TY - BOOK ID - 133822816 TI - Sensors for Gait, Posture, and Health Monitoring Volume 1 PY - 2020 PB - Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute DB - UniCat KW - History of engineering & technology KW - step detection KW - machine learning KW - outlier detection KW - transition matrices KW - autoencoders KW - ground reaction force (GRF) KW - micro electro mechanical systems (MEMS) KW - gait KW - walk KW - bipedal locomotion KW - 3-axis force sensor KW - shoe KW - force distribution KW - multi-sensor gait classification KW - distributed compressed sensing KW - joint sparse representation classification KW - telemonitoring of gait KW - operating range KW - accelerometer KW - stride length KW - peak tibial acceleration KW - running velocity KW - wearable sensors KW - feedback technology KW - rehabilitation KW - motor control KW - cerebral palsy KW - inertial sensors KW - gait events KW - spatiotemporal parameters KW - postural control KW - falls in the elderly KW - fall risk assessment KW - low-cost instrumented insoles KW - foot plantar center of pressure KW - flexible sensor KW - gait recognition KW - piezoelectric material KW - wearable KW - adaptability KW - force sensitive resistors KW - self-tuning triple threshold algorithm KW - sweat sensor KW - sweat rate KW - dehydration KW - IoT KW - PDMS KW - surface electromyography KW - handgrip force KW - force-varying muscle contraction KW - nonlinear analysis KW - wavelet scale selection KW - inertial measurement unit KW - gyroscope KW - asymmetry KW - feature extraction KW - gait analysis KW - lower limb prosthesis KW - trans-femoral amputee KW - MR damper KW - knee damping control KW - inertial measurement units KW - motion analysis KW - kinematics KW - functional activity KW - repeatability KW - reliability KW - biomechanics KW - cognitive frailty KW - cognitive–motor impairment KW - Alzheimer’s disease KW - motor planning error KW - instrumented trail-making task KW - ankle reaching task KW - dual task walking KW - nondestructive KW - joint moment KW - partial weight loading KW - muscle contributions KW - sit-to-stand training KW - motion parameters KW - step length KW - self-adaptation KW - Parkinson’s disease (PD) KW - tremor dominant (TD) KW - postural instability and gait difficulty (PIGD) KW - center of pressure (COP) KW - fast Fourier transform (FFT) KW - wavelet transform (WT) KW - fall detection system KW - smartphones KW - accelerometers KW - machine learning algorithms KW - supervised learning KW - ANOVA analysis KW - Step-detection KW - ActiGraph KW - Pedometer KW - acceleration KW - physical activity KW - physical function KW - physical performance test KW - chair stand KW - sit to stand transfer KW - wearables KW - gyroscopes KW - e-Health application KW - physical rehabilitation KW - shear and plantar pressure sensor KW - biaxial optical fiber sensor KW - multiplexed fiber Bragg gratings KW - frailty KW - pre-frail KW - wearable sensor KW - sedentary behavior KW - moderate-to-vigorous activity KW - steps KW - fall detection KW - elderly people monitoring KW - telerehabilitation KW - virtual therapy KW - Kinect KW - eHealth KW - telemedicine KW - insole KW - injury prevention KW - biomechanical gait variable estimation KW - inertial gait variable KW - total knee arthroplasty KW - falls in healthy elderly KW - fall prevention KW - biometrics KW - human gait recognition KW - ground reaction forces KW - Microsoft Kinect KW - high heels KW - fusion data KW - ensemble classifiers KW - accidental falls KW - older adults KW - neural networks KW - convolutional neural network KW - long short-term memory KW - accelerometry KW - obesity KW - nonlinear KW - electrostatic field sensing KW - gait measurement KW - temporal parameters KW - artificial neural network KW - propulsion KW - aging KW - walking KW - smart footwear KW - frailty prediction KW - fall risk KW - smartphone based assessments KW - adverse post-operative outcome KW - intelligent surveillance systems KW - human fall detection KW - health and well-being KW - safety and security KW - n/a KW - movement control KW - anterior cruciate ligament KW - kinetics KW - real-time feedback KW - biomechanical gait features KW - impaired gait classification KW - pattern recognition KW - sensors KW - clinical KW - knee KW - osteoarthritis KW - shear stress KW - callus KW - woman KW - TUG KW - IMU