Advanced Signal Processing in Wearable Sensors for Health Monitoring
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 p...
Αποθηκεύτηκε σε:
| Μορφή: | Online |
|---|---|
| Γλώσσα: | Αγγλικά |
| Έκδοση: |
MDPI - Multidisciplinary Digital Publishing Institute
2022
|
| Θέματα: | |
| Διαθέσιμο Online: | ONIX_20220506_9783036538877_92 |
| Ετικέτες: |
Δεν υπάρχουν, Καταχωρήστε ετικέτα πρώτοι!
|
| _version_ | 1869518098362531840 |
|---|---|
| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-81026 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-810262024-04-09T23:16:01Z Advanced Signal Processing in Wearable Sensors for Health Monitoring Abbod, Maysam Shieh, Jiann-Shing 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 thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology 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. 2022-05-06T11:22:29Z 2022-05-06T11:22:29Z 2022 book ONIX_20220506_9783036538877_92 9783036538877 9783036538884 https://directory.doabooks.org/handle/20.500.12854/81026 eng image/jpeg Attribution 4.0 International https://mdpi.com/books/pdfview/book/5368 https://mdpi.com/books/pdfview/book/5368 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-3888-4 10.3390/books978-3-0365-3888-4 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036538877 9783036538884 206 Basel open access |
| spellingShingle | 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 thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology Advanced Signal Processing in Wearable Sensors for Health Monitoring |
| title | Advanced Signal Processing in Wearable Sensors for Health Monitoring |
| title_full | Advanced Signal Processing in Wearable Sensors for Health Monitoring |
| title_fullStr | Advanced Signal Processing in Wearable Sensors for Health Monitoring |
| title_full_unstemmed | Advanced Signal Processing in Wearable Sensors for Health Monitoring |
| title_short | Advanced Signal Processing in Wearable Sensors for Health Monitoring |
| title_sort | advanced signal processing in wearable sensors for health monitoring |
| topic | 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 thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| topic_facet | 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 thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| url | ONIX_20220506_9783036538877_92 |