Data Analytics and Applications of the Wearable Sensors in Healthcare

• Eará dahkkit: Syed Abdul, Shabbir (Doaimmaheaddji), Luque, Luis Fernandez (Doaimmaheaddji), Garcia-Gomez, Juan Miguel (Doaimmaheaddji), Garcia-Zapirain, Begoña (Doaimmaheaddji), Hsueh, Pei-Yun (Doaimmaheaddji)
• Materiálatiipa: Elektrovnnalaš Girjji oassi
• Giella: eaŋgalasgiella
• Almmustuhtton: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2020
• Fáttát: Humanities; Social interaction; eHealth; wearable; monitoring; services; integration; IoT; Telemedicine; wearable sensors; multivariate analysis; longitudinal study; functional decline; exercise intervention; accidental falls; fall detection; real-world; signal analysis; performance measures; non-wearable sensors; accelerometers; cameras; machine learning; smart textiles; healthcare; talking detection; activity recognition and monitoring; patient health and state monitoring; wearable sensing; orientation-invariant sensing; motion sensors; accelerometer; gyroscope; magnetometer; pattern classification; artificial intelligence; supervised machine learning; predictive analytics; hemodialysis; non-contact sensor; heart rate; respiration rate; heart rate variability; time-domain features; frequency-domain features; principal component analysis; behaviour analysis; classifier efficiency; personal risk detection; one-class classification; actigraphy; encoding; data compression; denoising; edge computing; signal processing; wearables; activity monitoring; citizen science; cluster analysis; physical activity; sedentary behavior; walking; energy expenditure; wearable device; impedance pneumography; neural network; mechanocardiogram (MCG); smart clothes; heart failure (HF); left ventricular ejection fraction (LVEF); technology acceptance model (TAM); physical activity classification; free-living; GENEactiv accelerometer; Gaussian mixture model; hidden Markov model; wavelets; skill assessment; deep learning; LSTM; state space model; probabilistic inference; latent features; human activity recognition; MIMU; genetic algorithm; feature selection; classifier optimization; bispectrum; entropy; feature extraction; heat stroke; filtering algorithm; physiological parameters; exercise experiment; biomedical signal processing; wearable biomedical sensors; wireless sensor network; respiratory monitoring; optoelectronic plethysmography; biofeedback; biomedical technology; exercise therapy; orthopedics; mobile health; qualitative; human factors; inertial measurement unit; disease prevention; occupational healthcare; P-Ergonomics; precision ergonomics; musculoskeletal disorders; wellbeing at work; electrocardiogram; conductive gels; noncontact electrode; myocardial ischemia; pacemaker; ventricular premature contraction; upper extremity; motion; action research arm test; activities of daily living; IoT wearable monitor; health; posture analysis; spinal posture; wearable sensor; embedded system; recurrent neural networks; physical workload; wearable systems for healthcare; machine learning for real-time applications; actigraph; body worn sensors; clothing sensors; cross correlation analysis; healthcare movement sensing; wearable devices; calibration; inertial measurement units; human movement; physical activity type; real-life; GPS; GIS; n/a
• Liŋkkat: DOAB: download the publication; DOAB: description of the publication

This book provides a collection of comprehensive research articles on data analytics and applications of wearable devices in healthcare. This Special Issue presents 28 research studies from 137 authors representing 37 institutions from 19 countries. To facilitate the understanding of the research ar...