Smart Health Technologies for the COVID-19 Pandemic : Internet of Medical Things Perspectives

Smart Health Technologies for the COVID-19 Pandemic: Internet of medical things perspectives looks at the role technology has played to monitor, map and fight the global COVID-19 pandemic. Chapters outline risk assessment methodologies and social distancing and infection control technologies in the face of this disease outbreak. The applications of Big Data and artificial intelligence in the fight against the spread of COVID-19 are explored in this edited book, as well as advances in early diagnostic testing and remote monitoring systems, and blockchain-based solutions for secure data handling. The implementation of machine learning for reviewing and analysing biomedical data and assisting with drug design is also discussed. Emphasising the vital role that intelligent advanced healthcare informatics has played during this crucial time, this book is a valuable resource for researchers in the fields of biomedical engineering, bioengineering, electronics engineering, health informatics, wireless body area networks (WBAN), data analytics, telemedicine, and those in related fields. Cover Contents About the editors Preface Book organisation Chapter 1: Internet of Things (IoT) and blockchain-based solutions to confront COVID-19 pandemic Chapter 2: Application of big data and computational intelligence in fighting COVID-19 epidemic Chapter 3: Cloud-based IoMT for early COVID-19 diagnosis and monitoring Chapter 4: Assessment analysis of COVID-19 on the global economics and trades Chapter 5: Early diagnosis and remote monitoring using cloud-based IoMT for COVID-19 Chapter 6: Blockchain technology for secure COVID-19 pandemic data handling Chapter 7: Social-distancing technologies for COVID-19 Chapter 8: Social health protection in touristic destinations during COVID-19 Chapter 9: Analysis of Artificial Intelligence and Internet of Things in biomedical imaging and sequential data for COVID-19 Chapter 10: Review of medical imaging with machine learning and deep learning-based approaches for COVID-19 Chapter 11: Machine-based drug design to inhibit SARSCoV 2 virus Chapter 12: Stress detection for cognitive rehabilitation in COVID-19 scenario Chapter 13: Arduino-based robot for purification of COVID-19 using far UVC light Chapter 14: Effect of COVID-19 pandemic on waste management system and infection control Chapter 15: Natural adjunctive therapies options other than COVID-19 antiviral therapies Chapter 16: Risk assessment and spread of COVID-19 1 Internet of Things (IoT) and blockchain-based solutions to confront COVID-19 pandemic Abstract 1.1 Introduction 1.2 Internet of Things (IoT) and blockchain overview 1.2.1 Internet of Things 1.2.1.1 Major IoT components 1.2.2 Blockchain 1.2.2.1 Blockchain components 1.2.2.2 Blockchain consensus mechanism 1.2.2.3 Types of blockchain 1.3 IoT technologies to confront COVID-19 1.3.1 Health monitoring systems 1.3.2 Tracking and detecting possible patients 1.3.3 Disinfecting area 1.3.4 Telemedicine 1.3.5 Logistics delivery 1.4 Blockchain technologies to confront COVID-19 1.4.1 Contact tracing 1.4.2 Database security 1.4.3 Information sharing 1.4.4 Prevention of data fabrication 1.4.5 Internet of Medical Things 1.5 Challenges, solutions, and deliverables 1.5.1 Challenges of IoT and blockchain technology 1.5.2 Possible solutions and deliverables 1.6 Key findings and discussion 1.7 Conclusion and future scopes References 2 Application of big data and computational intelligence in fighting COVID-19 epidemic Abstract 2.1 Introduction 2.2 Applicability of computational intelligence in combating COVID-19 pandemic 2.3 Big data and analytics in battling COVID-19 outbreak 2.4 The limitations of using big data and computational intelligence to fight the COVID-19 pandemic 2.5 The practical case of using computational intelligence in fighting COVID-19 pandemic 2.5.1 Confusion matrix 2.5.2 ROC curves 2.5.3 Precision-recall curve 2.6 Conclusion References 3 