Advances in ubiquitous sensing applications for healthcare – serie

Healthcare Data Analytics and Management help readers disseminate cutting-edge research that delivers insights into the analytic tools, opportunities, novel strategies, techniques and challenges for handling big data, data analytics and management in healthcare. As the rapidly expanding and heterogeneous nature of healthcare data poses challenges for big data analytics, this book targets researchers and bioengineers from areas of machine learning, data mining, data management, and healthcare providers, along with clinical researchers and physicians who are interested in the management and analysis of healthcare data.

Covers data analysis, management and security concepts and tools in the healthcare domain Highlights electronic medical health records and patient information records Discusses the different techniques to integrate Big data and Internet-of-Things in healthcare, including machine learning and data mining Includes multidisciplinary contributions in relation to healthcare applications and challenges

Wearable and Implantable Medical Devices: Applications and Challenges, Fourth Edition highlights the new aspects of wearable and implanted sensors technology in the healthcare sector and monitoring systems. The book's contributions include several interdisciplinary domains, such as wearable sensors, implanted sensors devices, Internet-of-Things (IoT), security, real-time medical healthcare monitoring, WIBSN design and data management, encryption, and decision-support systems. Contributions emphasize several topics, including real-world applications and the design and implementation of wearable devices. This book demonstrates that this new field has a brilliant future in applied healthcare research and in healthcare monitoring systems.

Includes comprehensive information on wearable and implanted device technology, wearable and implanted sensors design, WIBSN requirements, WIBSN in monitoring systems and security conceptsHighlights machine learning and computing in healthcare monitoring systems based on WIBSNIncludes a multidisciplinary approach to different healthcare applications and their associated challenges based on wearable and implanted technologies

U-Healthcare Monitoring Systems: Volume One: Design and Applications focuses on designing efficient U-healthcare systems which require the integration and development of information technology service/facilities, wireless sensors technology, wireless communication tools, and localization techniques, along with health management monitoring, including increased commercialized service or trial services. These u-healthcare systems allow users to check and remotely manage the health conditions of their parents. Furthermore, context-aware service in u-healthcare systems includes a computer which provides an intelligent service based on the user's different conditions by outlining appropriate information relevant to the user's situation.

This volume will help engineers design sensors, wireless systems and wireless communication embedded systems to provide an integrated u-healthcare monitoring system. This volume provides readers with a solid basis in the design and applications of u-healthcare monitoring systems.

Provides a solid basis in the design and applications of the u-healthcare monitoring systems Illustrates the concept of the u-healthcare monitoring system and its requirements, with a specific focus on the medical sensors and wireless sensors communication Presents a multidisciplinary volume that includes different applications of the monitoring system which highlight the main features for biomedical sensor devices

Advances in Ubiquitous Computing: Cyber-Physical Systems, Smart Cities and Ecological Monitoring debuts some of the newest methods and approaches to multimodal user-interface design, safety compliance, formal code verification and deployment requirements, as they pertain to cyber-physical systems, smart homes and smart cities, and biodiversity monitoring. In this anthology, the authors assiduously examine a panoply of topics related to wireless sensor networks. These topics include interacting with smart-home appliances and biomedical devices, designing multilingual speech recognition systems that are robust to vehicular, mechanical and other noises common to large metropolises, and an examination of new methods of speaker recognition to control for the emotion-state of the speaker, which can easily impede speaker verification over a wireless medium.

This volume recognizes that any discussion of pervasive computing in smart cities must not end there, as the perilous effects of climate change proves that our lives are not circumscribed by the geographically sculpted boundaries of cities, counties, countries, or continents. Contributors address present and emerging technologies of scalable biodiversity monitoring: pest control, disease transmission, environmental monitoring, and habitat preservation. The need to collect, store, process, and interpret vast amounts of data originating from sources spread over large areas and for prolonged periods of time requires immediate data storage and processing, reliable networking, and solid communication infrastructure, along with intelligent data analysis and interpretation methods that can resolve contradictions and uncertainty in the data-all of which can be bolstered by modern advances in ubiquitous computing.

