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Real-time estimation of FES-induced joint torque with evoked EMG: Application to spinal cord injured patients
BACKGROUND: Functional electrical stimulation (FES) is a neuroprosthetic technique for restoring lost motor function of spinal cord injured (SCI) patients and motor-impaired subjects by delivering short electrical pulses to their paralyzed muscles or motor nerves. FES induces action potentials respe...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4918196/ https://www.ncbi.nlm.nih.gov/pubmed/27334441 http://dx.doi.org/10.1186/s12984-016-0169-y |
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author | Li, Zhan Guiraud, David Andreu, David Benoussaad, Mourad Fattal, Charles Hayashibe, Mitsuhiro |
author_facet | Li, Zhan Guiraud, David Andreu, David Benoussaad, Mourad Fattal, Charles Hayashibe, Mitsuhiro |
author_sort | Li, Zhan |
collection | PubMed |
description | BACKGROUND: Functional electrical stimulation (FES) is a neuroprosthetic technique for restoring lost motor function of spinal cord injured (SCI) patients and motor-impaired subjects by delivering short electrical pulses to their paralyzed muscles or motor nerves. FES induces action potentials respectively on muscles or nerves so that muscle activity can be characterized by the synchronous recruitment of motor units with its compound electromyography (EMG) signal is called M-wave. The recorded evoked EMG (eEMG) can be employed to predict the resultant joint torque, and modeling of FES-induced joint torque based on eEMG is an essential step to provide necessary prediction of the expected muscle response before achieving accurate joint torque control by FES. METHODS: Previous works on FES-induced torque tracking issues were mainly based on offline analysis. However, toward personalized clinical rehabilitation applications, real-time FES systems are essentially required considering the subject-specific muscle responses against electrical stimulation. This paper proposes a wireless portable stimulator used for estimating/predicting joint torque based on real time processing of eEMG. Kalman filter and recurrent neural network (RNN) are embedded into the real-time FES system for identification and estimation. RESULTS: Prediction results on 3 able-bodied subjects and 3 SCI patients demonstrate promising performances. As estimators, both Kalman filter and RNN approaches show clinically feasible results on estimation/prediction of joint torque with eEMG signals only, moreover RNN requires less computational requirement. CONCLUSION: The proposed real-time FES system establishes a platform for estimating and assessing the mechanical output, the electromyographic recordings and associated models. It will contribute to open a new modality for personalized portable neuroprosthetic control toward consolidated personal healthcare for motor-impaired patients. |
format | Online Article Text |
id | pubmed-4918196 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-49181962016-06-24 Real-time estimation of FES-induced joint torque with evoked EMG: Application to spinal cord injured patients Li, Zhan Guiraud, David Andreu, David Benoussaad, Mourad Fattal, Charles Hayashibe, Mitsuhiro J Neuroeng Rehabil Research BACKGROUND: Functional electrical stimulation (FES) is a neuroprosthetic technique for restoring lost motor function of spinal cord injured (SCI) patients and motor-impaired subjects by delivering short electrical pulses to their paralyzed muscles or motor nerves. FES induces action potentials respectively on muscles or nerves so that muscle activity can be characterized by the synchronous recruitment of motor units with its compound electromyography (EMG) signal is called M-wave. The recorded evoked EMG (eEMG) can be employed to predict the resultant joint torque, and modeling of FES-induced joint torque based on eEMG is an essential step to provide necessary prediction of the expected muscle response before achieving accurate joint torque control by FES. METHODS: Previous works on FES-induced torque tracking issues were mainly based on offline analysis. However, toward personalized clinical rehabilitation applications, real-time FES systems are essentially required considering the subject-specific muscle responses against electrical stimulation. This paper proposes a wireless portable stimulator used for estimating/predicting joint torque based on real time processing of eEMG. Kalman filter and recurrent neural network (RNN) are embedded into the real-time FES system for identification and estimation. RESULTS: Prediction results on 3 able-bodied subjects and 3 SCI patients demonstrate promising performances. As estimators, both Kalman filter and RNN approaches show clinically feasible results on estimation/prediction of joint torque with eEMG signals only, moreover RNN requires less computational requirement. CONCLUSION: The proposed real-time FES system establishes a platform for estimating and assessing the mechanical output, the electromyographic recordings and associated models. It will contribute to open a new modality for personalized portable neuroprosthetic control toward consolidated personal healthcare for motor-impaired patients. BioMed Central 2016-06-22 /pmc/articles/PMC4918196/ /pubmed/27334441 http://dx.doi.org/10.1186/s12984-016-0169-y Text en © The Author(s) 2016 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Li, Zhan Guiraud, David Andreu, David Benoussaad, Mourad Fattal, Charles Hayashibe, Mitsuhiro Real-time estimation of FES-induced joint torque with evoked EMG: Application to spinal cord injured patients |
title | Real-time estimation of FES-induced joint torque with evoked EMG: Application to spinal cord injured patients |
title_full | Real-time estimation of FES-induced joint torque with evoked EMG: Application to spinal cord injured patients |
title_fullStr | Real-time estimation of FES-induced joint torque with evoked EMG: Application to spinal cord injured patients |
title_full_unstemmed | Real-time estimation of FES-induced joint torque with evoked EMG: Application to spinal cord injured patients |
title_short | Real-time estimation of FES-induced joint torque with evoked EMG: Application to spinal cord injured patients |
title_sort | real-time estimation of fes-induced joint torque with evoked emg: application to spinal cord injured patients |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4918196/ https://www.ncbi.nlm.nih.gov/pubmed/27334441 http://dx.doi.org/10.1186/s12984-016-0169-y |
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