Cloud-based IoMT for early COVID-19 diagnosis and monitoring Abstract 3.1 Introduction 3.2 Overview about COVID-19 treatments 3.2.1 Symptoms 3.2.2 Methodologies in COVID-19 diagnosis 3.2.3 Treatment approaches 3.2.4 Available vaccine 3.2.4.1 The whole-microbe approach 3.2.4.2 Viral vector vaccine 3.2.4.3 The subunit approach 3.2.5 COVID-19 timeline 3.3 Related work 3.3.1 Lightweight block encryption–based secure health monitoring system for data management 3.3.1.1 IoT network and data collection 3.3.1.2 Communication service provider 3.3.1.3 Distributed data storage 3.3.1.4 Healthcare provider 3.3.2 Smart diagnostic/therapeutic framework for COVID19 patients 3.3.3 IoT-based framework for collecting real-time symptom data using machine learning algorithms 3.3.3.1 Dataset 3.3.3.2 Data preprocessing 3.3.3.3 Predictive model 3.4 Proposed methodology 3.4.1 Architecture of proposed IoT framework 3.4.1.1 Collection and upload of symptom data 3.4.1.2 Center for quarantine and isolation 3.4.1.3 Data analysis center—a place where people may go to get their data analyzed 3.4.1.4 Way to connect with doctors 3.4.1.5 The cloud infrastructure connects them all 3.4.2 Data acquisition using wearables devices 3.5 Implementation of proposed framework 3.6 Results and discussion 3.7 Conclusion and future scopes References 4 Assessment analysis of COVID-19 on the global economics and trades Abstract 4.1 Introduction 4.2 Backgrounds 4.3 Social impacts on finance 4.4 Framework for the international financial system, bionetworks, and maintainability on pandemic 4.4.1 Assessment strategy constructions to fight COVID-19 4.4.2 Macro-finance impacts 4.4.3 Econometric effects: consumer preferences 4.4.4 Nonpositive impacts of COVID-19 4.4.5 Impact of international commercial trading 4.4.6 COVID-19’s effect on the aviation industry 4.4.7 Significant collision on the travel sector 4.4.8 Significant reduction in primary energy usage 4.4.9 Record decrease in CO2 emissions 4.4.10 Rise in digitalization 4.5 The role of circular economy 4.5.1 The circular economy for slowing the onset of climate collapse 4.5.2 Social finance system 4.5.3 Hurdles to CE for context of COVID-19 4.6 Chances financial support after COVID-19 4.6.1 Several solutions to manage hospital medical and general waste 4.6.2 Facilities for CE in communication sector 4.6.3 Use digitalization after COVID-19 4.7 Conclusions References 5 Early diagnosis and remote monitoring using cloud-based IoMT for COVID-19 Abstract 5.1 Introduction 5.2 Detection techniques 5.3 Internet of Medical Things 5.4 IoMT devices for the identification of COVID-19 symptoms and remote monitoring 5.4.1 Wearables 5.4.1.1 Smart thermometers 5.4.1.2 Smart helmet 5.4.1.3 Smart glasses 5.4.1.4 Robots 5.4.1.5 Drones 5.4.1.6 IoT buttons 5.4.1.7 WHOOP strap 5.4.2 Smartphone applications 5.5 Early diagnosis of COVID-19 and remote monitoring procedures 5.6 Machine learning and deep learning in COVID-19 diagnosis 5.7 Related works 5.8 Experimental case study 5.8.1 Dataset description 5.8.2 Methodology 5.8.3 Training 5.8.4 Experimental setup and results 5.9 Measures for monitoring and tracking COVID-19 5.10 Limitations of using IoMT devices 5.11 Conclusion and future scope References 6 Blockchain technology for secure COVID-19 pandemic data handling Abstract 6.1 Introduction 6.2 Recent developments in blockchain technology 6.2.1 Healthcare data systems 6.2.2 Healthcare data exchanges 6.2.3 Healthcare administration 6.2.4 Pharmaceuticals 6.3 Potential benefits of blockchain technology in data handling 6.3.1 Better exchange of healthcare data records 6.3.2 Validating trust in medical research and supplies 6.3.3 Validating correct billing management 6.3.4 Internet of Things (IoT) in healthcare 6.3.5 Optimized privacy and data security 6.4 Key challenges of blockchain technology in data handling 6.4.1 Security 6.4.2 Speed 6.4.3 Interoperability 6.4.4 Stringent data protection regulation 6.4.5 Scalability 6.4.6 Privacy 6.5 Prospects of blockchain technology 6.6 Research on