Health 5.0

This book presents a detailed explanation of the concepts, taxonomy, and challenges in Healthcare 4.0 and provides frameworks and architectures for the adaptation and fusion of emerging technologies under Healthcare 5.0. The healthcare industry requires technological upgrades to provide precise, accurate, and automatic remote health monitoring and diagnosis. This reference book offers an early opportunity to explore emerging technologies and understand current issues, problems, and concepts. Health 5.0: Concepts, Challenges, and Solutions provides state-of-the-art research topics and opportunities of IoMT in personalized healthcare. It presents diversified IoMT applications for smart healthcare that supports lifestyle changes as a preventive measure and discusses health and care for all citizens to lead a good quality of life. The book illustrates the advancements of IoMT in augmented reality and AI and offers use-cases of edge computing for remote healthcare. The book also highlights the architectures, frameworks, and current research issues in IoMT and goes on to focus on sustainable healthcare for smart cities using edge computing, IoMT, and big data. The details of the development of advanced therapeutic instrumentation, along with the promotion of digital therapy, are also provided. The primary audience for this book includes academicians, researcher scholars, IoMT developers, cloud-based health application designers, and medical practitioners who are engaged in providing healthcare services using recent technologies.

Dr. Hiren Kumar Thakkar received his M. Tech in CSE from IIIT Bhubaneswar, India in 2012 and a Ph.D. degree from Chang Gung University, Taiwan in 2018. Later, he worked as a postdoctoral fellow at Motor Behavioral Research Lab (MBRL), at Chang Gung University, Taiwan. Before joining PDEU, he worked as an Associate Professor at Nirma University, Ahmedabad. His research Interest includes Healthcare Data Analysis, Cloud Resource Management, and Opinion Feature Mining. He has published several SCI research papers in peer-reviewed journals and is a member of IEEE. Dr. Chintan Bhatt is an Assistant Professor in the Department of Computer Science and Engineering (CSE), School of Technology, Pandit Deendayal Energy University (PDEU). Before joining PDEU, Dr. Bhatt served as an Assistant Professor in the CE department (CSPIT, CHARUSAT) for 11 years. Dr. Bhatt is the author and co-author of 80+ publications in the areas of Computer Vision, the Internet of Things, and Fog Computing. He has won several awards including "CSI Award" and "Best Paper Award" for his CSI articles and conference publications. Dr. Victor C. M. Leung is an Emeritus Professor of Electrical and Computer Engineering at the University of British Columbia, Canada, and a Distinguished Professor of Computer Science and Software Engineering at Shenzhen University, China. He has co-authored numerous refereed articles in the broad areas of wireless networks and mobile systems and is a Clarivate Analytics "Highly Cited Researcher". Dr. received the 2017 IEEE ComSoc Fred W. Ellersick Prize, the 2017 IEEE Systems Journal Best Paper Award, the 2017 Canadian Award for Telecommunications Research, the 2018 IEEE TCGCC Distinguished Technical Achievement Recognition Award, the 2018 IEEE CSIM Best Journal Paper Award, and the 2019 IEEE TCGCC Best Journal Paper Award, as well as several other awards. He is a Life Fellow of IEEE, and a Fellow of the Royal Society of Canada, Canadian Academy of Engineering, and Engineering Institute of Canada. Dr. Ilangko Balasingham received the Siv. Ing.(MSc) and Dr. Ing.(Ph.D.) degrees both in signal processing from the Norwegian University of Science and Technology (NTNU), Trondheim, Norway in 1993 and 1998, respectively. He did his Masters thesis at the University of California in Santa Barbara. From 1998 to 2002, he was employed as a Research Engineer developing video streaming solutions for mobile handheld devices at Fast Search & Transfer ASA, Oslo, Norway. Since 2002, he has been with the Intervention Center, Oslo University Hospital - Rikshospitalet, Oslo, Norway as a Senior Research Scientist and Founder and Head of the Biomedical Sensor Network Research Group. He was appointed as a Professor of Medical Signal Processing and Communications at NTNU in 2006. In 2017, He was promoted to Head of the Section for Medical ICT R&D at the Intervention Center. He was a Professor by courtesy at the Frontier Institute, Nagoya Institute of Technology, Nagoya, Japan during the academic year 2016-2017. His research interests include medical signal and image processing, wireless biomedical sensor networks for short-range sensing, imaging, localization and communication, and multimedia patient record systems. Lately, he has been involved in nano-scale communication networks - molecular communications using biology and nanotechnology for intra-body applications, minimally invasive diagnosis, monitoring, and drug delivery.

Introduction. Part I: The Conventional Medicare, Health 5.0, and Next-Generation Internet of Medical Things (IoMT). 1. The History, Concepts, and Conventional Medicare Technologies. 2. Recent Advancements and Issues in Health 5.0. 3. Smart healthcare and IoMT: Best practices and perspectives of AI and ML. Part II: Intelligent IoMT and New Age Computing: Principles and Perspective with AI and Edge Computing. 4. Issues and Challenges of Intelligent IoMT for Edge Computing. 5. Data Generation, Storage, and Processing of IoMT Healthcare Data in Edge Computing. 6. Advancements of IoMT and AI for Advancement of Health Monitoring. Part III: IoMT Frameworks and Architectures for Artificial Intelligence (AI) in Augmented Reality. 7. AI-Enabled IoMT Frameworks for Automated Health Diagnosis. 8. AI-enabled Mobile Cloud-based Federated IoMT Architecture for Ambient Assisted Healthy Living. 9. Symptom-Based Real-Time Augmented Pattern Detection and Recognition for Healthy Living. Part IV: AI-Enabled Smart Models and Algorithms for IoMT Augmented Data Analysis. 10. Open CV Algorithm for IoMT-Based Patient Emotion Pattern Analysis. 11. Explainable AI Algorithms for Augmented Healthcare Hybridization. 12. Optimized Predictive Algorithms for Augmented Intelligence in Personalized e-health. Part V: IoMT Applications and its Future Trends for AI, Augmented Reality, and Edge Computing. 13. Sentiment analysis and Social Media Analysis for IoMT-Enabled Data. 14. Deep Learning for Augmented Medical Image Dara Analysis.