Sensors for Gait, Posture, and Health Monitoring Volume 3
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 wea...
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| Year of Publication: | 2020 |
| Language: | English |
| Physical Description: | 1 electronic resource (386 p.) |
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| 005 | 20231214133457.0 | ||
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| 008 | 202105s2020 xx |||||o ||| 0|eng d | ||
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| 035 | |a (oapen)https://directory.doabooks.org/handle/20.500.12854/68636 | ||
| 035 | |a (EXLCZ)995400000000040982 | ||
| 041 | 0 | |a eng | |
| 100 | 1 | |a Lockhart, Thurmon |4 edt | |
| 245 | 1 | 0 | |a Sensors for Gait, Posture, and Health Monitoring Volume 3 |
| 260 | |a Basel, Switzerland |b MDPI - Multidisciplinary Digital Publishing Institute |c 2020 | ||
| 300 | |a 1 electronic resource (386 p.) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 520 | |a 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 | ||
| 546 | |a English | ||
| 650 | 7 | |a Humanities |2 bicssc | |
| 650 | 7 | |a Social interaction |2 bicssc | |
| 653 | |a step detection | ||
| 653 | |a machine learning | ||
| 653 | |a outlier detection | ||
| 653 | |a transition matrices | ||
| 653 | |a autoencoders | ||
| 653 | |a ground reaction force (GRF) | ||
| 653 | |a micro electro mechanical systems (MEMS) | ||
| 653 | |a gait | ||
| 653 | |a walk | ||
| 653 | |a bipedal locomotion | ||
| 653 | |a 3-axis force sensor | ||
| 653 | |a shoe | ||
| 653 | |a force distribution | ||
| 653 | |a multi-sensor gait classification | ||
| 653 | |a distributed compressed sensing | ||
| 653 | |a joint sparse representation classification | ||
| 653 | |a telemonitoring of gait | ||
| 653 | |a operating range | ||
| 653 | |a accelerometer | ||
| 653 | |a stride length | ||
| 653 | |a peak tibial acceleration | ||
| 653 | |a running velocity | ||
| 653 | |a wearable sensors | ||
| 653 | |a feedback technology | ||
| 653 | |a rehabilitation | ||
| 653 | |a motor control | ||
| 653 | |a cerebral palsy | ||
| 653 | |a inertial sensors | ||
| 653 | |a gait events | ||
| 653 | |a spatiotemporal parameters | ||
| 653 | |a postural control | ||
| 653 | |a falls in the elderly | ||
| 653 | |a fall risk assessment | ||
| 653 | |a low-cost instrumented insoles | ||
| 653 | |a foot plantar center of pressure | ||
| 653 | |a flexible sensor | ||
| 653 | |a gait recognition | ||
| 653 | |a piezoelectric material | ||
| 653 | |a wearable | ||
| 653 | |a adaptability | ||
| 653 | |a force sensitive resistors | ||
| 653 | |a self-tuning triple threshold algorithm | ||
| 653 | |a sweat sensor | ||
| 653 | |a sweat rate | ||
| 653 | |a dehydration | ||
| 653 | |a IoT | ||
| 653 | |a PDMS | ||
| 653 | |a surface electromyography | ||
| 653 | |a handgrip force | ||
| 653 | |a force-varying muscle contraction | ||
| 653 | |a nonlinear analysis | ||
| 653 | |a wavelet scale selection | ||
| 653 | |a inertial measurement unit | ||
| 653 | |a gyroscope | ||
| 653 | |a asymmetry | ||
| 653 | |a feature extraction | ||
