Health Care Paradigms in the Internet of Things Ecosystem brings all IoT-enabled health care related technologies into a single platform so that undergraduate and postgraduate students, researchers, academicians and industry leaders can easily understand IoT-based healthcare systems. The book uses data and network engineering and intelligent decision support system-by-design principles to design a reliable IoT-enabled health care ecosystem and to implement cyber-physical pervasive infrastructure solutions. It takes the reader on a journey that begins with understanding the healthcare monitoring paradigm in IoT-enabled technologies and how it can be applied in various aspects. In addition, the book walks readers through real-time challenges and presents a guide on how to build a safe infrastructure for IoT-based health care. It also helps researchers and practitioners understand the e-health care architecture through IoT and the state-of-the-art in IoT countermeasures. Readers will find this to be a comprehensive discussion on functional frameworks for IoT-based healthcare systems, intelligent medicine, RFID technology, HMI, Cognitive Interpretation, Brain-Computer Interface, Remote Health Monitoring systems, wearable sensors, WBAN, and security and privacy issues in IoT-based health care monitoring systems.

Deep Learning (DL) is a method of machine learning, running over Artificial Neural Networks, that uses multiple layers to extract high-level features from large amounts of raw data. Deep Learning methods apply levels of learning to transform input data into more abstract and composite information. Handbook for Deep Learning in Biomedical Engineering: Techniques and Applications gives readers a complete overview of the essential concepts of Deep Learning and its applications in the field of Biomedical Engineering. Deep learning has been rapidly developed in recent years, in terms of both methodological constructs and practical applications. Deep Learning provides computational models of multiple processing layers to learn and represent data with higher levels of abstraction. It is able to implicitly capture intricate structures of large-scale data and is ideally suited to many of the hardware architectures that are currently available. The ever-expanding amount of data that can be gathered through biomedical and clinical information sensing devices necessitates the development of machine learning and AI techniques such as Deep Learning and Convolutional Neural Networks to process and evaluate the data. Some examples of biomedical and clinical sensing devices that use Deep Learning include: Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Ultrasound, Single Photon Emission Computed Tomography (SPECT), Positron Emission Tomography (PET), Magnetic Particle Imaging, EE/MEG, Optical Microscopy and Tomography, Photoacoustic Tomography, Electron Tomography, and Atomic Force Microscopy. Handbook for Deep Learning in Biomedical Engineering: Techniques and Applications provides the most complete coverage of Deep Learning applications in biomedical engineering available, including detailed real-world applications in areas such as computational neuroscience, neuroimaging, data fusion, medical image processing, neurological disorder diagnosis for diseases such as Alzheimer's, ADHD, and ASD, tumor prediction, as well as translational multimodal imaging analysis.

Nanoemulsions are produced by mixing an oil phase with an aqueous phase under shear pressure. This procedure yields uniform populations of oil droplets ranging in diameter from 200 to 8 nm that are kinetically stable colloidal substances with enhanced properties compared to the conventional emulsion substances. Nanoemulsions have broad potential applications in agriculture, food, health, and biomedical sciences. Nanoemulsion Applications in Agriculture, Food, Health, and Biomedical Sciences focuses on the aspects of nanoemulsion-like synthesis, characterization, and more and examines recent trends in their applications within a variety of relevant fields. Nanoemulsions have broad application in many different fields; without emulsification, process product development would not be possible. Covering topics such as cancer treatment, healthcare applications, and food manufacturing, this book is essential for scientists, doctors, researchers, post-graduate students, medical students, government officials, hospital directors, professors, and academicians.

Nature-Inspired Optimization Algorithms, Second Edition provides an introduction to all major nature-inspired algorithms for optimization. The book's unified approach, balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with case studies to illustrate how these algorithms work. Topics include particle swarm optimization, ant and bee algorithms, simulated annealing, cuckoo search, firefly algorithm, bat algorithm, flower algorithm, harmony search, algorithm analysis, constraint handling, hybrid methods, parameter tuning and control, and multi-objective optimization. This book can serve as an introductory book for graduates, for lecturers in computer science, engineering and natural sciences, and as a source of inspiration for new applications.

Data Analytics in Biomedical Engineering and Healthcare explores key applications using data analytics, machine learning, and deep learning in health sciences and biomedical data. The book is useful for those working with big data analytics in biomedical research, medical industries, and medical research scientists. The book covers health analytics, data science, and machine and deep learning applications for biomedical data, covering areas such as predictive health analysis, electronic health records, medical image analysis, computational drug discovery, and genome structure prediction using predictive modeling. Case studies demonstrate big data applications in healthcare using the MapReduce and Hadoop frameworks.