Examines the history, scope and advances in ubiquitous computing, including threats to wildlife, tracking of disease, smart cities and Wireless Sensor Networks Discusses user interface design, implementation and deployment of cyber-physical systems, such as wireless sensor networks, Internet of Things devices, and other networks of physical devices that have computational capabilities and reporting devices Covers the need for improved data sharing networks

Classification Techniques for Medical Image Analysis and Computer Aided Diagnosis covers the most current advances on how to apply classification techniques to a wide variety of clinical applications that are appropriate for researchers and biomedical engineers in the areas of machine learning, deep learning, data analysis, data management and computer-aided diagnosis (CAD) systems design. The book covers several complex image classification problems using pattern recognition methods, including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Bayesian Networks (BN) and deep learning. Further, numerous data mining techniques are discussed, as they have proven to be good classifiers for medical images.

Examines the methodology of classification of medical images that covers the taxonomy of both supervised and unsupervised models, algorithms, applications and challenges Discusses recent advances in Artificial Neural Networks, machine learning, and deep learning in clinical applications Introduces several techniques for medical image processing and analysis for CAD systems design

Real-Time Data Analytics for Large-Scale Sensor Data covers the theory and applications of hardware platforms and architectures, the development of software methods, techniques and tools, applications, governance and adoption strategies for the use of massive sensor data in real-time data analytics. It presents the leading-edge research in the field and identifies future challenges in this fledging research area. The book captures the essence of real-time IoT based solutions that require a multidisciplinary approach for catering to on-the-fly processing, including methods for high performance stream processing, adaptively streaming adjustment, uncertainty handling, latency handling, and more.

Examines IoT applications, the design of real-time intelligent systems, and how to manage the rapid growth of the large volume of sensor data Discusses intelligent management systems for applications such as healthcare, robotics and environment modeling Provides a focused approach towards the design and implementation of real-time intelligent systems for the management of sensor data in large-scale environments

Big Data Analytics for Intelligent Healthcare Management covers both the theory and application of hardware platforms and architectures, the development of software methods, techniques and tools, applications and governance, and adoption strategies for the use of big data in healthcare and clinical research. The book provides the latest research findings on the use of big data analytics with statistical and machine learning techniques that analyze huge amounts of real-time healthcare data.

Examines the methodology and requirements for development of big data architecture, big data modeling, big data as a service, big data analytics, and more Discusses big data applications for intelligent healthcare management, such as revenue management and pricing, predictive analytics/forecasting, big data integration for medical data, algorithms and techniques, etc. Covers the development of big data tools, such as data, web and text mining, data mining, optimization, machine learning, cloud in big data with Hadoop, big data in IoT, and more

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

Systems Simulation and Modelling for Cloud Computing and Big Data Applications provides readers with the most current approaches to solving problems through the use of models and simulations, presenting SSM based approaches to performance testing and benchmarking that offer significant advantages. For example, multiple big data and cloud application developers and researchers can perform tests in a controllable and repeatable manner. Inspired by the need to analyze the performance of different big data processing and cloud frameworks, researchers have introduced several benchmarks, including BigDataBench, BigBench, HiBench, PigMix, CloudSuite and GridMix, which are all covered in this book.

Despite the substantial progress, the research community still needs a holistic, comprehensive big data SSM to use in almost every scientific and engineering discipline involving multidisciplinary research. SSM develops frameworks that are applicable across disciplines to develop benchmarking tools that are useful in solutions development.

