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12528namaa2204309ui 4500 |
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20221228154124.0 |
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DE-2553 |
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m o d |
| 007 |
cr|mn|---annan |
| 008 |
20210501s2020 xx |||||o ||| 0|eng d |
| 020 |
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|a books978-3-03936-343-8
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| 020 |
|
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|a 9783039363421
|
| 020 |
|
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|a 9783039363438
|
| 040 |
|
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|a oapen
|c oapen
|b eng
|d DE-2553
|e rda
|
| 024 |
7 |
|
|a 10.3390/books978-3-03936-343-8
|c doi
|
| 041 |
0 |
|
|a eng
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| 042 |
|
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|a dc
|
| 072 |
|
7 |
|a TBX
|2 bicssc
|
| 100 |
1 |
|
|a Lockhart, Thurmon
|e editor
|
| 264 |
|
|
|b MDPI - Multidisciplinary Digital Publishing Institute,
|c 2020.
|
| 700 |
1 |
|
|a Lockhart, Thurmon
|e other
|
| 245 |
1 |
0 |
|a Sensors for Gait, Posture, and Health Monitoring Volume 1
|
| 300 |
|
|
|a 1 online resource (410 pages).
|
| 336 |
|
|
|a text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a computer
|b c
|2 rdamedia
|
| 338 |
|
|
|a online resource
|b cr
|2 rdacarrier
|
| 506 |
0 |
|
|a Open Access
|2 star
|f Unrestricted online access
|
| 540 |
|
|
|a Creative Commons
|f https://creativecommons.org/licenses/by/4.0/
|2 cc
|4 https://creativecommons.org/licenses/by/4.0/
|
| 546 |
|
|
|a English
|
| 650 |
|
7 |
|a History of engineering & technology
|2 bicssc
|
| 653 |
|
|
|a step detection
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| 653 |
|
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|a machine learning
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| 653 |
|
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|a outlier detection
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| 653 |
|
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|a transition matrices
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| 653 |
|
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|a autoencoders
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| 653 |
|
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|a ground reaction force (GRF)
|
| 653 |
|
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|a micro electro mechanical systems (MEMS)
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| 653 |
|
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|a gait
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| 653 |
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|a walk
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| 653 |
|
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|a bipedal locomotion
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| 653 |
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|a 3-axis force sensor
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| 653 |
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|a shoe
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| 653 |
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|a force distribution
|
| 653 |
|
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|a multi-sensor gait classification
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| 653 |
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|a distributed compressed sensing
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| 653 |
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|a joint sparse representation classification
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| 653 |
|
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|a telemonitoring of gait
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| 653 |
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|a operating range
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| 653 |
|
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|a accelerometer
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| 653 |
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|a stride length
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| 653 |
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|a peak tibial acceleration
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| 653 |
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|a running velocity
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| 653 |
|
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|a wearable sensors
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| 653 |
|
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|a feedback technology
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| 653 |
|
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|a rehabilitation
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| 653 |
|
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|a motor control
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| 653 |
|
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|a cerebral palsy
|
| 653 |
|
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|a inertial sensors
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| 653 |
|
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|a gait events
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| 653 |
|
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|a spatiotemporal parameters
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| 653 |
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|a postural control
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| 653 |
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|a falls in the elderly
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| 653 |
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|a fall risk assessment
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| 653 |
|
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|a low-cost instrumented insoles
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| 653 |
|
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|a foot plantar center of pressure
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| 653 |
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|a flexible sensor
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| 653 |
|
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|a gait recognition
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| 653 |
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|a piezoelectric material
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| 653 |
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|a wearable
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| 653 |
|
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|a adaptability
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| 653 |
|
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|a force sensitive resistors
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| 653 |
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|a self-tuning triple threshold algorithm
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| 653 |
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|a sweat sensor
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| 653 |
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|a sweat rate
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| 653 |
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|a dehydration
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| 653 |
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|a IoT
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| 653 |
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|a PDMS
|
| 653 |
|
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|a surface electromyography
|
| 653 |
|
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|a handgrip force
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| 653 |
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|a force-varying muscle contraction
|
| 653 |
|
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|a nonlinear analysis
|
| 653 |
|
|
|a wavelet scale selection
|
| 653 |
|
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|a inertial measurement unit
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| 653 |
|
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|a gyroscope
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| 653 |
|
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|a asymmetry
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| 653 |
|
|
|a feature extraction
|
| 653 |
|
|
|a gait analysis
|
| 653 |
|
|
|a lower limb prosthesis
|
| 653 |
|
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|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 |
|
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|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 |
|
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|a nondestructive
|
| 653 |
|
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|a joint moment
|
| 653 |
|
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|a partial weight loading
|
| 653 |
|
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|a muscle contributions
|
| 653 |
|
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|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 n/a
|
| 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 |
|
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|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 |
|
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|a complementary filter
|
| 653 |
|
|
|a fuzzy logic
|
| 653 |
|
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|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 |
|
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|a plantar pressure
|
| 653 |
|
|
|a flat foot
|
| 653 |
|
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|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
|
| 856 |
4 |
0 |
|a www.oapen.org
|u https://mdpi.com/books/pdfview/book/2396
|7 0
|z DOAB: download the publication
|
| 856 |
4 |
0 |
|a www.oapen.org
|u https://directory.doabooks.org/handle/20.500.12854/68634
|7 0
|z DOAB: description of the publication
|
| 590 |
|
|
|a Online publication
|
| 590 |
|
|
|a ebookoa1222
|
| 590 |
|
|
|a doab
|
| 942 |
|
|
|2 z
|c EB
|
| 999 |
|
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|c 3027514
|d 1431269
|
| 952 |
|
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|0 0
|1 0
|2 z
|4 0
|6 ONLINE
|7 1
|9 973153
|R 2022-12-28 14:41:24
|a DAIG
|b DAIG
|l 0
|o Online
|r 2022-12-28
|y EB
|
