This book shares the knowledge of active and prestigious worldwide researchers and scholars in the field of healthcare monitoring as authors investigate historical developments, summarize latest advancements, and envision future prospects on wearable, attachable, and invisible devices that monitor diverse physiological information. The coverage of the book spans multiple disciplines, from biomechanics, to bioelectricity, biochemistry, biophysics and biomaterials. There is also wide coverage of various physical and chemical quantities such as electricity, pressure, flow, motion, force, temperature, gases, and biomarkers. Each chapter explores the background of a specific monitoring device, as well as its physical and chemical principles and instrumentation, signal processing and data analysis, achieved outcomes and application scenarios, and future research topics. There are chapters on: Electrocardiograms, electroencephalograms, and electromyograms Measurement of flow phenomenon Latest wearable technologies for the quantification of human motion Various forms of wearable thermometers Monitoring of gases and chemical substances produced during metabolism...and more! This book is appropriate and accessible for students and scientists, as well as researchers in biomedical engineering, computer engineers, healthcare entrepreneurs, administrative officers, policy makers, market vendors, and healthcare personnel. It helps to provide us with insights into future endeavors, formulate innovative businesses and services, and will help improve people’s health and quality of life.

v. 1. Research findings -- v. 2. Concepts and methodology -- v. 3. Implementation issues -- v. 4. Programs, tools and products.

The third edition of this hands-on textbook pursues the focus on the principles of wireless sensor networks (WSNs), their applications, their protocols and standards, and their analysis and test tools; a meticulous care has been accorded to the definitions and terminology. To make WSNs felt and seen, the adopted technologies as well as their manufacturers are presented in detail. In introductory computer networking books, chapters sequencing follows the bottom up or top down architecture of the seven layers protocol. This book is some more steps after, both horizontally and vertically, the view and understanding are getting clearer, chapters ordering is based on topics significance to the elaboration of wireless sensor networks (WSNs) concepts and issues. This book is intended for a wide audience, it is meant to be help and motivate, for both the senior undergraduates, postgraduates, researchers, and practitioners; concepts and WSNs related applications are laid out, research and practical issues are backed by appropriate literature, and new trends are put under focus. For senior undergraduate students, it familiarizes with conceptual foundations, applications and practical projects implementations. For graduate students and researchers, energy-efficient routing protocols, transport layer protocols and cross-layering protocols approach are presented. Testbeds and simulators provide a must follow emphasis on the analysis methods and tools for WSNs. For practitioners, besides applications and deployment, the manufacturers and components of WSNs at several platforms and testbeds are fully explored.

This book focuses on the principles of wireless sensor networks (WSNs), their applications, and their analysis tools, with meticulous attention paid to definitions and terminology. This book presents the adopted technologies and their manufacturers in detail, making WSNs tangible for the reader. In introductory computer networking books, chapter sequencing follows the bottom-up or top-down architecture of the 7-layer protocol. This book addresses subsequent steps in this process, both horizontally and vertically, thus fostering a clearer and deeper understanding through chapters that elaborate on WSN concepts and issues. With such depth, this book is intended for a wide audience; it is meant to be a helper and motivator for senior undergraduates, postgraduates, researchers, and practitioners. It lays out important concepts and WSN-relate applications; uses appropriate literature to back research and practical issues; and focuses on new trends. Senior undergraduate students can use it to familiarize themselves with conceptual foundations and practical project implementations. For graduate students and researchers, test beds and simulators provide vital insights into analysis methods and tools for WSNs. Lastly, in addition to applications and deployment, practitioners will be able to learn more about WSN manufacturers and components within several platforms and test beds.

Sensors for Health Monitoring discusses the characteristics of U-Healthcare systems in different domains, providing a foundation for working professionals and undergraduate and postgraduate students. The book provides information and advice on how to choose the best sensors for a U-Healthcare system, advises and guides readers on how to overcome challenges relating to data acquisition and signal processing, and presents comprehensive coverage of up-to-date requirements in hardware, communication and calculation for next-generation uHealth systems. It then compares new technological and technical trends and discusses how they address expected u-Health requirements. In addition, detailed information on system operations is presented and challenges in ubiquitous computing are highlighted. The book not only helps beginners with a holistic approach toward understanding u-Health systems, but also presents researchers with the technological trends and design challenges they may face when designing such systems. - Presents an outstanding update on the use of U-Health data analysis and management tools in different applications, highlighting sensor systems - Highlights Internet of Things enabled U-Healthcare - Covers different data transmission techniques, applications and challenges with extensive case studies for U-Healthcare systems

This book is a comprehensive collection of chapters focusing on the core areas of computing and their further applications in the real world. Each chapter is a paper presented at the Computing Conference 2021 held on 15-16 July 2021. Computing 2021 attracted a total of 638 submissions which underwent a double-blind peer review process. Of those 638 submissions, 235 submissions have been selected to be included in this book. The goal of this conference is to give a platform to researchers with fundamental contributions and to be a premier venue for academic and industry practitioners to share new ideas and development experiences. We hope that readers find this volume interesting and valuable as it provides the state-of-the-art intelligent methods and techniques for solving real-world problems. We also expect that the conference and its publications is a trigger for further related research and technology improvements in this important subject.

