Smart Healthcare Monitoring Using IoT with 5G: Challenges, Directions, and Future Predictions
- Length: 280 pages
- Edition: 1
- Language: English
- Publisher: CRC Press
- Publication Date: 2021-12-06
- ISBN-10: 0367775298
- ISBN-13: 9780367775292
- Sales Rank: #9331479 (See Top 100 Books)
Focusing on the challenges, directions, and future predictions with the role of 5G in smart healthcare monitoring, this book offers the fundamental concepts and analysis on methods to apply IoT in monitoring devices for diagnosing and transferring data. It also discusses self-managing to help providers improve their experience of care.
Smart Healthcare Monitoring Using IoT with 5G: Challenges, Directions, and Future Predictions illustrates user-focused wearable devices such as Fitbit health monitors and smartwatches where consumers are self-managing and self-monitoring their own health and providers can improve the experience of care. The book goes on to cover new points of security and privacy concerns with the expectation of IoT devices gaining more popularity within the next 10 years. Case studies depicting applications, best practices, as well as future predictions of smart healthcare monitoring by way of a 5G network are also included.
Interested readers of this book will include anyone working or involved in research in the field of smart healthcare which includes, but is not limited to healthcare specialists, Computer Science Engineers, Electronics Engineers, and Pharmaceutical practitioners.
Cover Half Title Series Page Title Page Copyright Page Contents Preface Editor biographies 1. The Internet of Things in Healthcare Management: Potential Applications and Challenges 1.1 Introduction 1.2 Internet of Things in the Healthcare Management 1.3 Internet of Things in Healthcare Management—Potential Applications 1.4 Challenges (Advantages and Barriers) References 2. Blending of Internet of Things and Deep Transfer Learning (DTL): Enabling Innovations in Healthcare (COVID-19) and Applications 2.1 Introduction 2.2 Historical Background 2.3 Technical Background 2.3.1 Deep Transfer Learning 2.3.2 Edge Devices 2.4 Literature Survey 2.5 Description of Proposed Model to Mitigate COVID-19 2.6 Review of Certain Reported Works Related to ML/DL Approaches of COVID-19 Detection, Prediction, and Other Issues 2.7 Challenges 2.8 Future Research Direction 2.9 Conclusion References 3. Potential Applications and Challenges of Internet of Things in Healthcare 3.1 Introduction 3.1.1 Healthcare and Internet of Things 3.1.2 IoT Healthcare 3.1.3 IoT Healthcare Applications 3.2 Literature Survey 3.3 Healthcare Services and Applications in IoT 3.3.1 IoT Healthcare Services 3.3.2 IoT Healthcare Applications 3.3.3 Techniques for Promoting Services 3.4 IoT Healthcare Security Issues and Challenges 3.4.1 Security Issues 3.4.2 IoT Healthcare Challenges 3.5 IoT Wearable Sensors 3.5.1 Health Monitoring Wearable Systems 3.6 Conclusion and Future Scope References 4. IoT and Smart Health Management 4.1 Introduction 4.2 Defining the IoT 4.3 The Architecture of the IoT 4.4 Application of the IoT 4.5 The IoT in Poland—A Case Study 4.6 Smart Health Management—Interpretation of the Phenomenon References 5. Current Status of Alzheimer's Disease in India: Prevalence, Stigma, and Myths 5.1 Background 5.2 Introduction 5.3 Changes Occur in Brain during AD 5.4 Mild Cognitive Impairment 5.5 Neural Mechanism 5.6 Pathobiogenesis 5.7 Impairment in Mitochondria Leads to Production of Reactive Oxygen Species 5.8 Genes Linked with Alzheimer Disease 5.8.1 Amyloid Precursor Protein 5.8.2 PSEN-1 and PSEN-2 Genes 5.8.3 Tau Gene 5.8.4 APOE Gene 5.9 Blood-Brain Barriers 5.10 Activation of Microglia to Generate Response to Injury 5.11 Early Symptoms for Diagnosis of Alzheimer's 5.12 Magnetic Resonance Imaging 5.13 Medications for Alzheimer's 5.13.1 Inhibitors of Cholinesterase 5.13.1.1 Tacrine 5.13.1.2 Donepezil 5.13.1.3 Rivastigmine 5.13.1.4 Galantamine 5.13.2 Neuronal Protection 5.13.2.1 Estrogen 5.13.2.2 Vitamin E 5.13.2.3 