Terahertz Biomedical and Healthcare Technologies: Materials to Devices reviews emerging advances in terahertz biomedical and healthcare technologies, including advances in fundamental materials science research, device design and fabrication, applications, and challenges and opportunities for improved performance. In addition, the improvement of materials, optical elements, and measuring techniques are also explored. Other sections cover the design and development of wide bandgap semiconductors for terahertz device applications, including their physics, device modeling, characterization and fabrication concepts. Finally, the book touches on potential defense, medical imaging, internet of things, and the machine learning applications of terahertz technologies.

3D Printing in Medicine and Surgery: Applications in Healthcare is an advanced book on surgical and enhanced medical applications that can be achieved with 3D printing. It is an essential handbook for medical practitioners, giving access to a range of practical methods, while also focusing on applied knowledge. This comprehensive resource features practical experiments and processes for preparing 3D printable materials. Early chapters cover foundational knowledge and background reading, while later chapters discuss and review the current technologies used to engineer specific tissue types, experiments and methods, medical approaches and the challenges that lie ahead for future research. The book is an indispensable reference guide to the various methods used by current medical practitioners working at the forefront of 3D printing applications in medicine.

A handy pocket guide to help occupational therapy students prepare for their practice placements. Occupational Therapy placements can be daunting - you'll be working in a range of settings and supporting individuals with a variety of conditions. There are new colleagues to work with, and newly learned theory to put into practice. This pocket guide is designed to make your placements much more enjoyable and less stressful. From basic equipment to role emerging placements, via positive risk taking, it's full of practical detail, hints and tips. Written by a senior lecturer with key input from students - this guidance is really produced with you in mind. Pocket-sized format - carry it with you at all times. Space to make your own notes - be it uniform policy, new terminology, or just the names of your new colleagues! Reduce your stress and make the most of your placements by having this book to hand from the start.

As old age is increasing globally, some challenges arise such as multimorbidity, a unique medical condition that has multiple potential complications and thus needs high-quality care directed by qualified healthcare providers. Multimorbidity is an important daily challenge to internists worldwide due to its many difficulties. Junior physicians dealing with multimorbidity must have the knowledge to practice high-quality care for their elderly patients. Cases on Multimorbidity and Its Impact on Elderly Patients considers approaches to manage multimorbidity and its unique complications and challenges to aid in appropriate daily decision making. Covering key topics such as weight loss, aging, and frailty, this reference work is ideal for medical professionals, nurses, policymakers, researchers, scholars, academicians, practitioners, instructors, and students.

Storing Digital Binary Data into Cellular DNA demonstrates how current digital information storage systems have short longevity and limited capacity, also pointing out that their production and consumption of data exceeds supply. Author Rocky Termanini explains the DNA system and how it encodes vast amounts of data, then presents information on the emergence of DNA as a storage technology for the ever-growing stream of data being produced and consumed. The book will be of interest to a range of readers looking to understand this game-changing technology, including researchers in computer science, biomedical engineers, geneticists, physicians, clinicians, law enforcement and cybersecurity experts.

Advances in telemedicine technologies have offered clinicians greater levels of real-time guidance and technical assistance for diagnoses, monitoring, operations or interventions from colleagues based in remote locations. The topic includes the use of videoconferencing, mentorship during surgical procedures, or machine-to-machine communication to process data from one location by programmes running in another. This edited book presents a variety of technologies with applications in telemedicine, originating from the fields of biomedical sensors, wireless sensor networking, computer-aided diagnosis methods, signal and image processing and analysis, automation and control, virtual and augmented reality, multivariate analysis, and data acquisition devices. The Internet of Medical Things (IoMT), surgical robots, telemonitoring, and teleoperation systems are also explored, as well as the associated security and privacy concerns in this field. Topics covered include critical factors in the development, implementation and evaluation of telemedicine; surgical tele-mentoring; technologies in medical information processing; recent advances of signal/image processing techniques in healthcare; a real-time ECG processing platform for telemedicine applications; data mining in telemedicine; social work and tele-mental health services for rural and remote communities; applying telemedicine to social work practice and education; advanced telemedicine systems for remote healthcare monitoring; the impact of tone-mapping operators and viewing devices on visual quality of experience of colour and grey-scale HDR images; modelling the relationships between changes in EEG features and subjective quality of HDR images; IoMT and healthcare delivery in chronic diseases; and transform domain robust watermarking method using Riesz wavelet transform for medical data security and privacy.