyroscopes 147

7.5.2.4 Fusing Accelerometer and Gyroscope Data 148

7.5.2.5 Quaternion Based Estimation of Orientation 148

7.5.2.6 Step Length Estimation 150

7.6 Conclusions 152

References 152

8 Popular Health Monitoring Systems157

8.1 Introduction 157

8.2 Technology for Data Acquisition 157

8.3 Physiological Health Monitoring Technologies 158

8.3.1 Predicting Patient Deterioration 158

8.3.2 Ambient Assisted Living: Monitoring Daily Living Activities 163

8.3.3 Monitoring Chronic Obstructive Pulmonary Disease Patients 164

8.3.4 Movement Tracking and Fall Detection/Prevention 165

8.3.5 Monitoring Patients with Dementia 166

8.3.6 Monitoring Patients with Parkinsons Disease 168

8.3.7 Odour Sensitivity Measurement 172

8.4 Conclusions 174

References 174

9 Machine Learning for Sensor Networks183

9.1 Introduction 183

9.2 Clustering Approaches 187

9.2.1k-means Clustering Algorithm 187

9.2.2 Iterative Self-organising Data Analysis Technique 188

9.2.3 Gap Statistics 188

9.2.4 Density-based Clustering 189

9.2.5 Affinity-based Clustering 190

9.2.6 Deep Clustering 190

9.2.7 Semi-supervised Clustering 191

9.2.7.1 Basic Semi-supervised Techniques 191

9.2.7.2 Deep Semi-supervised Techniques 191

9.2.8 Fuzzy Clustering 192

9.3 Classification Algorithms 193

9.3.1 Decision Trees 193

9.3.2 Random Forest 194

9.3.3 Linear Discriminant Analysis 194

9.3.4 Support Vector Machines 195

9.3.5k-nearest Neighbour 201

9.3.6 Gaussian Mixture Model 201

9.3.7 Logistic Regression 202

9.3.8 Reinforcement Learning 202

9.3.9 Artificial Neural Networks 203

9.3.9.1 Deep Neural Networks 204

9.3.9.2 Convolutional Neural Networks 205

9.3.9.3 Recent DNN Approaches 207

9.3.10 Gaussian Processes 208

9.3.11 Neural Processes 208

9.3.12 Graph Convolutional Networks 209

9.3.13 Naïve Bayes Classifier 209

9.3.14 Hidden Markov Model 210

9.3.14.1 Forward Algorithm 212

9.3.14.2 Backward Algorithm 212

9.3.14.3 HMM Design 212

9.4 Common Spatial Patterns 213

9.5 Applications of Machine Learning in BSNs and WSNs 216

9.5.1 Human Activity Detection 216

9.5.2 Scoring Sleep Stages 217

9.5.3 Fault Detection 218

9.5.4 Gas Pipeline Leakage Detection 218

9.5.5 Measuring Pollution Level 218

9.5.6 Fatigue-tracking and Classification System 218

9.5.7 Eye-blink Artefact Removal from EEG Signals 219

9.5.8 Seizure Detection 219

9.5.9 BCI Applications 219

9.6 Conclusions 219

References 220

10 Signal Processing for Sensor Networks229

10.1 Introduction 229

10.2 Signal Processing Problems for Sensor Networks 230

10.3 Fundamental Concepts in Signal Processing 231

10.3.1 Nonlinearity of the Medium 231

10.3.2 Nonstationarity 232

10.3.3 Signal Segmentation 233

10.3.4 Signal Filtering 236

10.4 Mathematical Data Models 237

10.4.1 Linear Models 237

10.4.1.1 Prediction Method 237

10.4.1.2 Pronys Method 238

10.4.1.3 Singular Spectrum Analysis 240

10.4.2 Nonlinear Modelling 242

10.4.3 Gaussian Mixture Model 243

10.5 Transform Domain Signal Analysis 245

10.6 Time-frequency Domain Transforms 245

10.6.1 Short-time Fourier Transform 245

10.6.2 Wavelet Transform 246

10.6.2.1 Continuous Wavelet Transform 246

10.6.2.2 Examples of Continuous Wavelets 247

10.6.2.3 Discrete Time Wavelet Transform 247

10.6.3 Multiresolution Analysis 248

10.6.4 Synchro-squeezing Wavelet Transform 249

10.7 Adaptive Filtering 250

10.8 Cooperative Adaptive Filtering 251

10.8.1 Diffusion Adaptation 252

10.9 Multichannel Signal Processing 254

10.9.1 Instantaneous and Convolutive BSS Problems 255

10.9.2 Array Processing 257

10.10 Signal Processing Platforms for BANs 258

10.11 Conclusions 259

References 260

11 Communication Systems for Body Area Networks267

11.1 Introduction 267

11.2 Short-range Communication Systems 271

11.2.1 Bluetooth 271

11.2.2 Wi-Fi 272

11.2.3 ZigBee 272

