Intelligent IoT Systems in Personalized Health Care delivers a significant forum for the technical advancement of IoMT learning in parallel computing environments across biomedical engineering diversified domains and its applications. Pursuing an interdisciplinary approach, the book focuses on methods used to identify and acquire valid, potentially useful knowledge sources. The book presents novel, in-depth, fundamental research contributions from a methodological/application perspective to help readers understand the fusion of AI with IoT and its capabilities in solving a diverse range of problems for biomedical engineering and its real-world personalized health care applications. The book is well suited for researchers exploring the significance of IoT based architecture to perform predictive analytics of user activities in sustainable health. Presents novel, in-depth, fundamental research contributions from a methodological/application perspective to help readers understand the fusion of AI with IoT Illustrates state-of-the-art developments in new theories and applications of IoMT techniques as applied to parallel computing environments in biomedical engineering systems Presents concepts and technologies successfully used in the implementation of today's intelligent data-centric IoT systems and Edge-Cloud-Big data

Smart healthcare technology improves the diagnosis and treatment of patients, provides easy access to medical facilities and emergency care services, and minimizes the gaps between patients and healthcare providers. While clinical data protection remains a major challenge, innovations such as the internet of medical things and smart healthcare systems increase the efficiency and quality of patient care. Healthcare technology can only become faster, more profitable, and more flexible as additional research on its advancements is conducted and collected. Smart Medical Data Sensing and IoT Systems Design in Healthcare is an essential reference source that focuses on robust and easy solutions for the delivery of medical information from patients to doctors and explores low-cost, high-performance, highly efficient, deployable IoT system options in healthcare systems. Featuring research on topics such as hospital management systems, electronic health records, and bio-signals, this book is ideally designed for technologists, engineers, scientists, clinicians, biomedical engineers, hospital directors, doctors, nurses, healthcare practitioners, telemedical agents, students, and academicians seeking coverage on the latest technological developments in medical data analysis and connectivity.

Recently, the fields of Artificial Intelligence (AI) and the Internet of Things (IoT) have revolutionized numerous industries, including healthcare. The convergence of AI and IoT has given birth to smart healthcare systems, transforming the way we deliver, receive, and experience healthcare services. This book explores the profound impact of these technologies on healthcare and presents a comprehensive overview of their applications, challenges, and prospects. Smart Healthcare Systems: AI and IoT Perspectives addresses various aspects of how smart healthcare can be used to detect and analyze diseases, the underlying methodologies, and related security concerns. It also discusses healthcare as a multidisciplinary field that involves a range of sectors such as the financial system, social factors, health technologies, and organizational structures that affect individuals, families, institutions, organizations, and populations’ healthcare. The book presents the goals of healthcare services which include patient safety, timeliness, effectiveness, efficiency, and equity. An outline of what smart healthcare consists of which is m-health, e-health, electronic resource management, smart and intelligent home services, and medical devices is included. Along with highlights on how AI and IoT-enabled healthcare technologies are suitable for remote health monitoring, including rehabilitation, and assisted ambient living. Rounding the offers of this book out is that it also covers how healthcare analytics can be applied to the data gathered from different areas to improve healthcare at a minimum expense. Researchers, Academicians, Industry, R&D Organizations, medical professionals, PG students, and policymakers in the fields of artificial intelligence, the internet of things, healthcare informatics, biomedical engineering, medical informatics, and related subjects can use this book to assist them in making appropriate decisions regarding these emerging disciplines.

Healthcare is an industry that has seen great advancements in personalized services through big data analytics. Despite the application of smart devices in the medical field, the mass volume of data that is being generated makes it challenging to correctly diagnose patients. This has led to the implementation of precise algorithms that can manage large amounts of information and successfully use smart living in medical environments. Professionals worldwide need relevant research on how to successfully implement these smart technologies within their own personalized healthcare processes. Applications of Deep Learning and Big IoT on Personalized Healthcare Services is a pivotal reference source that provides a collection of innovative research on the analytical methods and applications of smart algorithms for the personalized treatment of patients. While highlighting topics including cognitive computing, natural language processing, and supply chain optimization, this book is ideally designed for network designers, analysts, technology specialists, medical professionals, developers, researchers, academicians, and post-graduate students seeking relevant information on smart developments within individualized healthcare.

This book highlights the issues and challenges in personalised healthcare systems. The individual chapters address different aspects of such systems, including the novel Internet of Things (IoT) system architectures in healthcare and emerging e-health based IoT applications. Moreover, the book investigates the impact of cutting-edge innovations on the IoT.

This book presents research on how interpretable cognitive IoT can work to help with the massive amount of data in the healthcare industry. The authors give importance to IoT systems with intense machine learning features; this ensures the scope corresponds to use of cognitive IoT for understanding, reasoning, and learning from medical data. The authors discuss the interpretability of an intelligent system and its trustworthiness as a smart tool in the context of massive healthcare applications. As a whole, book combines three important topics: massive data, cognitive IoT, and interpretability. Topics include health data analytics for cognitive IoT, usability evaluation of cognitive IoT for healthcare, interpretable cognitive IoT for health robotics, and wearables in the context of IoT for healthcare. The book acts as a useful reference work for a wide audience including academicians, scientists, students, and professionals.

Intelligent IoT Systems in Personalized Health Care delivers a significant forum for the technical advancement of IoMT learning in parallel computing environments across biomedical engineering diversified domains and its applications. Pursuing an interdisciplinary approach, the book focuses on methods used to identify and acquire valid, potentially useful knowledge sources. The book presents novel, in-depth, fundamental research contributions from a methodological/application perspective to help readers understand the fusion of AI with IoT and its capabilities in solving a diverse range of problems for biomedical engineering and its real-world personalized health care applications. The book is well suited for researchers exploring the significance of IoT based architecture to perform predictive analytics of user activities in sustainable health. Presents novel, in-depth, fundamental research contributions from a methodological/application perspective to help readers understand the fusion of AI with IoT Illustrates state-of-the-art developments in new theories and applications of IoMT techniques as applied to parallel computing environments in biomedical engineering systems Presents concepts and technologies successfully used in the implementation of today's intelligent data-centric IoT systems and Edge-Cloud-Big data