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Wearable Movement Sensors for Rehabilitation: From Technology to Clinical Practice
| Wearable Movement Sensors for Rehabilitation: From Technology to Clinical Practice |
| Autore | Ribbers Gerard M |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 electronic resource (328 p.) |
| Soggetto topico | Technology: general issues |
| Soggetto non controllato |
accelerometers
wearable sensors exercise measurement GMFCS level relative orientation estimation IMU magnetometer-free gait analysis machine learning inertial measurement units neurological disorders falls validity 3-D motion analysis single leg squat motion capture clinical rehabilitation motor function outcomes implementation locomotion assistive devices embedded sensors accelerometry physical activity Fourier transform functional linear model walking distance lower limb amputation gait Lie group constrained extended Kalman filter pose estimation wearable devices distance measurement gait planning stride length center of pressure human-machine interaction perinatal stroke kinematics upper extremity cerebral palsy hemiplegia constraint inertial measurement unit wireless sensors network motion tracking range of motion shoulder goniometer spinal cord injury tetraplegia clinical setting circadian motor behavior body-worn sensors older adults physically active workers low back pain inertial motion units wearable sensor real-time gait detection insole pressure sensors pathological gait gait rehabilitation assistive device wearable technology stroke physical therapy arm use upper limb performance accelerometer sensor walking |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Wearable Movement Sensors for Rehabilitation |
| Record Nr. | UNINA-9910557359503321 |
Ribbers Gerard M
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Wearable Sensors in the Evaluation of Gait and Balance in Neurological Disorders
| Wearable Sensors in the Evaluation of Gait and Balance in Neurological Disorders |
| Autore | Suppa Antonio |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 |
| Descrizione fisica | 1 electronic resource (274 p.) |
| Soggetto topico | History of engineering & technology |
| Soggetto non controllato |
inertial measurement units
gait analysis biomedical signal processing pattern recognition step detection physiological signals Parkinson’s disease pathological gait turning analysis wearable sensors mobile gait analysis wearables inertial sensors traumatic brain injury dynamic balance gait disorders gait patterns head injury gait symmetry gait smoothness acceleration machine learning classification accelerometer GAITRite multi-regression normalization SVM random forest classifier balance gait transcranial direct current stimulation wearable electronics IMUs cueing posture rehabilitation cerebellar ataxia movement analysis personalized medicine stroke asymmetry trunk reliability validity aging reactive postural responses yaw perturbation kinematics postural stability dynamic posturography multiple sclerosis gait metrics test-retest reliability sampling frequency accelerometry autocorrelation harmonic ratio six-minute walk back school inertial sensor lower back pain stability timed up and go test gait assessment tri-axial accelerometer CV healthy subjects test-retest trajectory reconstruction stride segmentation dynamic time warping pedestrian dead-reckoning near falls loss of balance pre-impact fall detection activities of daily life bio-signals EEG EMG wireless sensors posturography Alzheimer’s disease vestibular syndrome diagnosis symptoms monitoring wearable home-monitoring |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557446403321 |
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Suppa Antonio
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||