ADVANCED HEALTHCARE SYSTEMS This book offers a complete package involving the incubation of machine learning, AI, and IoT in healthcare that is beneficial for researchers, healthcare professionals, scientists, and technologists. The applications and challenges of machine learning and artificial intelligence in the Internet of Things (IoT) for healthcare applications are comprehensively covered in this book. IoT generates big data of varying data quality; intelligent processing and analysis of this big data are the keys to developing smart IoT applications, thereby making space for machine learning (ML) applications. Due to its computational tools that can substitute for human intelligence in the performance of certain tasks, artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Since IoT platforms provide an interface to gather data from various devices, they can easily be deployed into AI/ML systems. The value of AI in this context is its ability to quickly mesh insights from data and automatically identify patterns and detect anomalies in the data that smart sensors and devices generate—information such as temperature, pressure, humidity, air quality, vibration, and sound—that can be really helpful to rapid diagnosis. Audience This book will be of interest to researchers in artificial intelligence, the Internet of Things, machine learning as well as information technologists working in the healthcare sector.

This book brings together leading experts from around the world to explore the transformative potential of Machine Learning (ML) and the Internet of Things (IoT) in healthcare. It provides a platform for studying a future where healthcare becomes more precise, personalized, and accessible for all. The book covers recent advancements that will shape the future of healthcare and how artificial intelligence is revolutionizing disease detection, from analyzing chest X-rays for pneumonia to solving the secrets of our genes. It investigates the transformative potential of smart devices, real-time analysis of heart data, and personalized treatment plan creation. It shows how ML and IoT work and presents real-world examples of how they are leading to earlier and more accurate diagnoses and personalized treatments. Therefore, this edited book will be an invaluable resource for researchers, healthcare professionals, data scientists, or simply someone passionate about the future of healthcare. Readers will discover the exciting possibilities that lie ahead at the crossroads of ML, IoT, and health informatics.

This book provides a practically applicable guide to designing evidence-based medical apps and mHealth interventions. It features detailed guidance and case studies where applicable on the best practices and available techniques from both technological (platform technologies, toolkits, sensors) and research perspectives. This approach enables the reader to develop a deep understanding of how to collect the appropriate data and work with users to build a user friendly app for their target audience. Information on how researchers and designers can communicate their intentions with a variety of stakeholders including medical practitioners, developers and researchers to ensure the best possible decisions are made during the development process to produce an app of optimal quality that also considers usability. Developing Medical Apps and mHealth Interventions comprehensively covers the development of medical and health apps for researchers, informaticians and physicians, and is a valuable resource for the experienced professional and trainee seeking a text on how to develop user friendly medical apps.

When considering the idea of using machine learning in healthcare, it is a Herculean task to present the entire gamut of information in the field of intelligent systems. It is, therefore the objective of this book to keep the presentation narrow and intensive. This approach is distinct from others in that it presents detailed computer simulations for all models presented with explanations of the program code. It includes unique and distinctive chapters on disease diagnosis, telemedicine, medical imaging, smart health monitoring, social media healthcare, and machine learning for COVID-19. These chapters help develop a clear understanding of the working of an algorithm while strengthening logical thinking. In this environment, answering a single question may require accessing several data sources and calling on sophisticated analysis tools. While data integration is a dynamic research area in the database community, the specific needs of research have led to the development of numerous middleware systems that provide seamless data access in a result-driven environment. Since this book is intended to be useful to a wide audience, students, researchers and scientists from both academia and industry may all benefit from this material. It contains a comprehensive description of issues for healthcare data management and an overview of existing systems, making it appropriate for introductory and instructional purposes. Prerequisites are minimal; the readers are expected to have basic knowledge of machine learning. This book is divided into 22 real-time innovative chapters which provide a variety of application examples in different domains. These chapters illustrate why traditional approaches often fail to meet customers’ needs. The presented approaches provide a comprehensive overview of current technology. Each of these chapters, which are written by the main inventors of the presented systems, specifies requirements and provides a description of both the chosen approach and its implementation. Because of the self-contained nature of these chapters, they may be read in any order. Each of the chapters use various technical terms which involve expertise in machine learning and computer science.