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Biosensors and systems in the form of wearables and “nearables” (i.e., everyday sensorized objects with transmitting capabilities such as smartphones) are rapidly evolving for use in healthcare. Unlike conventional approaches, these technologies can enable seamless or on-demand physiological monitoring, anytime and anywhere. Such monitoring can help transform healthcare from the current reactive, one-size-fits-all, hospital-centered approach into a future proactive, personalized, decentralized structure. Wearable and nearable biosensors and systems have been made possible through integrated innovations in sensor design, electronics, data transmission, power management, and signal processing. Although much progress has been made in this field, many open challenges for the scientific community remain, especially for those applications requiring high accuracy. This book contains the 12 papers that constituted a recent Special Issue of Sensors sharing the same title. The aim of the initiative was to provide a collection of state-of-the-art investigations on wearables and nearables, in order to stimulate technological advances and the use of the technology to benefit healthcare. The topics covered by the book offer both depth and breadth pertaining to wearable and nearable technology. They include new biosensors and data transmission techniques, studies on accelerometers, signal processing, and cardiovascular monitoring, clinical applications, and validation of commercial devices.

Humanities --- Social interaction --- tangent space --- Riemannian geometry --- particle swarm optimization (PSO) --- BCI --- EEG --- electro-oscillography (EOG) --- CSP --- FBCSP (filter bank common spatial pattern) --- online learning --- ballistocardiography --- pressure sensor --- Emfit --- home monitoring --- sleep recording --- sleep apnea --- unsupervised learning --- synchronization --- acoustic emissions --- joint sounds --- glove --- wearable sensing --- knee joint loading --- quaternion --- smartphone --- feature engineering --- human activity recognition --- sensor fusion --- ballistocardiogram --- blood pressure --- stroke volume --- cardiac output --- total peripheral resistance --- photoplethysmography --- photoplethysmogram --- heart rate --- consumer-wearable devices --- in-ear --- validation --- optical pulse rate monitoring --- pulse rate --- seismocardiography --- ultra-short heart rate variability --- stress evaluation --- accelerometers --- robotic assistant systems for surgery --- expertise --- pick-and-drop simulator task --- grip force profiles --- grip force control --- body sensor network --- wearable sensor --- telemedicine --- telerehabilitation --- seismocardiogram --- acceleration --- electrocardiogram --- cardiac mechanics --- pulse transit time --- adaptive recursive least squares filter (ARLSF) --- Seismocardiography (SCG) --- motion artifact --- Electrocardiogram (ECG) --- ageing --- gender --- machine learning --- support vector machine --- voice analysis --- pressure sensors --- compression therapy --- thin-film sensors --- wireless sensors --- medical pressure monitoring --- capacitive sensors --- flexible sensors --- LC sensor --- wound monitoring --- n/a

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This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine.