blockchain technology in COVID19 healthcare 6.7 Real-time analysis of COVID-19 pandemic data 6.7.1 The susceptible recovered infectious (SIR) model 6.7.2 Standard logistic regression model 6.7.3 Time-to-event analytics model 6.7.4 Results of major real-time analysis 6.8 Recommendations and future directions 6.9 Conclusion and future scopes Acknowledgments References 7 Social distancing technologies for COVID-19 Abstract 7.1 Introduction 7.2 Methodology 7.3 Social distancing technologies for education 7.3.1 Learning management system 7.3.1.1 Massive open online course 7.3.1.2 Google Classroom 7.3.1.3 Moodle 7.3.2 Social networking and conference software for education 7.4 Social distancing technology in healthcare 7.4.1 Wearable technology 7.4.2 Screening system 7.4.3 Queue systems 7.4.4 Payment system 7.4.5 Social distancing notified people in public 7.5 Social distancing technology in manufacturing 7.5.1 Checking the distance using wearable device 7.5.2 Distance monitoring using Wi-Fi 7.5.3 Distance monitoring using video analytics 7.5.4 Social distancing by replacing some work with a robot 7.6 Social-distancing technologies for supporting everyday life 7.6.1 Technologies support working at home 7.6.1.1 Remote working 7.6.2 Applications support work from home (WFH) service 7.6.2.1 Call center 7.6.2.2 E-Documents 7.6.2.3 Cloud service 7.6.2.4 Management information system 7.6.3 Conferencing application 7.6.3.1 LINE 7.6.3.2 Zoom 7.6.3.3 Google Meet 7.6.3.4 Webex 7.7 Social distancing and smart city 7.7.1 AI and big data 7.7.2 Implementation and usability 7.7.3 Privacy and security 7.7.4 Policy and legislation 7.8 Conclusion and future works References 8 Social health protection in touristic destinations during COVID-19 Abstract 8.1 Introduction 8.2 Related work 8.3 Proposal of software solution for health protection 8.3.1 System architecture 8.3.2 Healthcare service 8.3.3 Tourist service 8.3.4 Local government service 8.3.5 Border control 8.4 Data protection 8.5 Conclusion and future works References 9 Analysis of Artificial Intelligence and Internet of Things in biomedical imaging and sequential data for COVID-19 Abstract 9.1 Introduction 9.2 Definition of biomedical keywords 9.2.1 Microarray and RNA-seq data 9.2.2 De novo mutation 9.2.3 ChiP-seq data 9.2.4 Biomedical imaging 9.3 Categories of computational algorithms in biomedical data 9.3.1 Biomedical data analysis 9.3.2 Array-based data analysis 9.3.2.1 Microarray data analysis 9.3.2.2 RNA-seq data analysis 9.3.2.3 scRNA-seq data analysis 9.3.3 Hybrid data analysis 9.4 Different techniques for diagnosis using biomedical imaging 9.4.1 Brain 9.4.2 Breast 9.4.3 Kidney 9.4.4 Ovary 9.4.5 Skin cancer 9.4.6 Soft tissue sarcoma 9.5 Comparative review of computational algorithms 9.6 Role of CT in COVID-19 pandemic 9.7 Advent of smart technologies during COVID-19 9.7.1 Building ML models to diagnose COVID-19 9.7.2 Impact of IoT in healthcare 9.8 Conclusion References 10 Review of medical imaging with machine learning and deep learning-based approaches for COVID-19 Abstract 10.1 Introduction 10.2 Literature review 10.2.1 Reviewed work 10.2.1.1 Convolutional neural networks 10.2.1.2 Transfer learning 10.2.1.3 Ensemble 10.2.1.4 Generative networks 10.2.1.5 Generalized regression neural network and probabilistic neural network 10.2.1.6 YOLO 10.2.1.7 Machine learning 10.3 Comparative analysis of existing work 10.4 Research gaps 10.4.1 Unavailability of large datasets 10.4.2 Imbalanced datasets 10.4.3 Multiple image sources 10.5 Conclusion References 11 Machine-based drug design to inhibit SARS-CoV-2 virus Abstract 11.1 Introduction 11.2 What is SARS-coronavirus-2? 11.3 Mechanism of SARS-coronavirus-2 infection in human 11.4 How SARS-coronavirus-2 multiplies? 