The aim of this study is to estimate the stiffness of the muscle-tendon unit, of human lower limb, during the execution of a normal gait cycle. Unlike the analytical techniques already widely validated in literature and discussed below, a probabilistic approach based on the Gaussian Mixture Model (GMM) has been adopted here for the computation of the muscle-tendon unit stiffness. The obtained results for the major muscle groups are shown. The effectiveness of the proposed approach has been evaluated by computing the Root Mean Square (RMS) error between the stiffnesses calculated analytically and those calculated using the GMM, for each subject.

Human Muscle-Tendon Stiffness Estimation during Normal Gait Cycle based on Gaussian Mixture Model

BORTOLETTO, ROBERTO;MICHIELETTO, STEFANO;PAGELLO, ENRICO;
2015

Abstract

The aim of this study is to estimate the stiffness of the muscle-tendon unit, of human lower limb, during the execution of a normal gait cycle. Unlike the analytical techniques already widely validated in literature and discussed below, a probabilistic approach based on the Gaussian Mixture Model (GMM) has been adopted here for the computation of the muscle-tendon unit stiffness. The obtained results for the major muscle groups are shown. The effectiveness of the proposed approach has been evaluated by computing the Root Mean Square (RMS) error between the stiffnesses calculated analytically and those calculated using the GMM, for each subject.
2015
Advances in Intelligent Systems and Computing
THE 13th INTERNATIONAL CONFERENCE ON INTELLIGENT AUTONOMOUS SYSTEMS
978-3-319-08337-7
978-3-319-08338-4
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/2835794
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