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A Quick Turn of Foot: Rigid Foot-Ground Contact Models for Human Motion Prediction

Computer simulation can be used to predict human walking motions as a tool of basic science, device design, and for surgical planning. One the challenges of predicting human walking is accurately synthesizing both the movements and ground forces of the stance foot. Though the foot is commonly modele...

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Autores principales: Millard, Matthew, Mombaur, Katja
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6693511/
https://www.ncbi.nlm.nih.gov/pubmed/31440154
http://dx.doi.org/10.3389/fnbot.2019.00062
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author Millard, Matthew
Mombaur, Katja
author_facet Millard, Matthew
Mombaur, Katja
author_sort Millard, Matthew
collection PubMed
description Computer simulation can be used to predict human walking motions as a tool of basic science, device design, and for surgical planning. One the challenges of predicting human walking is accurately synthesizing both the movements and ground forces of the stance foot. Though the foot is commonly modeled as a viscoelastic element, rigid foot-ground contact models offer some advantages: fitting is reduced to a geometric problem, and the numerical stiffness of the equations of motion is similar in both swing and stance. In this work, we evaluate two rigid-foot ground contact models: the ellipse-foot (a single-segment foot), and the double-circle foot (a two-segment foot). To evaluate the foot models we use three different comparisons to experimental data: first we compare how accurately the kinematics of the ankle frame fit those of the model when it is forced to track the measured center-of-pressure (CoP) kinematics; second, we compare how each foot affects how accuracy of a sagittal plane gait model that tracks a subjects walking motion; and third, we assess how each model affects a walking motion prediction. For the prediction problem we consider a unique cost function that includes terms related to both muscular effort and foot-ground impacts. Although the ellipse-foot is superior to the double-circle foot in terms of fit and the accuracy of the tracking OCP solution, the predictive simulation reveals that the ellipse-foot is capable of producing large force transients due to its geometry: when the ankle quickly traverses its u-shaped trajectory, the body is accelerated the body upwards, and large ground forces result. In contrast, the two-segment double-circle foot produces ground forces that are of a similar magnitude to the experimental subject because the additional forefoot segment plastically contacts the ground, arresting its motion, similar to a human foot.
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spelling pubmed-66935112019-08-22 A Quick Turn of Foot: Rigid Foot-Ground Contact Models for Human Motion Prediction Millard, Matthew Mombaur, Katja Front Neurorobot Neuroscience Computer simulation can be used to predict human walking motions as a tool of basic science, device design, and for surgical planning. One the challenges of predicting human walking is accurately synthesizing both the movements and ground forces of the stance foot. Though the foot is commonly modeled as a viscoelastic element, rigid foot-ground contact models offer some advantages: fitting is reduced to a geometric problem, and the numerical stiffness of the equations of motion is similar in both swing and stance. In this work, we evaluate two rigid-foot ground contact models: the ellipse-foot (a single-segment foot), and the double-circle foot (a two-segment foot). To evaluate the foot models we use three different comparisons to experimental data: first we compare how accurately the kinematics of the ankle frame fit those of the model when it is forced to track the measured center-of-pressure (CoP) kinematics; second, we compare how each foot affects how accuracy of a sagittal plane gait model that tracks a subjects walking motion; and third, we assess how each model affects a walking motion prediction. For the prediction problem we consider a unique cost function that includes terms related to both muscular effort and foot-ground impacts. Although the ellipse-foot is superior to the double-circle foot in terms of fit and the accuracy of the tracking OCP solution, the predictive simulation reveals that the ellipse-foot is capable of producing large force transients due to its geometry: when the ankle quickly traverses its u-shaped trajectory, the body is accelerated the body upwards, and large ground forces result. In contrast, the two-segment double-circle foot produces ground forces that are of a similar magnitude to the experimental subject because the additional forefoot segment plastically contacts the ground, arresting its motion, similar to a human foot. Frontiers Media S.A. 2019-08-07 /pmc/articles/PMC6693511/ /pubmed/31440154 http://dx.doi.org/10.3389/fnbot.2019.00062 Text en Copyright © 2019 Millard and Mombaur. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Millard, Matthew
Mombaur, Katja
A Quick Turn of Foot: Rigid Foot-Ground Contact Models for Human Motion Prediction
title A Quick Turn of Foot: Rigid Foot-Ground Contact Models for Human Motion Prediction
title_full A Quick Turn of Foot: Rigid Foot-Ground Contact Models for Human Motion Prediction
title_fullStr A Quick Turn of Foot: Rigid Foot-Ground Contact Models for Human Motion Prediction
title_full_unstemmed A Quick Turn of Foot: Rigid Foot-Ground Contact Models for Human Motion Prediction
title_short A Quick Turn of Foot: Rigid Foot-Ground Contact Models for Human Motion Prediction
title_sort quick turn of foot: rigid foot-ground contact models for human motion prediction
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6693511/
https://www.ncbi.nlm.nih.gov/pubmed/31440154
http://dx.doi.org/10.3389/fnbot.2019.00062
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