11.5 Human antibody generation and role of vaccine 11.5.1 Immediate action of human antibody 11.5.2 Role of synthetic vaccine on COVID-19 11.6 Real-time COVID-19 identification test (RT-PCR) 11.6.1 Limitations of RT-PCR tool 11.7 Discussion on in silico methods in COVID-19 drug research 11.7.1 In silico-assisted anchoring site analysis 11.7.1.1 AutoDock Vina 11.7.1.2 Molecular dynamics 11.7.1.3 The DockThor-VS platform 11.7.2 Machine-assisted designing and evaluation of COVID-19 drug 11.7.2.1 Molecular docking program 11.7.2.2 High ambiguity–driven protein–protein DOCKing 11.7.2.3 Molecular dynamics 11.8 Machine-integrated advanced techniques for COVID-19 11.8.1 Computerized tomography in COVID-19 detection 11.8.2 Advanced MRI for COVID-19 treatment 11.9 Summary 11.10 Conclusion and future scopes 11.10.1 Future scope References 12 Stress detection for cognitive rehabilitation in COVID-19 scenario Abstract 12.1 Introduction 12.2 Related works 12.3 Proposed framework 12.3.1 Introduction to EEG 12.3.2 Feature extraction using DWT 12.3.3 Feature selection using principal component analysis 12.3.4 Classification using support vector machine 12.4 Experimental outcomes and discussions 12.4.1 Dataset preparation 12.4.2 sLORETA-based activated brain region selection 12.4.3 Discrete wavelet transform–based feature extraction outcome 12.4.4 Principal component analysis–based dimensionality reduction outcome 12.4.5 Support vector machine–based classification outcome 12.4.6 Performance metrics 12.4.7 Performance evaluation 12.4.8 Statistical significance using t-test 12.5 Conclusion and future works Acknowledgment References 13 Arduino-based robot for purification of COVID-19 using far UVC light Abstract 13.1 Introduction 13.1.1 Arduino 13.1.2 Far-UVC lamp 13.2 Literature survey 13.2.1 Improvements and requirements 13.2.1.1 Hardware requirements 13.2.1.2 Software requirements 13.3 Working of the proposed robot 13.3.1 Value proposition 13.4 Results and discussions 13.5 Conclusion and future scope References 14 Effect of COVID-19 pandemic on waste management system and infection control Abstract 14.1 Introduction 14.2 Socioeconomic and environmental impact 14.3 Impact of waste generation 14.4 Impacts on waste management 14.4.1 Waste management adjustments 14.4.1.1 Healthcare waste/BMW 14.4.1.2 Household waste/MSW 14.4.1.3 PPE waste 14.4.1.4 Dead bodies 14.5 Challenges in handling waste 14.6 Rethinking effective waste management 14.6.1 Policy, regulatory, and guidelines 14.6.2 Handling of infectious waste 14.6.3 Suitable disposal methods 14.6.4 Information, education, and communication 14.6.5 Data management and learning 14.6.6 Monitoring of segregation 14.6.7 Basic principles for managing waste 14.6.8 Fund raising and national and international collaboration 14.7 Conclusion and future scopes References 15 Natural adjunctive therapies options other than COVID-19 antiviral therapies Abstract 15.1 Introduction 15.2 Immune system and inflammatory responds 15.3 Proinflammatory cytokines 15.4 Immunomodulators and adjunctive therapies 15.4.1 Phenolic compounds 15.4.1.1 Resveratrol 15.4.1.2 Celastrol 15.4.1.3 Curcumin 15.4.1.4 Quercetin 15.4.1.5 Bee products 15.4.2 Melatonin 15.4.3 Zinc 15.4.4 Ascorbic acid (vitamin C) 15.4.5 Vitamin D 15.4.6 Vitamin E 15.4.7 Selenium 15.4.8 Omega-3 fatty acids 15.5 Dietary ingredients in immunity 15.6 Conclusion and future scope for natural antiviral therapies against COVID-19 References 16 Risk assessment and spread of COVID-19 Abstract 16.1 Introduction 16.2 Technology and epidemics 16.2.1 Healthcare 16.2.2 Education 16.2.3 Work 16.2.4 Others 16.3 Prediction techniques 16.4 General methods followed for risk assessment 16.4.1 What-if analysis 16.4.2 Fault-tree analysis 16.4.3 Guidelines issued by World Health Organization 16.5 Prevention and management of epidemics 16.5.1 Strategies proposed 16.5.2 Sentimental analysis using machine learning 16.6 Protecting the living beings from the impact of epidemics 16.6.1 Impact of COVID-19 on agriculture sector 16.6.2 Impact of COVID-19 on economy 16.6.3 Impact of COVID-19 on educational sector 16.7 Our contribution 16.7.1 Proposed method and its working 16.7.2 Components required 16.7.3 Software required and simulation 16.8 Conclusion and future scope References Index Back Cover