Sensors for Gait, Posture, and Health Monitoring Volume 2

© 2020 by the authors; CC BY-NC-ND license

step detection; machine learning; outlier detection; transition matrices; autoencoders; ground reaction force (GRF); micro electro mechanical systems (MEMS); gait; walk; bipedal locomotion; 3-axis force sensor; shoe; force distribution; multi-sensor gait classification; distributed compressed sensing; joint sparse representation classification; telemonitoring of gait; operating range; accelerometer; stride length; peak tibial acceleration; running velocity; wearable sensors; feedback technology; gait; rehabilitation; motor control; cerebral palsy; gait; inertial sensors; gait events; spatiotemporal parameters; postural control; falls in the elderly; fall risk assessment; low-cost instrumented insoles; foot plantar center of pressure; flexible sensor; gait recognition; piezoelectric material; wearable; adaptability; force sensitive resistors; self-tuning triple threshold algorithm; sweat sensor; sweat rate; dehydration; IoT; PDMS; surface electromyography; handgrip force; force-varying muscle contraction; nonlinear analysis; wavelet scale selection; inertial measurement unit; accelerometer; gyroscope; asymmetry; feature extraction; wearable sensors; gait analysis; lower limb prosthesis; trans-femoral amputee; MR damper; knee damping control; inertial measurement units; motion analysis; kinematics; gait; functional activity; repeatability; reliability; biomechanics; cognitive frailty; wearable; cognitive–motor impairment; Alzheimer’s disease; motor planning error; instrumented trail-making task; ankle reaching task; dual task walking; nondestructive; joint moment; partial weight loading; muscle contributions; sit-to-stand training; motion parameters; gait analysis; step length; self-adaptation; Parkinson’s disease (PD); tremor dominant (TD); postural instability and gait difficulty (PIGD); center of pressure (COP); fast Fourier transform (FFT); wavelet transform (WT); fall detection system; inertial sensors; smartphones; accelerometers; machine learning algorithms; supervised learning; ANOVA analysis; Step-detection; ActiGraph; Pedometer; acceleration; physical activity; physical function; physical performance test; chair stand; sit to stand transfer; wearables; inertial sensors; accelerometers; gyroscopes; gait analysis; e-Health application; physical rehabilitation; shear and plantar pressure sensor; biaxial optical fiber sensor; multiplexed fiber Bragg gratings; frailty; pre-frail; wearable sensor; physical activity; sedentary behavior; moderate-to-vigorous activity; steps; fall detection; wearable sensors; elderly people monitoring; telerehabilitation; virtual therapy; Kinect; eHealth; telemedicine; gait; insole; injury prevention; biomechanical gait variable estimation; inertial gait variable; total knee arthroplasty; wearable sensors; falls in healthy elderly; fall risk assessment; fall prevention; fall detection; wearable sensors; biometrics; human gait recognition; ground reaction forces; Microsoft Kinect; high heels; fusion data; ensemble classifiers; accidental falls; older adults; machine learning; neural networks; convolutional neural network; long short-term memory; accelerometry; obesity; postural control; nonlinear; electrostatic field sensing; gait measurement; temporal parameters; frailty; gait; artificial neural network; propulsion; aging; wearable sensor; walking; smart footwear; frailty prediction; fall risk; smartphone based assessments; adverse post-operative outcome; intelligent surveillance systems; human fall detection; health and well-being; safety and security; n/a; biomechanics; movement control; anterior cruciate ligament; kinetics; real-time feedback; gait analysis; biomechanical gait features; impaired gait classification; accelerometer; machine learning; pattern recognition; sensors; gait; clinical; knee; osteoarthritis; shear stress; shoe; callus; walking; woman; TUG; IMU; frailty; geriatric assessment; machine learning; wearable sensors; semi-unsupervised; self-assessment; domestic environment; functional decline; symmetry; trunk movement; autocorrelation; gait rehabilitation; wearable device; gait analysis; IMU sensors; gait classification; stroke patients; neurological disorders; gait recognition; scanning laser rangefinders (SLR), GAITRite; cadence; velocity and stride-length; inertial sensors; anterior cruciate ligament; rehabilitation; knee; gyroscope; power; angular velocity; human motion measurement; sensor fusion; complementary filter; fuzzy logic; inertial and magnetic sensors; ESOQ-2; Parkinson’s disease; UPDRS; movement disorders; human computer interface; RGB-Depth; hand tracking; automated assessment; machine learning; at-home monitoring; Parkinson’s Diseases; motorized walker; haptic cue; gait pattern; statistics study; walk detection; step counting; gait analysis; machine learning; signal processing; plantar pressure; flat foot; insoles; force sensors; arch index; sports analytics; deep learning; classification; inertial sensor; cross-country skiing; classical style; skating style; batteryless strain sensor; wireless strain sensor; resonant frequency modulation; Ecoflex; gait; human activity recognition; smartphone; wearable sensor; human daily activity; ensemble method; wearable sensors; inertial measurement unit; gait; running; stride length; velocity; smart shoe; concussion; inertial motion units (IMUs); vestibular exercises; validation; motion capture; user intent recognition; transfemoral prosthesis; multi-objective optimization; biogeography-based optimization; smart cane; weight-bearing; gait analysis; health monitoring; wearable/inertial sensors; accelerometer; regularity; variability; human; motion; locomotion; autocorrelation; Parkinson’s disease; UPDRS tasks; movement disorders; posture; postural stability; center of mass; RGB-depth; automated assessment; machine learning; at-home monitoring; neurorehabilitation; hallux abductus valgus; high heel; proximal phalanx of the hallux; abduction; valgus; ultrasonography; Achilles tendon; diagnostic; imaging; tendinopathy; foot insoles; electromyography; joint instability; muscle contractions; motorcycling; plantar pressure; accelerometry; wearable electronic devices; validity; walking; physical activity; relative movement; lower limb prosthetics; biomechanic measurement tasks; quantifying socket fit; gait analysis; aging; physical function; rehabilitation exercise; Kinect; dynamic time warping; automatic coaching; exergame; fine-wire intramuscular EMG electrode; non-human primate model; traumatic spinal cord injury; wavelet transform; relative power; linear mixed model; physical activity; accelerometer; VO₂; calibration; MET; VO_2net; speed; equivalent speed; free-living; children; adolescents; adults; gait event detection; hemiplegic gait; appropriate mother wavelet; acceleration signal; wavelet-selection criteria; conductive textile; gait; stroke; hemiparetic; real-time monitoring; gait analysis; lower limb locomotion activity; triplet Markov model; semi-Markov model; on-line EM algorithm; gait analysis; wearable sensors; inertial measurement unit; human kinematics; phase difference angle