Anti-inflammatory Drugs 5.14 CSF and Plasma Protein Dependent Biomarkers 5.15 Stigma 5.16 Care Giving in India 5.17 Non-Governmental Organizations Providing Shelter to the Dementia Patients [] 5.18 No Awareness in India 5.19 Myths Associated with AD 5.19.1 Myth 5.19.2 Myth 5.19.3 Myth 5.19.4 Myth 5.19.5 Myth 5.19.6 Myth 5.19.7 Myth 5.20 Indian Attitude toward Mental Illness 5.21 Population Aging in India 5.22 Characteristics of Dementia 5.23 Conclusion References 6. Phytochemicals' Potential to Reverse the Process of Neurodegeneration 6.1 Introduction 6.2 Alzheimer's Disease 6.3 Symptoms 6.4 Epidemiology 6.5 Etiology 6.5.1 Amyloid Hypothesis 6.5.2 Tau Hypothesis 6.6 Reduction in Antioxidant Enzymes in Brain 6.7 Role of Phytochemicals or Polyphenols 6.7.1 Curcumin 6.7.1.1 Mechanism of Action of Curcumin 6.7.2 AD Treatment: Curcumin Act as the Pleiotropic Agent 6.7.3 Clinical Aspects of Curcumin 6.7.4 Quercetin 6.7.4.1 Role of Quercetin in Neuroprotection: In Vivo Studies on Humans and Animals 6.7.5 EGCG (Epigallocatechin-3-Gallate) and FA (Ferulic Acid) 6.7.6 Supplementation of Choline 6.7.7 Resveratrol 6.7.8 Holy Shrub: Yarba Santa (Sterubin) 6.7.9 Safranal 6.7.10 Onosma tauricu 6.7.11 Matricaria chamomilla L. 6.7.12 Allylguaiacol 6.8 Conclusion References 7. Existing Methods and Emerging Trends for Novel Coronavirus (COVID-19) Detection Using Residual Network (ResNet): A Review on Deep Learning Analysis 7.1 Introduction 7.2 Materials 7.3 Methods 7.3.1 Residual Network 7.3.1.1 ResNet Architecture 7.3.1.2 Using ResNet with Keras 7.4 Review and Discussion 7.4.1 Pre-Trained Model with Deep Learning ResNet 7.4.1.1 X-Ray and ResNet 7.4.1.2 CT-scan and ResNet 7.4.2 Customized Deep Learning Methods 7.5 Future Scope and Limitations of This Study 7.6 Conclusion References 8. Clinical Impact of COVID on Diabetic Patients 8.1 Background 8.2 Introduction 8.3 Replication and Pathogenesis of Coronavirus 8.4 COVID-19 Transmission 8.5 Clinical Course (COVID-19) 8.6 Criteria for Diagnosis 8.7 Available Clinical Therapies for COVID-19 patients 8.8 Current Updated Status of Coronavirus 8.8.1 Situation Report 1: January 31, 8.8.2 Situation Report 2: February 6, 8.8.3 Situation Report 3: February 13, 8.8.4 Situation Report 4: February 21, 8.8.5 Situation Report 5: February 28, 8.8.6 Situation Report 6: March 9, 8.8.7 Situation Report 7: March 14, 8.8.8 Situation Report 8: March 22, 8.8.9 Situation Report 9: March 28, 8.8.10 Situation Report 10: April 5, 8.8.11 Situation Report 11: April 9, 8.9 Any Possible Relationship of COVID-19 Infection with Diabetic Patients 8.10 Conclusion References 9. Smart Hospitals Using Artificial Intelligence and Internet of Things for COVID-19 Pandemic 9.1 Introduction 9.2 Literature Review 9.3 Introduction to Smart Hospitals: A Digital Transformation in Healthcare 9.4 Technologies Involved in the Construction of Smart Hospitals 9.4.1 Google Cloud Platforms—Firebase 9.4.2 Real-Time Database Concept 9.4.3 Internet of Things 9.4.4 Artificial Intelligence 9.4.5 Android Operating System 9.5 Implementation of the Smart Hospital System 9.5.1 Initial Phase: Installation of Smart Devices in Hospitals 9.5.2 Raspberry Pi 9.5.3 pcDuino 9.5.4 Beagle Bone Black 9.5.5 Cubie Board 9.5.6 Sensors 9.5.7 Subsequent Phase: Development of Mobile Application 9.6 Doctor's Platform 9.7 Nurse's Platform 9.8 Patient's Platform 9.9 The Layout of the Smart Hospital System: Designing the Future Healthcare 9.10 Conclusion References 10. Researcher Issues and Future Directions in Healthcare Using IoT and Machine Learning 10.1 Introduction 10.2 Literature Survey 10.3 Challenges in Digital Healthcare Adoption 10.3.1 What Can IoT Do for Healthcare 10.4 Wearable Health Devices 10.5 Security Issues in Wearable Devices 10.6 Vital Signs—Most Important to be Monitored 10.6.1 Valuable Vital Sign 10.6.2 Pulse (HR) 10.6.3 Blood Pressure 10.6.4 Respiration Rate 10.6.5 Blood Oxygen Saturation (SpO2) 10.6.6 Skin Perspiration 10.6.7 Capnography 10.6.8 Internal Heat Level 10.6.9 Cardiovascular Implantable