LEADER 04819nam 2201009z- 450 001 9910566462503321 005 20231214133252.0 035 $a(CKB)5680000000037756 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/81026 035 $a(EXLCZ)995680000000037756 100 $a20202205d2022 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvanced Signal Processing in Wearable Sensors for Health Monitoring 210 $aBasel$cMDPI - Multidisciplinary Digital Publishing Institute$d2022 215 $a1 electronic resource (206 p.) 311 $a3-0365-3887-9 311 $a3-0365-3888-7 330 $aSmart, 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. 606 $aTechnology: general issues$2bicssc 606 $aHistory of engineering & technology$2bicssc 610 $aautomated dietary monitoring 610 $aeating detection 610 $aeating timing error analysis 610 $abiomedical signal processing 610 $asmart eyeglasses 610 $awearable health monitoring 610 $aartificial neural network 610 $ajoint moment prediction 610 $aextreme learning machine 610 $aHill muscle model 610 $aonline input variables 610 $aReview 610 $aECG 610 $aSignal Processing 610 $aMachine Learning 610 $aCardiovascular Disease 610 $aAnomaly Detection 610 $aphotoplethysmography 610 $amotion artifact 610 $aindependent component analysis 610 $amulti-wavelength 610 $acontinuous arterial blood pressure 610 $asystolic blood pressure 610 $adiastolic blood pressure 610 $adeep convolutional autoencoder 610 $agenetic algorithm 610 $aelectrocardiography 610 $avectorcardiography 610 $amyocardial infarction 610 $along short-term memory 610 $aspline 610 $amultilayer perceptron 610 $apain detection 610 $astress detection 610 $awearable sensor 610 $aphysiological signals 610 $abehavioral signals 610 $anon-invasive system 610 $ahemodynamics 610 $aarterial blood pressure 610 $acentral venous pressure 610 $apulmonary arterial pressure 610 $aintracranial pressure 610 $aheart rate measurement 610 $aremote HR 610 $aremote PPG 610 $aremote BCG 610 $ablind source separation 610 $adrowsiness detection 610 $aEEG 610 $afrequency-domain features 610 $amulticriteria optimization 610 $amachine learning 615 7$aTechnology: general issues 615 7$aHistory of engineering & technology 700 $aAbbod$b Maysam$4edt$01326293 702 $aShieh$b Jiann-Shing$4edt 702 $aAbbod$b Maysam$4oth 702 $aShieh$b Jiann-Shing$4oth 906 $aBOOK 912 $a9910566462503321 996 $aAdvanced Signal Processing in Wearable Sensors for Health Monitoring$93037274 997 $aUNINA