Reprint

Recent Advances in Motion Analysis

Edited by
May 2021
192 pages
  • ISBN978-3-0365-0438-4 (Hardback)
  • ISBN978-3-0365-0439-1 (PDF)

This book is a reprint of the Special Issue Recent Advances in Motion Analysis that was published in

Computer Science & Mathematics
Engineering
Physical Sciences
Summary
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.
Format
  • Hardback
License and Copyright
© 2022 by the authors; CC BY-NC-ND license
Keywords
falls; 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; machine learning; surface electromyography; 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

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