Examines the methodology and requirements of benchmarking big data and cloud computing tools, advances in big data frameworks and benchmarks for large-scale data analytics, and frameworks for benchmarking and predictive analytics in big data deployment Discusses applications using big data benchmarks, such as BigDataBench, BigBench, HiBench, MapReduce, HPCC, ECL, HOBBIT, GridMix and PigMix, and applications using big data frameworks, such as Hadoop, Spark, Samza, Flink and SQL frameworks Covers development of big data benchmarks to evaluate workloads in state-of-the-practice heterogeneous hardware platforms, advances in modeling and simulation tools for performance evaluation, security problems and scalable cloud computing environments

Smart Biosensors in Medical Care discusses the characteristics of biosensors and their potential applications in healthcare. This book is aimed at professionals, scientists and engineers who are interested in integrating biosensors into medical care systems for patients. It also provides fundamental and foundational knowledge for undergraduate and post graduate students. The book presents a comprehensive view of up-to-date requirements in hardware and communication, offers future perspectives on next-generation medical care systems, and includes global case studies of recent system operations in healthcare. Sections cover smart biosensors, such as wearable, implantable, patch based, and enzyme based for medical care.

Advances in ubiquitous sensing applications for healthcare is a series which covers new systems based on ubiquitous sensing for healthcare (USH). Volumes in this series cover a wide range of interdisciplinary areas, including wireless sensors networks, wireless body area networks, Big data, Internet-of-Things (IoT), security, monitoring, real time data collection, data management, systems design/analysis, and much more.

Covers the basics of biosensor based medical care data analysis and management Discusses data transmission techniques, presenting applications with extensive studies for biosensor based medical healthcare systems Offers solutions to the challenges of designing biosensor based medical healthcare systems

Compressive Sensing in Healthcare, part of the Advances in Ubiquitous Sensing Applications for Healthcare series gives a review on compressive sensing techniques in a practical way, also presenting deterministic compressive sensing techniques that can be used in the field. The focus of the book is on healthcare applications for this technology. It is intended for both the creators of this technology and the end users of these products. The content includes the use of EEG and ECG, plus hardware and software requirements for building projects. Body area networks and body sensor networks are explored.

Provides a toolbox for compressive sensing in health, presenting both mathematical and coding information Presents an intuitive introduction to compressive sensing, including MATLAB tutorials Covers applications of compressive sensing in health care

Security and Privacy Issues in IoT Devices and Sensor Networks investigates security breach issues in IoT and sensor networks, exploring various solutions. The book follows a two-fold approach, first focusing on the fundamentals and theory surrounding sensor networks and IoT security. It then explores practical solutions that can be implemented to develop security for these elements, providing case studies to enhance understanding. Machine learning techniques are covered, as well as other security paradigms, such as cloud security and cryptocurrency technologies. The book highlights how these techniques can be applied to identify attacks and vulnerabilities, preserve privacy, and enhance data security.

This in-depth reference is ideal for industry professionals dealing with WSN and IoT systems who want to enhance the security of these systems. Additionally, researchers, material developers and technology specialists dealing with the multifarious aspects of data privacy and security enhancement will benefit from the book's comprehensive information.

Provides insights into the latest research trends and theory in the field of sensor networks and IoT security Presents machine learning-based solutions for data security enhancement Discusses the challenges to implement various security techniques Informs on how analytics can be used in security and privacy

Translational Bioinformatics in Healthcare and Medicine offers an overview of main principles of bioinformatics, biological databases, clinical informatics, health informatics, viroinformatics and real-case applications of translational bioinformatics in healthcare. Written by experts from both technology and clinical sides, the content brings together essential knowledge to make the best of recent advancements of the field.

The book discusses topics such as next generation sequence analysis, genomics in clinical care, IoT applications, blockchain technology, patient centered interoperability of EHR, health data mining, and translational bioinformatics methods for drug discovery and drug repurposing. In addition, it discusses the role of bioinformatics in cancer research and viroinformatics approaches to counter viral diseases through informatics.

This is a valuable resource for bioinformaticians, clinicians, healthcare professionals, graduate students and several members of biomedical field who are interested in learning more about how bioinformatics can impact in their research and practice.