Technology: general issues --- History of engineering & technology --- electrocardiogram --- deep metric learning --- k-nearest neighbors classifier --- premature ventricular contraction --- dimensionality reduction --- classifications --- Laplacian eigenmaps --- locality preserving projections --- compressed sensing --- convolutional neural network --- EEG --- epileptic seizure detection --- RISC-V --- ultra-low-power --- sepsis --- atrial fibrillation --- prediction --- heart rate variability --- feature extraction --- random forest --- annotations --- myoelectric prosthesis --- sEMG --- grasp phases analysis --- grasp classification --- machine learning --- electronic nose --- liver dysfunction --- cirrhosis --- semiconductor metal oxide gas sensor --- vagus nerve --- intraneural --- decoding --- intrafascicular --- recording --- carbon nanotube --- artificial intelligence --- lens-free shadow imaging technique --- cell-line analysis --- cell signal enhancement --- deep learning --- ECG signal --- reconstruction dictionaries --- projection matrices --- signal classifications --- osteopenia --- sarcopenia --- XAI --- SHAP --- IMU --- gait analysis --- sensors --- convolutional neural networks --- Parkinson's disease --- biomedical monitoring --- accelerometer --- pressure sensor --- disease management --- electromyography --- correlation --- high blood pressure --- hypertension --- photoplethysmography --- electrocardiography --- calibration --- classification models --- COVID-19 --- ECG trace image --- transfer learning --- Convolutional Neural Networks (CNN) --- feature selection --- sympathetic activity (SNA) --- skin sympathetic nerve activity (SKNA) --- electrodes --- electrocardiogram (ECG) --- cardiac time interval --- dynamic time warping --- fiducial point detection --- heart failure --- seismocardiography --- wearable electroencephalography --- motor imagery --- motor execution --- beta rebound --- brain-machine interface --- EEG classification --- electrocardiogram --- deep metric learning --- k-nearest neighbors classifier --- premature ventricular contraction --- dimensionality reduction --- classifications --- Laplacian eigenmaps --- locality preserving projections --- compressed sensing --- convolutional neural network --- EEG --- epileptic seizure detection --- RISC-V --- ultra-low-power --- sepsis --- atrial fibrillation --- prediction --- heart rate variability --- feature extraction --- random forest --- annotations --- myoelectric prosthesis --- sEMG --- grasp phases analysis --- grasp classification --- machine learning --- electronic nose --- liver dysfunction --- cirrhosis --- semiconductor metal oxide gas sensor --- vagus nerve --- intraneural --- decoding --- intrafascicular --- recording --- carbon nanotube --- artificial intelligence --- lens-free shadow imaging technique --- cell-line analysis --- cell signal enhancement --- deep learning --- ECG signal --- reconstruction dictionaries --- projection matrices --- signal classifications --- osteopenia --- sarcopenia --- XAI --- SHAP --- IMU --- gait analysis --- sensors --- convolutional neural networks --- Parkinson's disease --- biomedical monitoring --- accelerometer --- pressure sensor --- disease management --- electromyography --- correlation --- high blood pressure --- hypertension --- photoplethysmography --- electrocardiography --- calibration --- classification models --- COVID-19 --- ECG trace image --- transfer learning --- Convolutional Neural Networks (CNN) --- feature selection --- sympathetic activity (SNA) --- skin sympathetic nerve activity (SKNA) --- electrodes --- electrocardiogram (ECG) --- cardiac time interval --- dynamic time warping --- fiducial point detection --- heart failure --- seismocardiography --- wearable electroencephalography --- motor imagery --- motor execution --- beta rebound --- brain-machine interface --- EEG classification

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• Reference Manager
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This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine.

electrocardiogram --- deep metric learning --- k-nearest neighbors classifier --- premature ventricular contraction --- dimensionality reduction --- classifications --- Laplacian eigenmaps --- locality preserving projections --- compressed sensing --- convolutional neural network --- EEG --- epileptic seizure detection --- RISC-V --- ultra-low-power --- sepsis --- atrial fibrillation --- prediction --- heart rate variability --- feature extraction --- random forest --- annotations --- myoelectric prosthesis --- sEMG --- grasp phases analysis --- grasp classification --- machine learning --- electronic nose --- liver dysfunction --- cirrhosis --- semiconductor metal oxide gas sensor --- vagus nerve --- intraneural --- decoding --- intrafascicular --- recording --- carbon nanotube --- artificial intelligence --- lens-free shadow imaging technique --- cell-line analysis --- cell signal enhancement --- deep learning --- ECG signal --- reconstruction dictionaries --- projection matrices --- signal classifications --- osteopenia --- sarcopenia --- XAI --- SHAP --- IMU --- gait analysis --- sensors --- convolutional neural networks --- Parkinson’s disease --- biomedical monitoring --- accelerometer --- pressure sensor --- disease management --- electromyography --- correlation --- high blood pressure --- hypertension --- photoplethysmography --- electrocardiography --- calibration --- classification models --- COVID-19 --- ECG trace image --- transfer learning --- Convolutional Neural Networks (CNN) --- feature selection --- sympathetic activity (SNA) --- skin sympathetic nerve activity (SKNA) --- electrodes --- electrocardiogram (ECG) --- cardiac time interval --- dynamic time warping --- fiducial point detection --- heart failure --- seismocardiography --- wearable electroencephalography --- motor imagery --- motor execution --- beta rebound --- brain–machine interface --- EEG classification --- n/a --- Parkinson's disease --- brain-machine interface