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Estimation of gait parameters using leg velocity for amputee population

Quantification of key gait parameters plays an important role in assessing gait deficits in clinical research. Gait parameter estimation using lower-limb kinematics (mainly leg velocity data) has shown promise but lacks validation for the amputee population. The aim of this study is to assess the ac...

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Autores principales: Aftab, Zohaib, Shad, Rizwan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9106160/
https://www.ncbi.nlm.nih.gov/pubmed/35560138
http://dx.doi.org/10.1371/journal.pone.0266726
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author Aftab, Zohaib
Shad, Rizwan
author_facet Aftab, Zohaib
Shad, Rizwan
author_sort Aftab, Zohaib
collection PubMed
description Quantification of key gait parameters plays an important role in assessing gait deficits in clinical research. Gait parameter estimation using lower-limb kinematics (mainly leg velocity data) has shown promise but lacks validation for the amputee population. The aim of this study is to assess the accuracy of lower-leg angular velocity to predict key gait events (toe-off and heel strike) and associated temporal parameters for the amputee population. An open data set of reflexive markers during treadmill walking from 10 subjects with unilateral transfemoral amputation was used. A rule-based dual-minima algorithm was developed to detect the landmarks in the shank velocity signal indicating toe-off and heel strike events. Four temporal gait parameters were also estimated (step time, stride time, stance and swing duration). These predictions were compared against the force platform data for 3000 walking cycles from 239 walking trials. Considerable accuracy was achieved for the HS event as well as for step and stride timings, with mean errors ranging from 0 to -13ms. The TO prediction exhibited a larger error with its mean ranging from 35-81ms. The algorithm consistently predicted the TO earlier than the actual event, resulting in prediction errors in stance and swing timings. Significant differences were found between the prediction for sound and prosthetic legs, with better TO accuracy on the prosthetic side. The prediction accuracy also appeared to improve with the subjects’ mobility level (K-level). In conclusion, the leg velocity profile, coupled with the dual-minima algorithm, can predict temporal parameters for the transfemoral amputee population with varying degrees of accuracy.
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spelling pubmed-91061602022-05-14 Estimation of gait parameters using leg velocity for amputee population Aftab, Zohaib Shad, Rizwan PLoS One Research Article Quantification of key gait parameters plays an important role in assessing gait deficits in clinical research. Gait parameter estimation using lower-limb kinematics (mainly leg velocity data) has shown promise but lacks validation for the amputee population. The aim of this study is to assess the accuracy of lower-leg angular velocity to predict key gait events (toe-off and heel strike) and associated temporal parameters for the amputee population. An open data set of reflexive markers during treadmill walking from 10 subjects with unilateral transfemoral amputation was used. A rule-based dual-minima algorithm was developed to detect the landmarks in the shank velocity signal indicating toe-off and heel strike events. Four temporal gait parameters were also estimated (step time, stride time, stance and swing duration). These predictions were compared against the force platform data for 3000 walking cycles from 239 walking trials. Considerable accuracy was achieved for the HS event as well as for step and stride timings, with mean errors ranging from 0 to -13ms. The TO prediction exhibited a larger error with its mean ranging from 35-81ms. The algorithm consistently predicted the TO earlier than the actual event, resulting in prediction errors in stance and swing timings. Significant differences were found between the prediction for sound and prosthetic legs, with better TO accuracy on the prosthetic side. The prediction accuracy also appeared to improve with the subjects’ mobility level (K-level). In conclusion, the leg velocity profile, coupled with the dual-minima algorithm, can predict temporal parameters for the transfemoral amputee population with varying degrees of accuracy. Public Library of Science 2022-05-13 /pmc/articles/PMC9106160/ /pubmed/35560138 http://dx.doi.org/10.1371/journal.pone.0266726 Text en © 2022 Aftab, Shad https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Aftab, Zohaib
Shad, Rizwan
Estimation of gait parameters using leg velocity for amputee population
title Estimation of gait parameters using leg velocity for amputee population
title_full Estimation of gait parameters using leg velocity for amputee population
title_fullStr Estimation of gait parameters using leg velocity for amputee population
title_full_unstemmed Estimation of gait parameters using leg velocity for amputee population
title_short Estimation of gait parameters using leg velocity for amputee population
title_sort estimation of gait parameters using leg velocity for amputee population
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9106160/
https://www.ncbi.nlm.nih.gov/pubmed/35560138
http://dx.doi.org/10.1371/journal.pone.0266726
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