Body Sensor Networking, Design and Algorithms

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 xiiiAbout the Companion Website xv1 Introduction 11.1 History of Wearable Technology 11.2 Introduction to BSN Technology 21.3 BSN Architecture 71.4 Layout of the Book 10References 112 Physical, Physiological, Biological, and Behavioural States of the Human Body 172.1 Introduction 172.2 Physical State of the Human Body 172.3 Physiological State of Human Body 192.4 Biological State of Human Body 232.5 Psychological and Behavioural State of the Human Body 242.6 Summary and Conclusions 30References 313 Physical, Physiological, and Biological Measurements 353.1 Introduction 353.2 Wearable Technology for Gait Monitoring 353.2.1 Accelerometer and Its Application to Gait Monitoring 363.2.1.1 How Accelerometers Operate 373.2.1.2 Accelerometers in Practice 393.2.2 Gyroscope and IMU 403.2.3 Force Plates 413.2.4 Goniometer 413.2.5 Electromyography 413.2.6 Sensing Fabric 423.3 Physiological Sensors 423.3.1 Multichannel Measurement of the Nerves Electric Potentials 423.3.2 Other Sensors 453.4 Biological Sensors 483.4.1 The Structures of Biological Sensors – The Principles 483.4.2 Emerging Biosensor Technologies 513.5 Conclusions 51References 534 Ambulatory and Popular Sensor Measurements 594.1 Introduction 594.2 Heart Rate 594.2.1 HR During Physical Exercise 604.3 Respiration 624.4 Blood Oxygen Saturation Level 674.5 Blood Pressure 704.5.1 Cuffless Blood Pressure Measurement 714.6 Blood Glucose 724.7 Body Temperature 734.8 Commercial Sensors 744.9 Conclusions 75References 765 Polysomnography and Sleep Analysis 835.1 Introduction 835.2 Polysomnography 845.3 Sleep Stage Classification 855.3.1 Sleep Stages 855.3.2 EEG-Based Classification of Sleep Stages 865.3.2.1 Time Domain Features 865.3.2.2 Frequency Domain Features 875.3.2.3 Time-frequency Domain Features 875.3.2.4 Short-time Fourier Transform 885.3.2.5 Wavelet Transform 885.3.2.6 Matching Pursuit 885.3.2.7 Empirical Mode Decomposition 895.3.2.8 Nonlinear Features 895.3.3 Classification Techniques 905.3.3.1 Using Neural Networks 905.3.3.2 Application of CNNs 925.3.4 Sleep Stage Scoring Using CNN 945.4 Monitoring Movements and Body Position During Sleep 965.5 Conclusions 99References 1006 Noninvasive, Intrusive, and Nonintrusive Measurements 1076.1 Introduction 1076.2 Noninvasive Monitoring 1076.3 Contactless Monitoring 1096.3.1 Remote Photoplethysmography 1096.3.1.1 Derivation of Remote PPG 1106.3.2 Spectral Analysis Using Autoregressive Modelling 1116.3.3 Estimation of Physiological Parameters Using Remote PPG 1146.3.3.1 Heart Rate Estimation 1146.3.3.2 Respiratory Rate Estimation 1166.3.3.3 Blood Oxygen Saturation Level Estimation 1176.3.3.4 Pulse Transmit Time Estimation 1186.3.3.5 Video Pre-processing 1196.3.3.6 Selection of Regions of Interest 1206.3.3.7 Derivation of the rPPG Signal 1206.3.3.8 Processing rPPG Signals 1206.3.3.9 Calculation of rPTT/dPTT 1216.4 Implantable Sensor Systems 1226.5 Conclusions 123References 1247 Single and Multiple Sensor Networking for Gait Analysis 1297.1 Introduction 1297.2 Gait Events and Parameters 1297.2.1 Gait Events 1297.2.2 Gait Parameters 1307.2.2.1 Temporal Gait Parameters 1307.2.2.2 Spatial Gait Parameters 1327.2.2.3 Kinetic Gait Parameters 1337.2.2.4 Kinematic Gait Parameters 1337.3 Standard Gait Measurement Systems 1357.3.1 Foot Plantar Pressure System 1357.3.2 Force-plate Measurement System 1357.3.3 Optical Motion Capture Systems 1377.3.4 Microsoft Kinect Image and Depth Sensors 1387.4 Wearable Sensors for Gait Analysis 1407.4.1 Single Sensor Platforms 1407.4.2 Multiple Sensor Platforms 1417.5 Gait Analysis Algorithms Based on Accelerometer/Gyroscope 1437.5.1 Estimation