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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

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

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 --- 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