Covers recent advancements in translational bioinformatics and its healthcare applications Discusses integrative and multidisciplinary approaches to U-healthcare systems development and management Bridges the gap among various knowledge domains in the field, integrating both technological and clinical knowledge into practical content

Computational Intelligence for Medical Internet of Things (MIoT) Applications: Machine Intelligence Applications for IoT in Healthcare explores machine intelligence techniques necessary for effective MIoT research and practice, taking a practical approach for practitioners and students entering the field. This book investigates advanced concepts and applications in the MIoT field, guiding readers through emerging developments and future trends. A wide range of international authors guide readers through advanced concepts, including deep learning, neural network, and big data analytic approaches for the classification, indexing, retrieval, analysis, and inferencing of healthcare data. Presents the state-of-the-art in machine intelligence and related technologies and methodologies for IoT in healthcare Discusses emerging developments and trends in machine intelligence for business and decision-making strategy in healthcare Features new models, practical solutions, prototypes, frameworks and technological advances related to machine intelligence for MIoT applications

Intelligent Biomedical Technologies and Applications for Healthcare 5.0, Volume Sixteen covers artificial health intelligence, biomedical image analysis, 5G, the Internet of Medical Things, intelligent healthcare systems, and extended health intelligence (EHI). This volume contains four sections. The focus of the first section is health data analytics and applications. The second section covers research on information exchange and knowledge sharing. The third section is on the Internet of Things (IoT) and the Internet of Everything (IoE)-based solutions. The final section focuses on the implementation, assessment, adoption, and management of healthcare informatics solutions.This new volume in the Advances in Ubiquitous Sensing Applications for Healthcare series focuses on innovative methods in the healthcare industry and will be useful for biomedical engineers, researchers, and students working in interdisciplinary fields of research. This volume bridges these newly developing technologies and the medical community in the rapidly developing healthcare world, introducing them to modern healthcare advances such as EHI and Smart Healthcare Systems.

Provides a comprehensive technological review of cutting-edge information in the wide domain of Healthcare 5.0Introduces concepts that combine computational methods, network standards, and healthcare systems to provide a much improved, more affordable experience delivered by healthcare services to its customersPresents innovative solutions utilizing informatics to deal with various healthcare technology issues

Deep Learning Applications in Translational Bioinformatics, a new volume in the Advances in Ubiquitous Sensing Application for Healthcare series, offers a detailed overview of basic bioinformatics, deep learning, and various applications of deep learning in translational bioinformatics, including deep learning ensembles, deep learning in protein classification, detection of various diseases, prediction of antiviral peptides, identification of antibiotic resistance, computer aided drug design and drug formulation. This new volume helps researchers working in the field of machine learning and bioinformatics foster future research and development.

Addresses the practical application of deep learning algorithms to a wide range of bioinformatics challengesPresents integrative and multidisciplinary approaches to ubiquitous healthcareIncludes case studies to illustrate the concepts discussed

Leveraging Metaverse and Analytics of Things (AoT) in Medical Systems explores the potential benefits and applications of emerging technologies such as metaverse and AoT in the field of healthcare. The book provides insights into how these technologies can be leveraged to improve the efficiency, effectiveness, and quality of medical systems. It explores the concept of metaverse and its potential applications in healthcare, including the use of virtual and augmented reality technologies for medical education, training, and simulation, as well as the development of immersive environments for patient care and therapy.The book also delves into the field of AoT, such as the use of wearable devices, smart sensors, and other connected technologies. Applications to monitor patient health, track medical outcomes, and inform clinical decision-making are covered. Integrating both technologies can help improve medical training, diagnosis, treatment, and patient outcomes through the use of virtual reality and real-time data analytics.