| 653 | |a gait analysis | ||
| 653 | |a lower limb prosthesis | ||
| 653 | |a trans-femoral amputee | ||
| 653 | |a MR damper | ||
| 653 | |a knee damping control | ||
| 653 | |a inertial measurement units | ||
| 653 | |a motion analysis | ||
| 653 | |a kinematics | ||
| 653 | |a functional activity | ||
| 653 | |a repeatability | ||
| 653 | |a reliability | ||
| 653 | |a biomechanics | ||
| 653 | |a cognitive frailty | ||
| 653 | |a cognitive-motor impairment | ||
| 653 | |a Alzheimer's disease | ||
| 653 | |a motor planning error | ||
| 653 | |a instrumented trail-making task | ||
| 653 | |a ankle reaching task | ||
| 653 | |a dual task walking | ||
| 653 | |a nondestructive | ||
| 653 | |a joint moment | ||
| 653 | |a partial weight loading | ||
| 653 | |a muscle contributions | ||
| 653 | |a sit-to-stand training | ||
| 653 | |a motion parameters | ||
| 653 | |a step length | ||
| 653 | |a self-adaptation | ||
| 653 | |a Parkinson's disease (PD) | ||
| 653 | |a tremor dominant (TD) | ||
| 653 | |a postural instability and gait difficulty (PIGD) | ||
| 653 | |a center of pressure (COP) | ||
| 653 | |a fast Fourier transform (FFT) | ||
| 653 | |a wavelet transform (WT) | ||
| 653 | |a fall detection system | ||
| 653 | |a smartphones | ||
| 653 | |a accelerometers | ||
| 653 | |a machine learning algorithms | ||
| 653 | |a supervised learning | ||
| 653 | |a ANOVA analysis | ||
| 653 | |a Step-detection | ||
| 653 | |a ActiGraph | ||
| 653 | |a Pedometer | ||
| 653 | |a acceleration | ||
| 653 | |a physical activity | ||
| 653 | |a physical function | ||
| 653 | |a physical performance test | ||
| 653 | |a chair stand | ||
| 653 | |a sit to stand transfer | ||
| 653 | |a wearables | ||
| 653 | |a gyroscopes | ||
| 653 | |a e-Health application | ||
| 653 | |a physical rehabilitation | ||
| 653 | |a shear and plantar pressure sensor | ||
| 653 | |a biaxial optical fiber sensor | ||
| 653 | |a multiplexed fiber Bragg gratings | ||
| 653 | |a frailty | ||
| 653 | |a pre-frail | ||
| 653 | |a wearable sensor | ||
| 653 | |a sedentary behavior | ||
| 653 | |a moderate-to-vigorous activity | ||
| 653 | |a steps | ||
| 653 | |a fall detection | ||
| 653 | |a elderly people monitoring | ||
| 653 | |a telerehabilitation | ||
| 653 | |a virtual therapy | ||
| 653 | |a Kinect | ||
| 653 | |a eHealth | ||
| 653 | |a telemedicine | ||
| 653 | |a insole | ||
| 653 | |a injury prevention | ||
| 653 | |a biomechanical gait variable estimation | ||
| 653 | |a inertial gait variable | ||
| 653 | |a total knee arthroplasty | ||
| 653 | |a falls in healthy elderly | ||
| 653 | |a fall prevention | ||
| 653 | |a biometrics | ||
| 653 | |a human gait recognition | ||
| 653 | |a ground reaction forces | ||
| 653 | |a Microsoft Kinect | ||
| 653 | |a high heels | ||
| 653 | |a fusion data | ||
| 653 | |a ensemble classifiers | ||
| 653 | |a accidental falls | ||
| 653 | |a older adults | ||
| 653 | |a neural networks | ||
| 653 | |a convolutional neural network | ||
| 653 | |a long short-term memory | ||
| 653 | |a accelerometry | ||
| 653 | |a obesity | ||
| 653 | |a nonlinear | ||
| 653 | |a electrostatic field sensing | ||
