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Neural network-based estimation of lower limb joint kinematics:A minimally intrusive approach for gait analysis

Medicine in Novel Technology and Devices 2024年第3期23卷 81-97页

作者： Farid Kenas Nadia Saadia Amina Ababou Noureddine Ababou mahdi zabat Karim BenSiSaid Laboratory of Robotics Parallelism and Embedded Systems University of Science and Technology Houari Boumediene AlgiersAlgeria Laboratory of Instrumentation University of Science and Technology Houari Boumediene AlgiersAlgeria

The establishment of a quantitative gait analysis system holds paramount importance,particularly in the context of functional rehabilitation of the lower *** emphasize the imperative for sensors to be portable,compact,integrated,and non-intrusive,crucial characteristics in the rehabilitation field to facilitate their use and ensure optimal integration into care *** study investigates an innovative approach aimed at reducing the reliance on body-fixed sensors by harnessing their data within a neural network,thus concentrating on the joint kinematics of the lower *** primary objective is to estimate the flexion-extension angles of the hip,knee,and ankle during walking,utilizing data collected by two sensors positioned on the subject's ***,the neural network undergoes training with calculated data(leg tilt angles and angular velocities)sourced from the OpenSim database,followed by further refinement with experimental data obtained from a subject walking on a treadmill,wherein leg tilt angles and angular velocities are *** significance of this research is underscored by the demonstrated capability,through conducted tests,of the implemented networks to efficiently fuse data from a minimal set of ***,the proposed approach emerges as both practical and minimally intrusive,facilitating a robust evaluation of gait kinematic parameters.

关键词： Artificial neural network Joint angle Inertial measurement unit Kinematic gait analysis Treadmill

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