Recent Advances in Motion Analysis

The 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 weara...

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Bibliographic Details
Other Authors: Di Nardo, Francesco (Editor), Fioretti, Sandro (Editor)
Format: Electronic Book Chapter
Language:English
Published: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021
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DOAB: description of the publication
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520 |a The 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. 
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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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