11.2.4 Radio Frequency Identification Devices 273

11.2.5 Ultrawideband 273

11.2.6 Other Short-range Communication Methods 274

11.2.7 RF Modules Available in Market 275

11.3 Limitations, Interferences, Noise, and Artefacts 275

11.4 Channel Modelling 276

11.4.1 BAN Propagation Scenarios 276

11.4.1.1 On-body Channel 276

11.4.1.2 In-body Channel 277

11.4.1.3 Off-body Channel 277

11.4.1.4 Body-to-body (or Interference) Channel 278

11.4.2 Recent Approaches to BAN Channel Modelling 278

11.4.3 Propagation Models 279

11.4.4 Standards and Guidelines 283

11.5 BAN-WSN Communications 284

11.6 Routing in WBAN 285

11.6.1 Posture-based Routing 285

11.6.2 Temperature-based Routing 286

11.6.3 Cross-layer Routing 287

11.6.4 Cluster-based Routing 288

11.6.5 QoS-based Routing 289

11.7 BAN-building Network Integration 290

11.8 Cooperative BANs 290

11.9 BAN Security 291

11.10 Conclusions 292

References 292

12 Energy Harvesting Enabled Body Sensor Networks301

12.1 Introduction 301

12.2 Energy Conservation 302

12.3 Network Capacity 302

12.4 Energy Harvesting 303

12.5 Challenges in Energy Harvesting 304

12.6 Types of Energy Harvesting 307

12.6.1 Harvesting Energy from Kinetic Sources 308

12.6.2 Energy Sources from Radiant Sources 312

12.6.3 Energy Harvesting from Thermal Sources 312

12.6.4 Energy Harvesting from Biochemical and Chemical Sources 313

12.7 Topology Control 315

12.8 Typical Energy Harvesters for BSNs 317

12.9 Predicting Availability of Energy 318

12.10 Reliability of Energy Storage 319

12.11 Conclusions 320

References 321

13 Quality of Service, Security, and Privacy for Wearable Sensor Data325

13.1 Introduction 325

13.2 Threats to a BAN 326

13.2.1 Denial-of-service 326

13.2.2 Man-in-the-middle Attack 327

13.2.3 Phishing and Spear Phishing Attacks 327

13.2.4 Drive-by Attack 327

13.2.5 Password Attack 328

13.2.6 SQL Injection Attack 328

13.2.7 Cross-site Scripting Attack 328

13.2.8 Eavesdropping 328

13.2.9 Birthday Attack 329

13.2.10 Malware Attack 329

13.3 Data Security and Most Common Encryption Methods 330

13.3.1 Data Encryption Standard (DES) 331

13.3.2 Triple DES 331

13.3.3 RivestShamirAdleman (RSA) 331

13.3.4 Advanced Encryption Standard (AES) 332

13.3.5 Twofish 334

13.4 Quality of Service (QoS) 334

13.4.1 Quantification of QoS 335

13.4.1.1 Data Quality Metrics 335

13.4.1.2 Network Quality Related Metrics 335

13.5 System Security 337

13.6 Privacy 339

13.7 Conclusions 339

References 340

14 Existing Projects and Platforms345

14.1 Introduction 345

14.2 Existing Wearable Devices 347

14.3 BAN Programming Framework 348

14.4 Commercial Sensor Node Hardware Platforms 348

14.4.1 Mica2/MicaZ Motes 348

14.4.2 TelosB Mote 349

14.4.3 Indriya-Zigbee Based Platform 350

14.4.4 IRIS 350

14.4.5 iSense Core Wireless Module 351

14.4.6 Preon32 Wireless Module 351

14.4.7 Wasp Mote 352

14.4.8 WiSense Mote 352

14.4.9 panStamp NRG Mote 354

14.4.10 Jennic JN5139 354

14.5 BAN Software Platforms 355

14.5.1 Titan 355

14.5.2 CodeBlue 355

14.5.3 RehabSPOT 356

14.5.4 SPINE and SPINE2 356

14.5.5 C-SPINE 356

14.5.6 MAPS 356

14.5.7 DexterNet 356

14.6 Popular BAN Application Domains 356

14.7 Conclusions 359

References 359

15 Conclusions and Suggestions for Future Research363

15.1 Summary 363

15.2 Future Directions in BSN Research 363

15.2.1 Smart Sensors: Intelligent, Biocompatible, and Wearable 364

15.2.2 Big Data Problem 366

15.2.3 Data Processing and Machine Learning 366

15.2.4 Decentralised and Cooperative Networks 367

15.2.5 Personalised Medicine Through Personalised Technology 367

15.2.6 Fitting BSN to 4G and 5G Communication Systems 367

15.2.7 Emerging Assistive Technology Applications 368

15.2.8 Solving Problems with Energy Harvesting 368

15.2.9 Virtual World 368

15.3 Conclusions 369

References 369

Index 373