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Recently, the attention paid to self-care tests and the easy and large screening of a high number of people has dramatically increased. Indeed, easy and affordable tools for the safe management of biological fluids together with self-diagnosis have emerged as compulsory requirements in this time of the COVID-19 pandemic, to lighten the pressure on public healthcare institutions and thus limiting the diffusion of infections. Obviously, other kinds of pathologies (cancer or other degenerative diseases) also continue to require attention, with progressively earlier and more widespread diagnoses. The contribution to the development of this research field comes from the areas of innovative plastic and 3D microfluidics, smart chemistry and the integration of miniaturized sensors, going in the direction of improving the performances of in vitro diagnostic (IVD) devices. In our Special Issue, we include papers describing easy strategies to identify diseases at the point-of-care and near-the-bed levels, but also dealing with innovative biomarkers, sample treatments, and chemistry processes which, in perspective, represent promising tools to be applied in the field.

Research & information: general --- Biology, life sciences --- light-emitting e-textiles --- alternating current electroluminescent devices --- light emitting diodes --- light electrochemical cells --- polymeric optical fibers --- fetal stem cells --- amniotic epithelial cells --- isolation protocol --- quality control --- label-free sorting --- diagnostic tool --- glucose --- glucose oxidase --- amperometric biosensor --- body fluids --- sweat --- wearable sensor --- oral cancer --- circulating tumor cells --- micromixers --- 3D microfluidics --- biodetection --- plastic microfluidics --- microfabrication --- cornea regeneration --- tissue engineering --- nanotechnology --- molecular mechanisms

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This book focuses on both fundamental and applied research on nanogenerators. The triboelectric nanogenerator (TENG) is based on expanded Maxwell’s equations for a mechano-driven system, including the polarization density term Ps in a displacement vector owing to the electrostatic charges on medium surfaces as produced by effects such as triboelectrification. The TENGs have potential applications in blue energy, wearable devices, environmental protectioin, medical science, and security. Hybridized and coupled nanogenerators further expand the application of nanogenerators in energy stability and multi-functional sensing.

Technology: general issues --- triboelectric nanogenerator --- network --- blue energy --- wave energy --- energy harvesting --- surface engineering --- surface morphology --- surface modification --- enhanced performance --- human–machine interface (HMI) --- triboelectric nanogenerator (TENG) --- artificial intelligence (AI) --- robot perception --- wearable sensor --- Internet of things (IoT) --- Beaufort scale monitoring --- near-zero power --- wake-up system --- triboelectric sensor --- ferroelectric materials --- nanogenerators --- piezoelectricity --- triboelectricity --- pyroelectricity --- bulk ferroelectric photovoltaic effect (BPVE) --- harvesting --- coupled effects --- mechanical conversion --- mechanical transmission --- triboelectric nanogenerators (TENGs) --- external mechanical system control --- regulated output --- uniform output --- stretchable electronic skin --- self-powered sensing --- human motion monitoring --- thermoplastic polyurethane fibers --- biosensors --- hybridization --- piezoelectric nanogenerator --- electromechanical conversion --- self-powered --- cell modulation --- smart textiles --- triboelectric nanogenerators --- electricity generation --- output enhancement --- air breakdown --- lubricant liquid --- mechanical lifespan

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Smart, wearables devices on a miniature scale are becoming increasingly widely available, typically in the form of smart watches and other connected devices. Consequently, devices to assist in measurements such as electroencephalography (EEG), electrocardiogram (ECG), electromyography (EMG), blood pressure (BP), photoplethysmography (PPG), heart rhythm, respiration rate, apnoea, and motion detection are becoming more available, and play a significant role in healthcare monitoring. The industry is placing great emphasis on making these devices and technologies available on smart devices such as phones and watches. Such measurements are clinically and scientifically useful for real-time monitoring, long-term care, and diagnosis and therapeutic techniques. However, a pertaining issue is that recorded data are usually noisy, contain many artefacts, and are affected by external factors such as movements and physical conditions. In order to obtain accurate and meaningful indicators, the signal has to be processed and conditioned such that the measurements are accurate and free from noise and disturbances. In this context, many researchers have utilized recent technological advances in wearable sensors and signal processing to develop smart and accurate wearable devices for clinical applications. The processing and analysis of physiological signals is a key issue for these smart wearable devices. Consequently, ongoing work in this field of study includes research on filtration, quality checking, signal transformation and decomposition, feature extraction and, most recently, machine learning-based methods.

