Recent Advances in Motion Analysis
| Weitere Verfasser: |
Di Nardo, Francesco
, [HerausgeberIn]
Fioretti, Sandro , [HerausgeberIn] |
|---|---|
| Umfang/Format: |
1 online resource (192 pages). |
| Schlagworte: | |
| Online Zugang: |
DOAB: download the publication DOAB: description of the publication |
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| 100 | 1 | |a Di Nardo, Francesco |e editor | |
| 264 | |b MDPI - Multidisciplinary Digital Publishing Institute, |c 2021. | ||
| 700 | 1 | |a Fioretti, Sandro |e editor | |
| 700 | 1 | |a Di Nardo, Francesco |e other | |
| 700 | 1 | |a Fioretti, Sandro |e other | |
| 245 | 1 | 0 | |a Recent Advances in Motion Analysis |
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| 546 | |a English | ||
| 650 | 7 | |a Technology: general issues |2 bicssc | |
| 653 | |a falls | ||
| 653 | |a slips | ||
| 653 | |a trips | ||
| 653 | |a postural perturbations | ||
| 653 | |a wearables | ||
| 653 | |a stretch-sensors | ||
| 653 | |a ankle kinematics | ||
| 653 | |a rowing | ||
| 653 | |a technology | ||
| 653 | |a inertial sensor | ||
| 653 | |a accelerometer | ||
| 653 | |a performance | ||
| 653 | |a signal processing | ||
| 653 | |a sEMG | ||
| 653 | |a knee | ||
| 653 | |a random forest | ||
| 653 | |a principal component analysis | ||
| 653 | |a back propagation | ||
| 653 | |a estimation model | ||
| 653 | |a knee angle | ||
| 653 | |a deep learning | ||
| 653 | |a neural networks | ||
| 653 | |a gait-phase classification | ||
| 653 | |a electrogoniometer | ||
| 653 | |a EMG sensors | ||
| 653 | |a walking | ||
| 653 | |a gait-event detection | ||
| 653 | |a automotive radar | ||
| 653 | |a machine learning | ||
| 653 | |a walking analysis | ||
| 653 | |a seated posture | ||
| 653 | |a cognitive engagement | ||
| 653 | |a stress level | ||
| 653 | |a load cells | ||
| 653 | |a embedded systems | ||
| 653 | |a sensorized seat | ||
| 653 | |a flexion-relaxation phenomenon | ||
| 653 | |a surface electromyography | ||
| 653 | |a wearable device | ||
| 653 | |a WBSN | ||
| 653 | |a automatic detection of the FRP | ||
| 653 | |a Internet of Things (IoT) | ||
| 653 | |a human activity recognition (HAR) | ||
| 653 | |a motion analysis | ||
| 653 | |a wearable sensors | ||
| 653 | |a cerebral palsy | ||
| 653 | |a hemiplegia | ||
| 653 | |a motor disorders | ||
| 653 | |a gait variability | ||
| 653 | |a coefficient of variation | ||
| 653 | |a surface EMG | ||
| 653 | |a statistical gait analysis | ||
| 653 | |a activation patterns | ||
| 653 | |a co-activation | ||
| 653 | |a Parkinson's disease | ||
| 653 | |a activity recognition | ||
| 653 | |a rate invariance | ||
| 653 | |a Lie group | ||
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