This book discusses recent advances in wearable technologies and personal monitoring devices, covering topics such as skin contact-based wearables (electrodes), non-contact wearables, the Internet of things (IoT), and signal processing for wearable devices.
Although it chiefly focuses on wearable devices and provides comprehensive descriptions of all the core principles of personal monitoring devices, the book also features a section on devices that are embedded in smart appliances/furniture, e.g. chairs, which, despite their limitations, have taken the concept of unobtrusiveness to the next level.
Wearable and personal devices are the key to precision medicine, and the medical community is finally exploring the opportunities offered by long-term monitoring of physiological parameters that are collected during day-to-day life without the bias imposed by the clinical environment. Such data offers a prime view of individuals’ physical condition, as well as the efficacy of therapy and occurrence of events. Offering an in-depth analysis of the latest advances in smart and pervasive wearable devices, particularly those that are unobtrusive and invisible, and addressing topics not covered elsewhere, the book will appeal to medical practitioners and engineers alike.
Author(s): Gaetano D. Gargiulo, Ganesh R. Naik
Publisher: Springer
Year: 2021
Language: English
Pages: 310
City: Cham
Preface
Contents
Part I Skin Contact (Electrodes)-Based Wearables
1 ECG Lead Reconstruction Methodologies for Remote Health Monitoring of Cardiovascular Diseases (CVD)
1.1 Introduction
1.2 A Glimpse of State of the Art Methodologies
1.3 Explanation of the State of the Art Methodologies
1.3.1 Accurate and Reliable 3-Lead to 12-Lead ECG Reconstruction Methodology for Remote Health Monitoring Applications
1.3.2 Robust and Accurate Personalised Reconstruction of Standard 12-Lead System from Frankvectorcardiographic System
1.3.3 Personalized Reduced 3-Lead System Formation Methodology for Remote Health Monitoring Applications and Reconstruction of Standard 12-Lead System
1.3.4 Reduced Lead System Selection Methodology for Reliable Standard 12-Lead Reconstruction Targeting Personalised Remote Health Monitoring Applications
1.3.5 Frank Vectorcardiographic System from Standard 12 Lead ECG: An Effort to Enhance Cardiovascular Diagnosis
1.3.6 A Novel 2 Lead to 12 Lead ECG Reconstruction Methodology for Remote Health Monitoring Applications
1.3.7 A Novel Method Based on Convolutional Neural Networks for Deriving Standard 12-Lead ECG from Serial 3-Lead ECG
1.3.8 A Novel Neural-Network Model for Deriving Standard 12-Lead ECGs from Serial Three-Lead ECGs: Application to Self-Care
1.3.9 Linear Affine Transformations Between 3-Lead (Frank XYZ Leads) Vectorcardiogram and 12-Lead Electrocardiogram Signals
1.3.10 Reconstruction of the 12-Lead Electrocardiogram from Reduced Lead Sets
1.4 Conclusion
References
2 Brain–Computer Interface as a Potential Access Method for Communication in Non-verbal Children with Cerebral Palsy: A State-of-the-Art Review