KW - geriatric assessment KW - semi-unsupervised KW - self-assessment KW - domestic environment KW - functional decline KW - symmetry KW - trunk movement KW - autocorrelation KW - gait rehabilitation KW - wearable device KW - IMU sensors KW - gait classification KW - stroke patients KW - neurological disorders KW - scanning laser rangefinders (SLR), GAITRite KW - cadence KW - velocity and stride-length KW - power KW - angular velocity KW - human motion measurement KW - sensor fusion KW - complementary filter KW - fuzzy logic KW - inertial and magnetic sensors KW - ESOQ-2 KW - Parkinson’s disease KW - UPDRS KW - movement disorders KW - human computer interface KW - RGB-Depth KW - hand tracking KW - automated assessment KW - at-home monitoring KW - Parkinson’s Diseases KW - motorized walker KW - haptic cue KW - gait pattern KW - statistics study KW - walk detection KW - step counting KW - signal processing KW - plantar pressure KW - flat foot KW - insoles KW - force sensors KW - arch index KW - sports analytics KW - deep learning KW - classification KW - inertial sensor KW - cross-country skiing KW - classical style KW - skating style KW - batteryless strain sensor KW - wireless strain sensor KW - resonant frequency modulation KW - Ecoflex KW - human activity recognition KW - smartphone KW - human daily activity KW - ensemble method KW - running KW - velocity KW - smart shoe KW - concussion KW - inertial motion units (IMUs) KW - vestibular exercises KW - validation KW - motion capture KW - user intent recognition KW - transfemoral prosthesis KW - multi-objective optimization KW - biogeography-based optimization KW - smart cane KW - weight-bearing KW - health monitoring KW - wearable/inertial sensors KW - regularity KW - variability KW - human KW - motion KW - locomotion KW - UPDRS tasks KW - posture KW - postural stability KW - center of mass KW - RGB-depth KW - neurorehabilitation KW - hallux abductus valgus KW - high heel KW - proximal phalanx of the hallux KW - abduction KW - valgus KW - ultrasonography KW - Achilles tendon KW - diagnostic KW - imaging KW - tendinopathy KW - foot insoles KW - electromyography KW - joint instability KW - muscle contractions KW - motorcycling KW - wearable electronic devices KW - validity KW - relative movement KW - lower limb prosthetics KW - biomechanic measurement tasks KW - quantifying socket fit KW - rehabilitation exercise KW - dynamic time warping KW - automatic coaching KW - exergame KW - fine-wire intramuscular EMG electrode KW - non-human primate model KW - traumatic spinal cord injury KW - wavelet transform KW - relative power KW - linear mixed model KW - VO2 KW - calibration KW - MET KW - VO2net KW - speed KW - equivalent speed KW - free-living KW - children KW - adolescents KW - adults KW - gait event detection KW - hemiplegic gait KW - appropriate mother wavelet KW - acceleration signal KW - wavelet-selection criteria KW - conductive textile KW - stroke KW - hemiparetic KW - real-time monitoring KW - lower limb locomotion activity KW - triplet Markov model KW - semi-Markov model KW - on-line EM algorithm KW - human kinematics KW - phase difference angle KW - cognitive-motor impairment KW - Alzheimer's disease KW - Parkinson's disease (PD) KW - Parkinson's disease KW - Parkinson's Diseases UR - https://www.unicat.be/uniCat?func=search&query=sysid:133822816 AB - In recent years, many technologies for gait and posture assessments have emerged. Wearable sensors, active and passive in-house monitors, and many combinations thereof all promise to provide accurate measures of physical activity, gait, and posture parameters. Motivated by market projections for wearable technologies and driven by recent technological innovations in wearable sensors (MEMs, electronic textiles, wireless communications, etc.), wearable health/performance research is growing rapidly and has the potential to transform future healthcare from disease treatment to disease prevention. The objective of this Special Issue is to address and disseminate the latest gait, posture, and activity monitoring systems as well as various mathematical models/methods that characterize mobility functions. This Special Issue focuses on wearable monitoring systems and physical sensors, and its mathematical models can be utilized in varied environments under varied conditions to monitor health and performance ER -