NARX muscle model based on Functional Electrical Stimulation (FES) using polynomial estimators and Singular Value Decomposition (SVD)

NARX muscle model based on Functional Electrical Stimulation (FES) using polynomial estimators and Singular Value Decomposition (SVD)

This paper presents the development of muscle model based on FES stimulation parameters using the Nonlinear Auto-Regressive model with Exogenous Inputs (NARX). FES stimulations with varying frequency, pulse width and pulse duration were used to estimate the muscle torque. The proposed approach manag...

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Bibliographic Details
Published in:Proceedings - 2015 IEEE Conference on System, Process and Control, ICSPC 2015
Main Author: Yassin I.M.; Zabidi A.; Jailani R.; Tahir N.M.; Ali M.S.A.M.
Format: Conference paper
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2016
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85009135369&doi=10.1109%2fSPC.2015.7473579&partnerID=40&md5=106b7692191df7c12d8cd44768d25944
id 2-s2.0-85009135369
spelling 2-s2.0-85009135369
Yassin I.M.; Zabidi A.; Jailani R.; Tahir N.M.; Ali M.S.A.M.
NARX muscle model based on Functional Electrical Stimulation (FES) using polynomial estimators and Singular Value Decomposition (SVD)
2016
Proceedings - 2015 IEEE Conference on System, Process and Control, ICSPC 2015


10.1109/SPC.2015.7473579
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85009135369&doi=10.1109%2fSPC.2015.7473579&partnerID=40&md5=106b7692191df7c12d8cd44768d25944
This paper presents the development of muscle model based on FES stimulation parameters using the Nonlinear Auto-Regressive model with Exogenous Inputs (NARX). FES stimulations with varying frequency, pulse width and pulse duration were used to estimate the muscle torque. The proposed approach managed to approximate the behavior of the system well with unbiased residuals. © 2015 IEEE.
Institute of Electrical and Electronics Engineers Inc.

English
Conference paper
author Yassin I.M.; Zabidi A.; Jailani R.; Tahir N.M.; Ali M.S.A.M.
spellingShingle Yassin I.M.; Zabidi A.; Jailani R.; Tahir N.M.; Ali M.S.A.M.
NARX muscle model based on Functional Electrical Stimulation (FES) using polynomial estimators and Singular Value Decomposition (SVD)
author_facet Yassin I.M.; Zabidi A.; Jailani R.; Tahir N.M.; Ali M.S.A.M.
author_sort Yassin I.M.; Zabidi A.; Jailani R.; Tahir N.M.; Ali M.S.A.M.
title NARX muscle model based on Functional Electrical Stimulation (FES) using polynomial estimators and Singular Value Decomposition (SVD)
title_short NARX muscle model based on Functional Electrical Stimulation (FES) using polynomial estimators and Singular Value Decomposition (SVD)
title_full NARX muscle model based on Functional Electrical Stimulation (FES) using polynomial estimators and Singular Value Decomposition (SVD)
title_fullStr NARX muscle model based on Functional Electrical Stimulation (FES) using polynomial estimators and Singular Value Decomposition (SVD)
title_full_unstemmed NARX muscle model based on Functional Electrical Stimulation (FES) using polynomial estimators and Singular Value Decomposition (SVD)
title_sort NARX muscle model based on Functional Electrical Stimulation (FES) using polynomial estimators and Singular Value Decomposition (SVD)
publishDate 2016
container_title Proceedings - 2015 IEEE Conference on System, Process and Control, ICSPC 2015
container_volume
container_issue
doi_str_mv 10.1109/SPC.2015.7473579
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85009135369&doi=10.1109%2fSPC.2015.7473579&partnerID=40&md5=106b7692191df7c12d8cd44768d25944
description This paper presents the development of muscle model based on FES stimulation parameters using the Nonlinear Auto-Regressive model with Exogenous Inputs (NARX). FES stimulations with varying frequency, pulse width and pulse duration were used to estimate the muscle torque. The proposed approach managed to approximate the behavior of the system well with unbiased residuals. © 2015 IEEE.
publisher Institute of Electrical and Electronics Engineers Inc.
issn
language English
format Conference paper
accesstype
record_format scopus
collection Scopus
_version_ 1809677910313795584

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