Technology: general issues --- History of engineering & technology --- automated dietary monitoring --- eating detection --- eating timing error analysis --- biomedical signal processing --- smart eyeglasses --- wearable health monitoring --- artificial neural network --- joint moment prediction --- extreme learning machine --- Hill muscle model --- online input variables --- Review --- ECG --- Signal Processing --- Machine Learning --- Cardiovascular Disease --- Anomaly Detection --- photoplethysmography --- motion artifact --- independent component analysis --- multi-wavelength --- continuous arterial blood pressure --- systolic blood pressure --- diastolic blood pressure --- deep convolutional autoencoder --- genetic algorithm --- electrocardiography --- vectorcardiography --- myocardial infarction --- long short-term memory --- spline --- multilayer perceptron --- pain detection --- stress detection --- wearable sensor --- physiological signals --- behavioral signals --- non-invasive system --- hemodynamics --- arterial blood pressure --- central venous pressure --- pulmonary arterial pressure --- intracranial pressure --- heart rate measurement --- remote HR --- remote PPG --- remote BCG --- blind source separation --- drowsiness detection --- EEG --- frequency-domain features --- multicriteria optimization --- machine learning --- n/a

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This Special Issue shows a range of potential opportunities for the application of wearable movement sensors in motor rehabilitation. However, the papers surely do not cover the whole field of physical behavior monitoring in motor rehabilitation. Most studies in this Special Issue focused on the technical validation of wearable sensors and the development of algorithms. Clinical validation studies, studies applying wearable sensors for the monitoring of physical behavior in daily life conditions, and papers about the implementation of wearable sensors in motor rehabilitation are under-represented in this Special Issue. Studies investigating the usability and feasibility of wearable movement sensors in clinical populations were lacking. We encourage researchers to investigate the usability, acceptance, feasibility, reliability, and clinical validity of wearable sensors in clinical populations to facilitate the application of wearable movement sensors in motor rehabilitation.

accelerometers --- wearable sensors --- exercise --- measurement --- GMFCS level --- relative orientation estimation --- IMU --- magnetometer-free --- gait analysis --- machine learning --- inertial measurement units --- neurological disorders --- falls --- validity --- 3-D motion analysis --- single leg squat --- motion capture --- clinical --- rehabilitation --- motor function --- outcomes --- implementation --- locomotion --- assistive devices --- embedded sensors --- accelerometry --- physical activity --- Fourier transform --- functional linear model --- walking distance --- lower limb amputation --- gait --- Lie group --- constrained extended Kalman filter --- pose estimation --- wearable devices --- distance measurement --- gait planning --- stride length --- center of pressure --- human–machine interaction --- perinatal stroke --- kinematics --- upper extremity --- cerebral palsy --- hemiplegia --- constraint --- inertial measurement unit --- wireless sensors network --- motion tracking --- range of motion --- shoulder --- goniometer --- spinal cord injury --- tetraplegia --- clinical setting --- circadian motor behavior --- body-worn sensors --- older adults --- physically active workers --- low back pain --- inertial motion units --- wearable sensor --- real-time gait detection --- insole pressure sensors --- pathological gait --- gait rehabilitation --- assistive device --- wearable technology --- stroke --- physical therapy --- arm use --- upper limb performance --- accelerometer --- sensor --- walking --- n/a --- human-machine interaction

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This Special Issue shows a range of potential opportunities for the application of wearable movement sensors in motor rehabilitation. However, the papers surely do not cover the whole field of physical behavior monitoring in motor rehabilitation. Most studies in this Special Issue focused on the technical validation of wearable sensors and the development of algorithms. Clinical validation studies, studies applying wearable sensors for the monitoring of physical behavior in daily life conditions, and papers about the implementation of wearable sensors in motor rehabilitation are under-represented in this Special Issue. Studies investigating the usability and feasibility of wearable movement sensors in clinical populations were lacking. We encourage researchers to investigate the usability, acceptance, feasibility, reliability, and clinical validity of wearable sensors in clinical populations to facilitate the application of wearable movement sensors in motor rehabilitation.

Technology: general issues --- accelerometers --- wearable sensors --- exercise --- measurement --- GMFCS level --- relative orientation estimation --- IMU --- magnetometer-free --- gait analysis --- machine learning --- inertial measurement units --- neurological disorders --- falls --- validity --- 3-D motion analysis --- single leg squat --- motion capture --- clinical --- rehabilitation --- motor function --- outcomes --- implementation --- locomotion --- assistive devices --- embedded sensors --- accelerometry --- physical activity --- Fourier transform --- functional linear model --- walking distance --- lower limb amputation --- gait --- Lie group --- constrained extended Kalman filter --- pose estimation --- wearable devices --- distance measurement --- gait planning --- stride length --- center of pressure --- human-machine interaction --- perinatal stroke --- kinematics --- upper extremity --- cerebral palsy --- hemiplegia --- constraint --- inertial measurement unit --- wireless sensors network --- motion tracking --- range of motion --- shoulder --- goniometer --- spinal cord injury --- tetraplegia --- clinical setting --- circadian motor behavior --- body-worn sensors --- older adults --- physically active workers --- low back pain --- inertial motion units --- wearable sensor --- real-time gait detection --- insole pressure sensors --- pathological gait --- gait rehabilitation --- assistive device --- wearable technology --- stroke --- physical therapy --- arm use --- upper limb performance --- accelerometer --- sensor --- walking