Active and Assisted Living (AAL) systems aim at improving the quality of life and supporting independent and healthy living of older or impaired people by using a distributed network of sensors and actuators to create a ubiquitous technological layer, able to interact transparently with the users, observing and interpreting their actions and intentions, learning their preferences and adjusting the parameters of the system to improve their quality of life and work. This book provides a comprehensive review of the technologies and applications for AAL. Topics covered include the current state of the art of smart environments and labs from an AAL point of view; ambient and wearable sensors for human health monitoring; computer vision for active and assisted living; data fusion for identifying lifestyle patterns; interoperable enhanced living environments; reasoning systems for AAL; person-environment interaction; data analytics for enabling connected health; human gait analysis for frailty detection; fall prevention and detection; supporting activities of daily living; outdoor mobility assistance; location and orientation technologies based on WiFi systems; health, wellbeing and engagement in life through AAL; tablet-based clinical decision support system for hospitalised older adults; smart, age-friendly cities and communities; privacy and ethical issues; and human-centred design. The book concludes with a case study on the design and implementation of a smart home technological platform for the delivery of AAL services. With a wide range of chapters from international contributors, this book is essential reading for researchers and students in academics and industry developing AAL technologies, healthcare practitioners, and engineers with an interest in the field.

Affective computing is the study and development of systems and devices that can recognise human emotions. This can be done using sensing technologies and AI algorithms to process biological signals or facial images to identify the different affective states, such as happiness, anger, fear, surprise, sadness and disgust. This non-invasive technique has applications in healthcare such as emotional impairment detection, mental health assessment, emotional stress assessment, cognitive decline detection, attention deficit disorders, neurodegenerative diseases, neurological disorders, autism spectrum disorder, stress, anxiety or other behavioural assessment.

This edited book provides an overview of the ongoing research on affective computing applications in healthcare using AI and IoT. This book covers recent advancements in computing technology, modelling methods, frameworks, and algorithms used for human affect detection using bio-signal and image processing methods.

The book explores the use of EEG signals, thermal imaging, eye-movement patterns, gesture recognition systems and IoT systems to gather information and discusses the use of deep learning, CNN and RNN-LSTM models of how this information can be usefully processed to detect emotional states.

Discussing the latest trends and developments in research in the field, this book is a useful resource for researchers in affective computing, affective neuroscience, cognitive neuroscience, computer vision, signal and image processing, cybersecurity, AI/ML/DL, data science, HCI, sensing and robotics.

Practicing physicians, clinical experts or researchers in experimental and applied cognitive psychology who wish to understand the emotional wellbeing (pain and stress level analysis) of patients may also find this book of interest.

Machine learning algorithms are increasingly finding applications in the healthcare sector. Whether assisting a clinician to process an individual patient's data or helping administrators view hospital bed turnover, the volume and complexity of healthcare data is a compelling reason for the development of machine learning based tools to aid in its interpretation and use.

This edited book focuses on the applications of machine learning in the healthcare sector, both at the macro-level for guiding policy decisions, and at the granular level, showing how ML techniques can be applied to process an individual patient's medical data to swiftly aid diagnosis.

Written by an international team of experts, the book presents several applications of machine learning in the healthcare sector, including health system planning, optimisation and preparedness, outlining the benefits and challenges of coordination and data sharing. Machine learning has many applications in processing patient data and topics such as arrhythmia detection, image-guided microsurgery and early detection of Alzheimer's disease are discussed in depth. The book also looks at machine learning applications exploiting wearable sensors for real-time analysis and concepts around enhancing physical performance.

Suitable for an audience of computer scientists, healthcare engineers and those involved with digital medicine, this book brings together a plethora of machine learning applications from across the board of the healthcare services.

Nanomaterials are able to penetrate nanoscale pores of tissues, possess prolonged circulation, enter cells, and have increased surface area per volume allowing for greater drug loading. For these reasons, nanomaterials are finding numerous uses in medicine including fighting cancer, promoting tissue regeneration, reversing aging, inhibiting infection, limiting inflammation or scar tissue growth, and many others. This book describes the engineering applications and challenges of using nanostructured surfaces and nanomaterials in healthcare. Topics covered include biomimetic coating of calcium phosphates on Ti metals; surface modifications of orthopaedic implant materials using an electroplating process; design, fabrication and application of carbon-based nano biomaterials; usage of stem cells in bone and cartilage tissue engineering; nanobiomaterials and 3D bioprinting for osteochondral regeneration; self- assembled peptide hydrogels for biomedical applications; antimicrobial properties of nanomaterials; nanoparticle enhanced radiation therapy for bacterial infection; nanomaterials used in implant technology and their toxicity; challenges of risk assessment of nanomaterials in consumer products and current regulatory status; and the clinical rationale for silicon nitride bioceramics in orthopedics. With contributions from an international selection of researchers this book is essential reading for researchers in industry and academia working at the interfaces of healthcare, engineering and nanotechnology.

Blockchain and machine learning technologies can mitigate healthcare issues such as slow access to medical data, poor system interoperability, lack of patient agency, and data quality and quantity for medical research. Blockchain technology facilitates and secures the storage of information in such a way that doctors can see a patient's entire medical history, but researchers see only statistical data instead of any personal information. Machine learning can make use of this data to notice patterns and give accurate predictions, providing more support for the patients and also in research related fields where there is a need for accurate data to predict credible results. This book examines the application of blockchain technology and machine learning algorithms in various healthcare settings, covering the basics of the technologies and exploring how they can be used to improve clinical outcomes and improving the patient's experience. These topics are illustrated with reference to issues around the supply chain, drug verification, reimbursement, control access and clinical trials. Case studies are given for applications in the analysis of breast cancer, hepatitis C, and COVID-19.