| 653 | |a gait measurement | ||
| 653 | |a temporal parameters | ||
| 653 | |a artificial neural network | ||
| 653 | |a propulsion | ||
| 653 | |a aging | ||
| 653 | |a walking | ||
| 653 | |a smart footwear | ||
| 653 | |a frailty prediction | ||
| 653 | |a fall risk | ||
| 653 | |a smartphone based assessments | ||
| 653 | |a adverse post-operative outcome | ||
| 653 | |a intelligent surveillance systems | ||
| 653 | |a human fall detection | ||
| 653 | |a health and well-being | ||
| 653 | |a safety and security | ||
| 653 | |a movement control | ||
| 653 | |a anterior cruciate ligament | ||
| 653 | |a kinetics | ||
| 653 | |a real-time feedback | ||
| 653 | |a biomechanical gait features | ||
| 653 | |a impaired gait classification | ||
| 653 | |a pattern recognition | ||
| 653 | |a sensors | ||
| 653 | |a clinical | ||
| 653 | |a knee | ||
| 653 | |a osteoarthritis | ||
| 653 | |a shear stress | ||
| 653 | |a callus | ||
| 653 | |a woman | ||
| 653 | |a TUG | ||
| 653 | |a IMU | ||
| 653 | |a geriatric assessment | ||
| 653 | |a semi-unsupervised | ||
| 653 | |a self-assessment | ||
| 653 | |a domestic environment | ||
| 653 | |a functional decline | ||
| 653 | |a symmetry | ||
| 653 | |a trunk movement | ||
| 653 | |a autocorrelation | ||
| 653 | |a gait rehabilitation | ||
| 653 | |a wearable device | ||
| 653 | |a IMU sensors | ||
| 653 | |a gait classification | ||
| 653 | |a stroke patients | ||
| 653 | |a neurological disorders | ||
| 653 | |a scanning laser rangefinders (SLR), GAITRite | ||
| 653 | |a cadence | ||
| 653 | |a velocity and stride-length | ||
| 653 | |a power | ||
| 653 | |a angular velocity | ||
| 653 | |a human motion measurement | ||
| 653 | |a sensor fusion | ||
| 653 | |a complementary filter | ||
| 653 | |a fuzzy logic | ||
| 653 | |a inertial and magnetic sensors | ||
| 653 | |a ESOQ-2 | ||
| 653 | |a Parkinson's disease | ||
| 653 | |a UPDRS | ||
| 653 | |a movement disorders | ||
| 653 | |a human computer interface | ||
| 653 | |a RGB-Depth | ||
| 653 | |a hand tracking | ||
| 653 | |a automated assessment | ||
| 653 | |a at-home monitoring | ||
| 653 | |a Parkinson's Diseases | ||
| 653 | |a motorized walker | ||
| 653 | |a haptic cue | ||
| 653 | |a gait pattern | ||
| 653 | |a statistics study | ||
| 653 | |a walk detection | ||
| 653 | |a step counting | ||
| 653 | |a signal processing | ||
| 653 | |a plantar pressure | ||
| 653 | |a flat foot | ||
| 653 | |a insoles | ||
| 653 | |a force sensors | ||
| 653 | |a arch index | ||
| 653 | |a sports analytics | ||
| 653 | |a deep learning | ||
| 653 | |a classification | ||
| 653 | |a inertial sensor | ||
| 653 | |a cross-country skiing | ||
| 653 | |a classical style | ||
| 653 | |a skating style | ||
| 653 | |a batteryless strain sensor | ||
| 653 | |a wireless strain sensor | ||
| 653 | |a resonant frequency modulation | ||
| 653 | |a Ecoflex | ||
| 653 | |a human activity recognition | ||
| 653 | |a smartphone | ||
| 653 | |a human daily activity | ||
| 653 | |a ensemble method | ||
| 653 | |a running | ||
| 653 | |a velocity | ||
| 653 | |a smart shoe | ||
| 653 | |a concussion | ||