Devices 10.7 IoT and AI as Together 10.8 AI and IoT In Healthcare 10.8.1 Gadgets with Actual Interfaces to/from This Present Reality 10.8.2 Organized Information Contribution through Sensors 10.8.3 Minuscule Information/Yield Gadgets 10.8.4 Human-Machine-Climate Framework Drivers 10.8.5 Continuous Activity and Choice Control 10.9 Impactful Uniqueness of IoT Associated with Medical Field 10.10 Future Model of Healthcare Based IoT and Machine Learning 10.10.1 IoT Architecture for Disease Detection 10.10.2 Improving Appropriation of Medical Care System with IoT and Machine Learning 10.11 Conclusion References 11. Diseases Prediction and Diagnosis System for Healthcare Using IoT and Machine Learning 11.1 Introduction 11.1.1 Study of ML Algorithms for Disease Prediction 11.1.2 IoT in Healthcare 11.1.2.1 Applications of IoT in Healthcare 11.2 Health Monitoring Using IoT and Machine Learning 11.2.1 Materials and Methods Used for Health Monitoring 11.2.1.1 Heartbeat Rate Sensor 11.2.1.2 Dataset 11.2.1.3 Information Pre-processing 11.2.1.4 Machine Learning Classifiers 11.3 Applying Machine Learning in IoT Data for Disease Prediction and Diagnosis Model 11.3.1 Brain Tumor Classification Using Machine Learning and Role of IoT in Providing Solution 11.3.1.1 IoT Thingspeak Platform 11.3.1.2 Required Model and its Stages 11.3.1.3 Implementation and Result 11.3.2 Early Detection of Dementia Disease Using Machine Learning and IoT Based Application 11.3.2.1 Characteristics of Dementia and Its Adverse Effects of Dementia 11.3.2.2 Identifying Dementia Onset 11.3.2.3 Model Development and Performance Analysis 11.3.2.4 Features Extracting Algorithm from CASAS Dataset 11.3.2.5 Discussion and Result 11.3.3 A Segmentation and Classification IoT Model for Predicting Lung Cancer 11.3.3.1 System Architecture 11.3.3.2 Results and Discussion 11.4 Heart Disease Prediction Model Using IoT and ML Algorithms 11.4.1 Introduction 11.4.2 IoT-Based Disease Diagnosis Model 11.4.3 Results and Conclusion 11.5 Automated Nutrition Monitoring System Using IoT and Deep Learning 11.5.1 Introduction 11.5.2 Smart Log System 11.5.3 Result and Conclusion 11.6 Monitoring of Arrhythmia Patients in Real Time Via IoT and ML 11.6.1 Arrhythmia Patient Monitoring and Classification 11.6.1.1 Data Sensing 11.6.1.2 Classification and Visualization 11.6.2 Results and Discussion 11.6.3 Conclusion 11.7 IoT in Healthcare: An Overview of Benefits and Challenges 11.7.1 Benefits of Applying IoT 11.7.2 Challenges of IoT in Healthcare 11.8 ML Applications to IoT 11.9 Conclusion and Future Scope References 12. Challenges and Solution of COVID-19 Pandemic Based on AI and Big Data 12.1 Introduction 12.2 Artificial Intelligence in COVID-19 12.2.1 Detecting COVID-19 from Chest Imaging Technique (X-Ray) 12.2.2 Medical Chatbots to Address Public Enquiries 12.2.3 Social Control through Robots and Drones 12.3 Big Data in COVID-19 12.3.1 Modelling of Disease Activity, Its Growth, and Areas of Spread 12.3.2 Modelling of Preparedness and Vulnerability of Countries 12.4 Challenges in COVID-19 Predictions Using AI and Big Data 12.5 COVID-19 Impacts and Proposed Solutions 12.5.1 Worldwide Impacts of COVID-19 12.5.1.1 Impact on the Economy 12.5.1.2 Impact on the Environment 12.5.1.3 Impact on the Society 12.5.1.4 Impact on the Functioning of Organizations 12.5.2 Proposed Solutions 12.5.2.1 Lockdown and Restriction of Mass Gathering 12.5.2.2 Afforestation 12.5.2.3 Population Control 12.5.2.4 Ban on Wildlife Trade 12.5.2.5 Strictness to Personal Hygiene 12.6 Conclusion and Future Scope Acknowledgment References 13. A Review of Artificial Intelligence Applications for COVID-19 Contact Tracing 13.1 Introduction 13.2 Classifying Contact Tracing Segments 13.3 Aims of Each Segment 13.4 Applications of AI in the Contact Tracing Segments 13.5 Conclusion References Index
Donate to keep this site alive
1. Disable the AdBlock plugin. Otherwise, you may not get any links.
2. Solve the CAPTCHA.
3. Click download link.
4. Lead to download server to download.