This edited book brings together research from laboratories across the world, in order to offer a global perspective on advances in prosthetic hand control. State-of-the-art control of prosthetics in the laboratory and clinical spaces are presented and the challenges discussed, and the effect of user training on control of prosthetics to evaluate the translational efficacy and value for the end-user is highlighted. The book begins with a chapter introducing the fundamental principles, engineering challenges and control solutions for prosthetic hands. Further chapters address methods to design bespoke sockets, magnetomyography, implantable technologies for closed-loop control of prostheses, direct neural control of prostheses via nerve implants as well as user-prosthesis co-adaptation, and two chapters on prosthetics for children. The book concludes with a chapter by Dr Nazarpour on the future of myoelectric prosthetics control, with particular focus on the successful translation of research advances into real clinical gains. The book is essential reading for anyone involved in research or undertaking advanced courses in prosthetic design and control. It provides an in-depth exploration of this rewarding topic, by exploring technologies with the potential to improve the quality of life of upper-limb prosthetic users.

Medical images, in various formats, are used by clinicians to identify abnormalities or markers associated with certain conditions, such as cancers, diseases, abnormalities or other adverse health conditions. Deep learning algorithms use vast volumes of data to train the computer to recognise certain features in the images that are associated with the disease or condition that you wish to identify.

Whilst analysing the images by eye can take a lot of time, deep learning algorithms have the benefit of reviewing medical images at a faster rate than a human can, which aids the clinician, speeding up diagnoses and freeing up clinicians' time for other duties.

Deep Learning in Medical Image Processing and Analysis introduces the fundamentals of deep learning for biomedical image analysis for applications including ophthalmology, cancer detection and heart disease. The book considers the principles of multi-instance feature selection, swarm optimisation, parallel processing models, artificial neural networks, support vector machines, as well as their design and optimisation, in biomedical applications. Topics such as data security, patient confidentiality, effectiveness and reliability will also be discussed.

Written by an international team of experts, this edited book covers principles and applications for industry and academic researchers, scientists, engineers, developers, and designers in the fields of machine learning, deep learning, AI, image processing, signal processing, computer science or related fields. It will also be of interest to standards bodies and regulators, and clinicians using deep learning models.

In healthcare, a digital twin is a digital representation of a patient or healthcare system using integrated simulations and service data. The digital twin tracks a patient's records, crosschecks them against registered patterns and analyses any diseases or contra indications. The digital twin uses adaptive analytics and algorithms to produce accurate prognoses and suggest appropriate interventions. A digital twin can run various medical scenarios before treatment is initiated on the patient, thus increasing patient safety as well as providing the most appropriate treatments to meet the patient's requirements.

Digital Twin Technologies for Healthcare 4.0 discusses how the concept of the digital twin can be merged with other technologies, such as artificial intelligence (AI), machine learning (ML), big data analytics, IoT and cloud data management, for the improvement of healthcare systems and processes. The book also focuses on the various research perspectives and challenges in implementation of digital twin technology in terms of data analysis, cloud management and data privacy issues.

With chapters on visualisation techniques, prognostics and health management, this book is a must-have for researchers, engineers and IT professionals in healthcare as well as those involved in using digital twin technology, AI, IoT and big data analytics for novel applications.

Electromagnetic waves have long been used in medical settings for diagnostic purposes, such as for the detection of cancerous tissues, stroke events or cardiovascular risk, as the behaviour of the waves upon meeting their target gives pertinent information for diagnostic and imaging purposes. This edited book presents advances in the use of electromagnetic waves and antennas in healthcare settings, both as diagnostic tools (such as radar-based imaging, holographic microwave imaging, thermoacoustic imaging systems), and therapeutic interventions (such as microwave ablation therapies for cancer). Written by an international team of biomedical engineering researchers, it discusses all aspects related to the design and modelling of electromagnetic imaging techniques, electromagnetic devices, wireless implants, wearable systems and wireless sensor networks and in vitro and in vivo testing. Design issues for wearable antennas, wearable sensors, magnetic coils and coil array issues are explored and biomedical applications such as cancer detection, stoke event detection, GI diagnostics, and cardiovascular risk prediction are discussed. The book also explores scattering problems of electromagnetic waves between different tissues, and how these complex scattering problems can be resolved. This book will be of interest to researchers and engineers in the electromagnetic wave community, those in antenna research, biomedical engineering and related fields.

Enhanced living environments employ information and communications technologies to support true ambient assisted living for adults and people with disabilities. This book provides an overview of today's architectures, techniques, protocols, components, and cloud-based solutions related to ambient assisted living and enhanced living environments. Topics covered include: an introduction to enhanced living environments; pervasive sensing for social connectedness; ethics in information and communication technologies; service scenarios in smart personal environments; technological support to stress level monitoring; big data systems to improve healthcare information searching over the Internet; sensors for wireless body area networks; linear wireless sensor networks and protocols in next generation networks; model-compilation challenges for cyber-physical systems; health monitoring using wireless body area networks; wearable health care; and intelligent systems for after-stroke home rehabilitation.