2.1 Introduction
2.1.1 The Need for Brain–Computer Interface Research for Communication in Children with Cerebral Palsy
2.2 Outline, Scope, and Limitations of the Review
2.3 Research on Brain–Computer Interfaces in Adults
2.3.1 Characteristics of the BCI
2.3.2 Individual Characteristics of the BCI User
2.3.3 Type of Feedback and Instruction
2.3.4 BCI Performance Evaluation
2.4 Research on Brain–Computer Interfaces in Children for Communication and Receptive Language
2.5 Ethical Considerations
2.6 Conclusions and Recommendations
References
3 PPG-Based Non-invasive Methodologies for Pervasive Monitoring of Vitals: BP and HR
3.1 Introduction
3.2 HR and BP Estimation Methods
3.3 Long-Term Recurrent Convolutional Network (LRCN)
3.3.1 CorNET: Deep Learning Framework
3.3.2 PP-Net: Deep Learning Framework
3.4 Performance and Complexity Analysis
3.5 Conclusion
References
4 Wearable Sensor System to Monitor the Status of the Automobile Drivers
4.1 Introduction
4.2 System Design
4.3 Wearable Sensors
4.4 Fatigue Module
4.5 Conclusion
References
5 Movement Analysis of Human Lower Limb Using EMG Sensor for Effective Implementation of Artificial Lower Extremity
5.1 Introduction
5.2 Structural View of Lower Limb
5.3 Working Principle of EMG Signal Processing
5.4 EMG Sensor Details
5.5 Muscular Model Simulation Software
5.6 Experimental Results and Discussions
5.6.1 Output Measurement from the Specified EMG Acquisition Sensor
5.6.2 Output Measurement in the Rest Condition of Lower Limb Muscle
5.6.3 OpenSim Graphical Plotter for Rest Condition
5.6.4 Analysis for Rest Condition
5.7 Output Measurement in the Stretched Condition of Lower Limb Muscle
5.7.1 OpenSim Graphical Plotter for Stretched Condition (Knee Position)
5.7.2 OpenSim Graphical Plotter for Stretched Condition (Ankle Position)
5.7.3 Analysis for Stretched Conditions
5.8 Comparative Study of Rest and Stretched Condition Graphs
5.9 System Stability Analysis of the Mathematical Model of Lower Limb
5.9.1 Advantages and Limitations
5.10 Conclusions
References
Part II Non-contact-Based Wearables
6 Sleep Monitoring Wearables: Present to Future
6.1 Introduction to Sleep Wearables
6.1.1 What Are Sleep Wearables and Where Are They Used?
6.1.2 Current Limitations of Sleep Wearable Industry
6.2 Current Understanding of Sleep and Its Measures
6.2.1 Overview of Sleep
6.2.2 Conventional Sleep Measurements
6.3 Principles and Challenges While Choosing or Developing Sleep Wearables
6.3.1 Sleep Sensors Based Sleep Technology
6.3.2 Engineering Guidelines While Developing a Sleep Wearable
6.3.3 Regulatory Conformances in the Development of a Sleep Wearable
6.3.4 Case Study for Adding ERP in Sleep and tACS for PSG Recording
6.4 Some Novel Ideas for Sleep Wearable Technology
6.4.1 Capturing the Cycling Pattern of Sleep Stages
6.4.2 Acoustic (Sleep ERPs) and Electrical Neuromodulation (Sleep tACS) Based Approaches During Sleep
6.5 Summary
References
7 Non-invasive Monitoring of Health Using Sensor-Rich Wearables and Smart Devices
7.1 Introduction
7.1.1 Evolution of Sensors in Wearables and Smart Devices
7.1.2 A Classification of Sensors
7.2 Wearable-Based Health Monitoring: A Generic Framework
7.3 Non-invasive Sensor-Based Health Screening and Monitoring
7.3.1 Motion Sensors
7.3.2 Physiological Sensors
7.3.3 Ambient Sensors
7.3.4 Display Sensors
7.4 Conclusion
References
8 Sleep Monitoring in Adults Using Wearables and Unobtrusive Technology
8.1 Physiological Background of Human Sleep
8.1.1 Sleep Stages
8.1.2 Sleep Architecture
8.2 Polysomnography at a Sleep Laboratory
8.3 Wearables and Unobtrusive Technologies for Sleep Monitoring at Home
8.3.1 Out of Center (Home) Poly(somno)Graphy Devices
8.3.2 Home Sleep Monitoring with Unobtrusive Sensors
8.4 Machine Learning Algorithms for Sleep Staging at Home
8.4.1 State-of-the-art Algorithms Based on Cardiac and Respiratory Signals
8.4.2 Sleep Staging Approaches Based on Wearable and Unobtrusive Sensor Technologies
8.4.3 Signal Processing Challenges Presented by Wearable Systems
8.4.4 Future Research in Sleep Staging at Home
8.5 Detection, Screening, and Phenotyping of Sleep Apnea in an Ambulatory Setting
8.5.1 Cardiovascular
8.5.2 Oximetry
8.5.3 Effort and Respiration
8.5.4 Sound
8.5.5 Automatically Generated Features Using Deep Learning
8.5.6 Multimodal OSA Detection
8.5.7 Comparison of Methods and Modalities
8.5.8 Future of Sleep Apnea Screening: Beyond the AHI?
8.6 Conclusion
References
Part III Beyond Wearables
9 The CueMinder Project: Patient-Driven Wearable Technology to Improve Quality of Life
9.1 Introduction
9.2 Design Process
9.2.1 Empathy
9.2.2 Define
9.2.3 Ideate
9.2.4 Prototype
9.2.5 Test and Assessment
9.3 Future Directions
9.4 Conclusion
References
10 IoT and Machine Learning Algorithms for Fall Detection
10.1 Introduction
10.2 Fall Detection
10.2.1 Practical Factors for Fall
10.2.2 Wearable Devices
10.2.3 Context-Aware Systems
10.3 How IoT Helps in Fall Detection
10.3.1 Edge Section
10.3.2 Fog Layer
10.3.3 Cloud Layer
10.4 Phases During Fall Detection
10.4.1 Pre-impact Phase
10.4.2 Impact Phase
10.4.3 Post-impact Phase
10.5 Advantage and Disadvantage of IoT in Fall Detection
10.5.1 Advantages
10.5.2 Disadvantage
10.6 Challenges for IoT Devices
10.7 Discussion
10.8 Conclusion and Future Scope
References
Part IV Signal Processing for Wearable/IOT Applications
11 Smart Home Automation Using Wearable Technology
11.1 Introduction
11.2 Related Works
11.3 Communication Protocols
11.3.1 Zigbee
11.3.2 Z-wave
11.3.3 Bluetooth Low Energy (BLE)
11.4 Role of Internet of Things in Smart Home Automation
11.5 Gesture-Based Home Automation and Security for the Visually Impaired People and Old Age People
11.5.1 Description of the System
11.6 Conclusion
References
12 A Novel Architecture Design for Complex Network Measures of Brain Connectivity Aiding Diagnosis
12.1 Introduction
12.2 Materials and Methods
12.2.1 Artefact Removal Methodology
12.2.2 Analysing Wireless EEG-Based Functional Connectivity Measures with Respect to Change in Environmental Factors
12.2.3 Classifying Human Emotional States Using Wireless EEG-Based ERP and Functional Connectivity Measures
12.2.4 Brain Connectivity Analysis from EEG Signals Using Stable Phase-Synchronized States During Face-Perception Tasks
12.2.5 On the Existence of Synchrostates in Multichannel EEG Signals During Face-Perception Tasks
12.2.6 Integrated Toolbox to Characterize Functional and Effective Brain Connectivity
12.2.7 EEG Processing Overview
12.3 Results and Observation
12.3.1 Phase Lag Index: Assessment of Functional Connectivity from Multichannel EEG and MEG With Diminished Bias from Common Sources
12.4 Conclusion
References