Features research methods, data analysis techniques, and best practices related to the implementation of Metaverse and Analytics of Things (AoT) in medical systemsProvides practical guidance and recommendations on how healthcare organizations can adopt and implement Metaverse and Analytics of Things (AoT) in their operationsIncludes case studies and real-world examples of how healthcare organizations have successfully leveraged technology and data analytics to improve patient care and operational efficiency

IoMT Applications in Healthcare 5.0 introduces the concept of Healthcare 5.0, emphasizing the transformative potential of intelligent diagnosis, remote treatment, and smart health management. Sections define IoMT and its multi-tiered architecture, discussing its pivotal role in improving electronic device productivity in healthcare, explore IoMT applications in Healthcare 5.0, discuss remote physical therapy using IoMT in Healthcare 5.0, delve into the security and privacy considerations of IoMT in healthcare, and address how to overcome challenges and future directions in IoMT. The book concludes by summarizing key findings, providing a comprehensive overview of the discussed topics.Written by experts in the field, this is a key text for Biomedical Engineers, Data Scientists, and IoT Developers and specialists who work in the Internet of Medical Things. Graduate students of IoT engineering, biology, biostatistics, biomedical sciences, bioinformatics, health-informatics, and Interdisciplinary sciences will also find this an invaluable resource.Provides an integrative and multidisciplinary approach to Healthcare 5.0Covers recent advancement in IoMT in transitioning healthcareDiscusses the evolution of healthcare and IoMT

Recent Advances in Computational Intelligence Applications for Biometrics and Biomedical Devices focuses on the intersection of biometric-driven computational approaches and techniques within a connected multi-modal environment, particularly emphasizing their applications in healthcare. The book explores cutting-edge methodological approaches that leverage technologies like blockchain and integrate them with information fusion, data security for medical devices, and trust management. Readers with find this to be a comprehensive overview of the topics covered, including machine learning and deep learning for biomedical-based biometrics, computational medical imaging techniques, security strategies for healthcare systems, AI technology for multimodal biometrics, and feature reduction techniques.Other sections cover blockchain and fog computing models for medical sensor data storage and evolutionary optimization for biometric feature identification and recognition, amongst others.Covers recent computational advancements in biomedical-driven biometrics for connected healthcareExplores intelligent computational multimodal technologies for healthcare, incorporating deep learning, parallel computing, and distributed computingPresents security aspects of next-generation healthcare technologies, including machine learning, deep learning, and blockchain integration with IoT for information fusion, data security, and trust managementProvides both fundamental and high-level concepts, making it suitable for both industry professionals and beginnersIncludes applications of cloud and pervasive computing technologies in biometrics for real-world scenarios

Natural Language Processing for Healthcare: The Rise of Intelligent Assistants addresses the critical gap between cutting-edge AI research and its practical applications in healthcare, offering an accessible guide tailored to the unique challenges of medical environments. It highlights how NLP technologies are revolutionizing patient care, medical documentation, and clinical decision-making while emphasizing ethical, legal, and interoperability considerations. Structured into four sections, the book begins by laying foundational knowledge in NLP and healthcare data, covering concepts such as tokenization, medical ontologies like UMLS and SNOMED CT, machine learning models, including BioBERT and ClinicalBERT, and emerging impacts of large language models like GPT.The applications section explores real-world implementations of intelligent assistants, such as virtual health chatbots, clinical documentation tools, conversational AI for patient engagement, and voice recognition integrated into electronic health records. Technical chapters provide insights into system architectures, evaluation metrics, data privacy, security, and interoperability standards like FHIR. The final section looks ahead to future directions including multilingual NLP, federated learning for privacy preservation, and the evolving landscape of AI-driven healthcare assistants. This book is an indispensable resource for a broad audience.Bridges AI research and healthcare practice with accessible, healthcare-focused NLP insights for clinical and operational useProvides practical guidance on designing and deploying intelligent virtual assistants to enhance patient care and engagementAddresses ethical, legal, and interoperability challenges unique to healthcare NLP applicationsExplores cutting-edge technologies, including large language models and federated learning in real-world medical contextsEquips data scientists and clinicians with tools to analyze unstructured medical data and improve clinical decision-making