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Predicting muscle forces of individuals with hemiparesis following stroke

BACKGROUND: Functional electrical stimulation (FES) has been used to improve function in individuals with hemiparesis following stroke. An ideal functional electrical stimulation (FES) system needs an accurate mathematical model capable of designing subject and task-specific stimulation patterns. Su...

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Autores principales: Kesar, Trisha M, Ding, Jun, Wexler, Anthony S, Perumal, Ramu, Maladen, Ryan, Binder-Macleod, Stuart A
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2008
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2292738/
https://www.ncbi.nlm.nih.gov/pubmed/18304360
http://dx.doi.org/10.1186/1743-0003-5-7
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author Kesar, Trisha M
Ding, Jun
Wexler, Anthony S
Perumal, Ramu
Maladen, Ryan
Binder-Macleod, Stuart A
author_facet Kesar, Trisha M
Ding, Jun
Wexler, Anthony S
Perumal, Ramu
Maladen, Ryan
Binder-Macleod, Stuart A
author_sort Kesar, Trisha M
collection PubMed
description BACKGROUND: Functional electrical stimulation (FES) has been used to improve function in individuals with hemiparesis following stroke. An ideal functional electrical stimulation (FES) system needs an accurate mathematical model capable of designing subject and task-specific stimulation patterns. Such a model was previously developed in our laboratory and shown to predict the isometric forces produced by the quadriceps femoris muscles of able-bodied individuals and individuals with spinal cord injury in response to a wide range of clinically relevant stimulation frequencies and patterns. The aim of this study was to test our isometric muscle force model on the quadriceps femoris, ankle dorsiflexor, and ankle plantar-flexor muscles of individuals with post-stroke hemiparesis. METHODS: Subjects were seated on a force dynamometer and isometric forces were measured in response to a range of stimulation frequencies (10 to 80-Hz) and 3 different patterns. Subject-specific model parameter values were obtained by fitting the measured force responses from 2 stimulation trains. The model parameters thus obtained were then used to obtain predicted forces for a range of frequencies and patterns. Predicted and measured forces were compared using intra-class correlation coefficients, r(2 )values, and model error relative to the physiological error (variability of measured forces). RESULTS: Results showed excellent agreement between measured and predicted force-time responses (r(2 )>0.80), peak forces (ICCs>0.84), and force-time integrals (ICCs>0.82) for the quadriceps, dorsiflexor, and plantar-fexor muscles. The model error was within or below the +95% confidence interval of the physiological error for >88% comparisons between measured and predicted forces. CONCLUSION: Our results show that the model has potential to be incorporated as a feed-forward controller for predicting subject-specific stimulation patterns during FES.
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spelling pubmed-22927382008-04-14 Predicting muscle forces of individuals with hemiparesis following stroke Kesar, Trisha M Ding, Jun Wexler, Anthony S Perumal, Ramu Maladen, Ryan Binder-Macleod, Stuart A J Neuroeng Rehabil Research BACKGROUND: Functional electrical stimulation (FES) has been used to improve function in individuals with hemiparesis following stroke. An ideal functional electrical stimulation (FES) system needs an accurate mathematical model capable of designing subject and task-specific stimulation patterns. Such a model was previously developed in our laboratory and shown to predict the isometric forces produced by the quadriceps femoris muscles of able-bodied individuals and individuals with spinal cord injury in response to a wide range of clinically relevant stimulation frequencies and patterns. The aim of this study was to test our isometric muscle force model on the quadriceps femoris, ankle dorsiflexor, and ankle plantar-flexor muscles of individuals with post-stroke hemiparesis. METHODS: Subjects were seated on a force dynamometer and isometric forces were measured in response to a range of stimulation frequencies (10 to 80-Hz) and 3 different patterns. Subject-specific model parameter values were obtained by fitting the measured force responses from 2 stimulation trains. The model parameters thus obtained were then used to obtain predicted forces for a range of frequencies and patterns. Predicted and measured forces were compared using intra-class correlation coefficients, r(2 )values, and model error relative to the physiological error (variability of measured forces). RESULTS: Results showed excellent agreement between measured and predicted force-time responses (r(2 )>0.80), peak forces (ICCs>0.84), and force-time integrals (ICCs>0.82) for the quadriceps, dorsiflexor, and plantar-fexor muscles. The model error was within or below the +95% confidence interval of the physiological error for >88% comparisons between measured and predicted forces. CONCLUSION: Our results show that the model has potential to be incorporated as a feed-forward controller for predicting subject-specific stimulation patterns during FES. BioMed Central 2008-02-27 /pmc/articles/PMC2292738/ /pubmed/18304360 http://dx.doi.org/10.1186/1743-0003-5-7 Text en Copyright © 2008 Kesar et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research
Kesar, Trisha M
Ding, Jun
Wexler, Anthony S
Perumal, Ramu
Maladen, Ryan
Binder-Macleod, Stuart A
Predicting muscle forces of individuals with hemiparesis following stroke
title Predicting muscle forces of individuals with hemiparesis following stroke
title_full Predicting muscle forces of individuals with hemiparesis following stroke
title_fullStr Predicting muscle forces of individuals with hemiparesis following stroke
title_full_unstemmed Predicting muscle forces of individuals with hemiparesis following stroke
title_short Predicting muscle forces of individuals with hemiparesis following stroke
title_sort predicting muscle forces of individuals with hemiparesis following stroke
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2292738/
https://www.ncbi.nlm.nih.gov/pubmed/18304360
http://dx.doi.org/10.1186/1743-0003-5-7
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