of Gait Events 1437.5.2 Estimation of Gait Parameters 1447.5.2.1 Estimation of Orientation 1447.5.2.2 Estimating Angles Using Accelerometers 1467.5.2.3 Estimating Angles Using Gyroscopes 1477.5.2.4 Fusing Accelerometer and Gyroscope Data 1487.5.2.5 Quaternion Based Estimation of Orientation 1487.5.2.6 Step Length Estimation 1507.6 Conclusions 152References 1528 Popular Health Monitoring Systems 1578.1 Introduction 1578.2 Technology for Data Acquisition 1578.3 Physiological Health Monitoring Technologies 1588.3.1 Predicting Patient Deterioration 1588.3.2 Ambient Assisted Living: Monitoring Daily Living Activities 1638.3.3 Monitoring Chronic Obstructive Pulmonary Disease Patients 1648.3.4 Movement Tracking and Fall Detection/Prevention 1658.3.5 Monitoring Patients with Dementia 1668.3.6 Monitoring Patients with Parkinson’s Disease 1688.3.7 Odour Sensitivity Measurement 1728.4 Conclusions 174References 1749 Machine Learning for Sensor Networks 1839.1 Introduction 1839.2 Clustering Approaches 1879.2.1 k-means Clustering Algorithm 1879.2.2 Iterative Self-organising Data Analysis Technique 1889.2.3 Gap Statistics 1889.2.4 Density-based Clustering 1899.2.5 Affinity-based Clustering 1909.2.6 Deep Clustering 1909.2.7 Semi-supervised Clustering 1919.2.7.1 Basic Semi-supervised Techniques 1919.2.7.2 Deep Semi-supervised Techniques 1919.2.8 Fuzzy Clustering 1929.3 Classification Algorithms 1939.3.1 Decision Trees 1939.3.2 Random Forest 1949.3.3 Linear Discriminant Analysis 1949.3.4 Support Vector Machines 1959.3.5 k-nearest Neighbour 2019.3.6 Gaussian Mixture Model 2019.3.7 Logistic Regression 2029.3.8 Reinforcement Learning 2029.3.9 Artificial Neural Networks 2039.3.9.1 Deep Neural Networks 2049.3.9.2 Convolutional Neural Networks 2059.3.9.3 Recent DNN Approaches 2079.3.10 Gaussian Processes 2089.3.11 Neural Processes 2089.3.12 Graph Convolutional Networks 2099.3.13 Naïve Bayes Classifier 2099.3.14 Hidden Markov Model 2109.3.14.1 Forward Algorithm 2129.3.14.2 Backward Algorithm 2129.3.14.3 HMM Design 2129.4 Common Spatial Patterns 2139.5 Applications of Machine Learning in BSNs and WSNs 2169.5.1 Human Activity Detection 2169.5.2 Scoring Sleep Stages 2179.5.3 Fault Detection 2189.5.4 Gas Pipeline Leakage Detection 2189.5.5 Measuring Pollution Level 2189.5.6 Fatigue-tracking and Classification System 2189.5.7 Eye-blink Artefact Removal from EEG Signals 2199.5.8 Seizure Detection 2199.5.9 BCI Applications 2199.6 Conclusions 219References 22010 Signal Processing for Sensor Networks 22910.1 Introduction 22910.2 Signal Processing Problems for Sensor Networks 23010.3 Fundamental Concepts in Signal Processing 23110.3.1 Nonlinearity of the Medium 23110.3.2 Nonstationarity 23210.3.3 Signal Segmentation 23310.3.4 Signal Filtering 23610.4 Mathematical Data Models 23710.4.1 Linear Models 23710.4.1.1 Prediction Method 23710.4.1.2 Prony’s Method 23810.4.1.3 Singular Spectrum Analysis 24010.4.2 Nonlinear Modelling 24210.4.3 Gaussian Mixture Model 24310.5 Transform Domain Signal Analysis 24510.6 Time-frequency Domain Transforms 24510.6.1 Short-time Fourier Transform 24510.6.2 Wavelet Transform 24610.6.2.1 Continuous Wavelet Transform 24610.6.2.2 Examples of Continuous Wavelets 24710.6.2.3 Discrete Time Wavelet Transform 24710.6.3 Multiresolution Analysis 24810.6.4 Synchro-squeezing Wavelet Transform 24910.7 Adaptive Filtering 25010.8 Cooperative Adaptive Filtering 25110.8.1 Diffusion Adaptation 25210.9 Multichannel Signal Processing 25410.9.1 Instantaneous and Convolutive BSS Problems 25510.9.2 Array Processing 25710.10 Signal Processing Platforms for BANs 25810.11 Conclusions 259References 26011 Communication Systems for Body Area Networks 26711.1 Introduction 26711.2 