LEADER 04323nam 2201045z- 450 001 9910557900003321 005 20231214132858.0 035 $a(CKB)5400000000046280 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/76283 035 $a(EXLCZ)995400000000046280 100 $a20202201d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aRecent Advances in Motion Analysis 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 electronic resource (192 p.) 311 $a3-0365-0438-9 311 $a3-0365-0439-7 330 $aThe advances in the technology and methodology for human movement capture and analysis over the last decade have been remarkable. Besides acknowledged approaches for kinematic, dynamic, and electromyographic (EMG) analysis carried out in the laboratory, more recently developed devices, such as wearables, inertial measurement units, ambient sensors, and cameras or depth sensors, have been adopted on a wide scale. Furthermore, computational intelligence (CI) methods, such as artificial neural networks, have recently emerged as promising tools for the development and application of intelligent systems in motion analysis. Thus, the synergy of classic instrumentation and novel smart devices and techniques has created unique capabilities in the continuous monitoring of motor behaviors in different fields, such as clinics, sports, and ergonomics. However, real-time sensing, signal processing, human activity recognition, and characterization and interpretation of motion metrics and behaviors from sensor data still representing a challenging problem not only in laboratories but also at home and in the community. This book addresses open research issues related to the improvement of classic approaches and the development of novel technologies and techniques in the domain of motion analysis in all the various fields of application. 606 $aTechnology: general issues$2bicssc 610 $afalls 610 $aslips 610 $atrips 610 $apostural perturbations 610 $awearables 610 $astretch-sensors 610 $aankle kinematics 610 $arowing 610 $atechnology 610 $ainertial sensor 610 $aaccelerometer 610 $aperformance 610 $asignal processing 610 $asEMG 610 $aknee 610 $arandom forest 610 $aprincipal component analysis 610 $aback propagation 610 $aestimation model 610 $aknee angle 610 $adeep learning 610 $aneural networks 610 $agait-phase classification 610 $aelectrogoniometer 610 $aEMG sensors 610 $awalking 610 $agait-event detection 610 $aautomotive radar 610 $amachine learning 610 $awalking analysis 610 $aseated posture 610 $acognitive engagement 610 $astress level 610 $aload cells 610 $aembedded systems 610 $asensorized seat 610 $aflexion-relaxation phenomenon 610 $asurface electromyography 610 $awearable device 610 $aWBSN 610 $aautomatic detection of the FRP 610 $aInternet of Things (IoT) 610 $ahuman activity recognition (HAR) 610 $amotion analysis 610 $awearable sensors 610 $acerebral palsy 610 $ahemiplegia 610 $amotor disorders 610 $agait variability 610 $acoefficient of variation 610 $asurface EMG 610 $astatistical gait analysis 610 $aactivation patterns 610 $aco-activation 610 $aParkinson?s disease 610 $aactivity recognition 610 $arate invariance 610 $aLie group 615 7$aTechnology: general issues 700 $aDi Nardo$b Francesco$4edt$01311295 702 $aFioretti$b Sandro$4edt 702 $aDi Nardo$b Francesco$4oth 702 $aFioretti$b Sandro$4oth 906 $aBOOK 912 $a9910557900003321 996 $aRecent Advances in Motion Analysis$93030209 997 $aUNINA