| 653 | |a inertial motion units (IMUs) | ||
| 653 | |a vestibular exercises | ||
| 653 | |a validation | ||
| 653 | |a motion capture | ||
| 653 | |a user intent recognition | ||
| 653 | |a transfemoral prosthesis | ||
| 653 | |a multi-objective optimization | ||
| 653 | |a biogeography-based optimization | ||
| 653 | |a smart cane | ||
| 653 | |a weight-bearing | ||
| 653 | |a health monitoring | ||
| 653 | |a wearable/inertial sensors | ||
| 653 | |a regularity | ||
| 653 | |a variability | ||
| 653 | |a human | ||
| 653 | |a motion | ||
| 653 | |a locomotion | ||
| 653 | |a UPDRS tasks | ||
| 653 | |a posture | ||
| 653 | |a postural stability | ||
| 653 | |a center of mass | ||
| 653 | |a RGB-depth | ||
| 653 | |a neurorehabilitation | ||
| 653 | |a hallux abductus valgus | ||
| 653 | |a high heel | ||
| 653 | |a proximal phalanx of the hallux | ||
| 653 | |a abduction | ||
| 653 | |a valgus | ||
| 653 | |a ultrasonography | ||
| 653 | |a Achilles tendon | ||
| 653 | |a diagnostic | ||
| 653 | |a imaging | ||
| 653 | |a tendinopathy | ||
| 653 | |a foot insoles | ||
| 653 | |a electromyography | ||
| 653 | |a joint instability | ||
| 653 | |a muscle contractions | ||
| 653 | |a motorcycling | ||
| 653 | |a wearable electronic devices | ||
| 653 | |a validity | ||
| 653 | |a relative movement | ||
| 653 | |a lower limb prosthetics | ||
| 653 | |a biomechanic measurement tasks | ||
| 653 | |a quantifying socket fit | ||
| 653 | |a rehabilitation exercise | ||
| 653 | |a dynamic time warping | ||
| 653 | |a automatic coaching | ||
| 653 | |a exergame | ||
| 653 | |a fine-wire intramuscular EMG electrode | ||
| 653 | |a non-human primate model | ||
| 653 | |a traumatic spinal cord injury | ||
| 653 | |a wavelet transform | ||
| 653 | |a relative power | ||
| 653 | |a linear mixed model | ||
| 653 | |a VO2 | ||
| 653 | |a calibration | ||
| 653 | |a MET | ||
| 653 | |a VO2net | ||
| 653 | |a speed | ||
| 653 | |a equivalent speed | ||
| 653 | |a free-living | ||
| 653 | |a children | ||
| 653 | |a adolescents | ||
| 653 | |a adults | ||
| 653 | |a gait event detection | ||
| 653 | |a hemiplegic gait | ||
| 653 | |a appropriate mother wavelet | ||
| 653 | |a acceleration signal | ||
| 653 | |a wavelet-selection criteria | ||
| 653 | |a conductive textile | ||
| 653 | |a stroke | ||
| 653 | |a hemiparetic | ||
| 653 | |a real-time monitoring | ||
| 653 | |a lower limb locomotion activity | ||
| 653 | |a triplet Markov model | ||
| 653 | |a semi-Markov model | ||
| 653 | |a on-line EM algorithm | ||
| 653 | |a human kinematics | ||
| 653 | |a phase difference angle | ||
| 776 | |z 3-03936-346-8 | ||
| 776 | |z 3-03936-347-6 | ||
| 700 | 1 | |a Lockhart, Thurmon |4 oth | |
| 906 | |a BOOK | ||
| ADM | |b 2023-12-15 05:55:14 Europe/Vienna |f system |c marc21 |a 2022-04-04 09:22:53 Europe/Vienna |g false | ||
| AVE | |i DOAB Directory of Open Access Books |P DOAB Directory of Open Access Books |x https://eu02.alma.exlibrisgroup.com/view/uresolver/43ACC_OEAW/openurl?u.ignore_date_coverage=true&portfolio_pid=5338091980004498&Force_direct=true |Z 5338091980004498 |b Available |8 5338091980004498 | ||