Short-range Communication Systems 27111.2.1 Bluetooth 27111.2.2 Wi-Fi 27211.2.3 ZigBee 27211.2.4 Radio Frequency Identification Devices 27311.2.5 Ultrawideband 27311.2.6 Other Short-range Communication Methods 27411.2.7 RF Modules Available in Market 27511.3 Limitations, Interferences, Noise, and Artefacts 27511.4 Channel Modelling 27611.4.1 BAN Propagation Scenarios 27611.4.1.1 On-body Channel 27611.4.1.2 In-body Channel 27711.4.1.3 Off-body Channel 27711.4.1.4 Body-to-body (or Interference) Channel 27811.4.2 Recent Approaches to BAN Channel Modelling 27811.4.3 Propagation Models 27911.4.4 Standards and Guidelines 28311.5 BAN-WSN Communications 28411.6 Routing in WBAN 28511.6.1 Posture-based Routing 28511.6.2 Temperature-based Routing 28611.6.3 Cross-layer Routing 28711.6.4 Cluster-based Routing 28811.6.5 QoS-based Routing 28911.7 BAN-building Network Integration 29011.8 Cooperative BANs 29011.9 BAN Security 29111.10 Conclusions 292References 29212 Energy Harvesting Enabled Body Sensor Networks 30112.1 Introduction 30112.2 Energy Conservation 30212.3 Network Capacity 30212.4 Energy Harvesting 30312.5 Challenges in Energy Harvesting 30412.6 Types of Energy Harvesting 30712.6.1 Harvesting Energy from Kinetic Sources 30812.6.2 Energy Sources from Radiant Sources 31212.6.3 Energy Harvesting from Thermal Sources 31212.6.4 Energy Harvesting from Biochemical and Chemical Sources 31312.7 Topology Control 31512.8 Typical Energy Harvesters for BSNs 31712.9 Predicting Availability of Energy 31812.10 Reliability of Energy Storage 31912.11 Conclusions 320References 32113 Quality of Service, Security, and Privacy for Wearable Sensor Data 32513.1 Introduction 32513.2 Threats to a BAN 32613.2.1 Denial-of-service 32613.2.2 Man-in-the-middle Attack 32713.2.3 Phishing and Spear Phishing Attacks 32713.2.4 Drive-by Attack 32713.2.5 Password Attack 32813.2.6 SQL Injection Attack 32813.2.7 Cross-site Scripting Attack 32813.2.8 Eavesdropping 32813.2.9 Birthday Attack 32913.2.10 Malware Attack 32913.3 Data Security and Most Common Encryption Methods 33013.3.1 Data Encryption Standard (DES) 33113.3.2 Triple DES 33113.3.3 Rivest–Shamir–Adleman (RSA) 33113.3.4 Advanced Encryption Standard (AES) 33213.3.5 Twofish 33413.4 Quality of Service (QoS) 33413.4.1 Quantification of QoS 33513.4.1.1 Data Quality Metrics 33513.4.1.2 Network Quality Related Metrics 33513.5 System Security 33713.6 Privacy 33913.7 Conclusions 339References 34014 Existing Projects and Platforms 34514.1 Introduction 34514.2 Existing Wearable Devices 34714.3 BAN Programming Framework 34814.4 Commercial Sensor Node Hardware Platforms 34814.4.1 Mica2/MicaZ Motes 34814.4.2 TelosB Mote 34914.4.3 Indriya-Zigbee Based Platform 35014.4.4 IRIS 35014.4.5 iSense Core Wireless Module 35114.4.6 Preon32 Wireless Module 35114.4.7 Wasp Mote 35214.4.8 WiSense Mote 35214.4.9 panStamp NRG Mote 35414.4.10 Jennic JN5139 35414.5 BAN Software Platforms 35514.5.1 Titan 35514.5.2 CodeBlue 35514.5.3 RehabSPOT 35614.5.4 SPINE and SPINE2 35614.5.5 C-SPINE 35614.5.6 MAPS 35614.5.7 DexterNet 35614.6 Popular BAN Application Domains 35614.7 Conclusions 359References 35915 Conclusions and Suggestions for Future Research 36315.1 Summary 36315.2 Future Directions in BSN Research 36315.2.1 Smart Sensors: Intelligent, Biocompatible, and Wearable 36415.2.2 Big Data Problem 36615.2.3 Data Processing and Machine Learning 36615.2.4 Decentralised and Cooperative Networks 36715.2.5 Personalised Medicine Through Personalised Technology 36715.2.6 Fitting BSN to 4G and 5G Communication Systems 36715.2.7 Emerging Assistive Technology Applications 36815.2.8 Solving Problems with Energy Harvesting 36815.2.9 Virtual World 36815.3 Conclusions 369References 369Index 373