000 | 01368nam a2200133Ia 4500 | ||
---|---|---|---|
008 | 220620s9999||||xx |||||||||||||| ||und|| | ||
020 | _a9783040000000 | ||
245 | 0 | _aRecent Advances in Motion Analysis | |
546 | _aEnglish[eng] | ||
650 | _afalls||slips||trips||postural perturbations||wearables||stretch-sensors||ankle kinematics||rowing||technology||inertial sensor||accelerometer||performance||signal processing||sEMG||knee||random forest||principal component analysis||back propagation||estimation model||knee angle||deep learning||neural networks||gait-phase classification||electrogoniometer||EMG sensors||walking||gait-event detection||automotive radar||machine learning||walking analysis||seated posture||cognitive engagement||stress level||load cells||embedded systems||sensorized seat||flexion-relaxation phenomenon||surface electromyography||wearable device||WBSN||automatic detection of the FRP||Internet of Things (IoT)||human activity recognition (HAR)||motion analysis||wearable sensors||cerebral palsy||hemiplegia||motor disorders||gait variability||coefficient of variation||surface EMG||statistical gait analysis||activation patterns||co-activation||Parkinson’s disease||activity recognition||rate invariance||Lie group | ||
700 | _aDi Nardo, Francesco||Fioretti, Sandro | ||
856 | _uhttps://mdpi.com/books/pdfview/book/3661 | ||
942 | _cEB | ||
999 |
_c34207 _d34207 |