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Biologically Inspired Optimal Terminal Iterative Learning Control for the Swing Phase of Gait in a Hybrid Neuroprosthesis: A Modeling Study

(1) Background: An iterative learning control (ILC) strategy was developed for a “Muscle First” Motor-Assisted Hybrid Neuroprosthesis (MAHNP). The MAHNP combines a backdrivable exoskeletal brace with neural stimulation technology to enable persons with paraplegia due to spinal cord injury (SCI) to e...

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Autores principales: Makowski, Nathaniel S., Fitzpatrick, Marshaun N., Triolo, Ronald J., Reyes, Ryan-David, Quinn, Roger D., Audu, Musa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8869465/
https://www.ncbi.nlm.nih.gov/pubmed/35200424
http://dx.doi.org/10.3390/bioengineering9020071
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author Makowski, Nathaniel S.
Fitzpatrick, Marshaun N.
Triolo, Ronald J.
Reyes, Ryan-David
Quinn, Roger D.
Audu, Musa
author_facet Makowski, Nathaniel S.
Fitzpatrick, Marshaun N.
Triolo, Ronald J.
Reyes, Ryan-David
Quinn, Roger D.
Audu, Musa
author_sort Makowski, Nathaniel S.
collection PubMed
description (1) Background: An iterative learning control (ILC) strategy was developed for a “Muscle First” Motor-Assisted Hybrid Neuroprosthesis (MAHNP). The MAHNP combines a backdrivable exoskeletal brace with neural stimulation technology to enable persons with paraplegia due to spinal cord injury (SCI) to execute ambulatory motions and walk upright. (2) Methods: The ILC strategy was developed to swing the legs in a biologically inspired ballistic fashion. It maximizes muscular recruitment and activates the motorized exoskeletal bracing to assist the motion as needed. The control algorithm was tested using an anatomically realistic three-dimensional musculoskeletal model of the lower leg and pelvis suitably modified to account for exoskeletal inertia. The model was developed and tested with the OpenSim biomechanical modeling suite. (3) Results: Preliminary data demonstrate the efficacy of the controller in swing-leg simulations and its ability to learn to balance muscular and motor contributions to improve performance and accomplish consistent stepping. In particular, the controller took 15 iterations to achieve the desired outcome with 0.3% error.
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spelling pubmed-88694652022-02-25 Biologically Inspired Optimal Terminal Iterative Learning Control for the Swing Phase of Gait in a Hybrid Neuroprosthesis: A Modeling Study Makowski, Nathaniel S. Fitzpatrick, Marshaun N. Triolo, Ronald J. Reyes, Ryan-David Quinn, Roger D. Audu, Musa Bioengineering (Basel) Article (1) Background: An iterative learning control (ILC) strategy was developed for a “Muscle First” Motor-Assisted Hybrid Neuroprosthesis (MAHNP). The MAHNP combines a backdrivable exoskeletal brace with neural stimulation technology to enable persons with paraplegia due to spinal cord injury (SCI) to execute ambulatory motions and walk upright. (2) Methods: The ILC strategy was developed to swing the legs in a biologically inspired ballistic fashion. It maximizes muscular recruitment and activates the motorized exoskeletal bracing to assist the motion as needed. The control algorithm was tested using an anatomically realistic three-dimensional musculoskeletal model of the lower leg and pelvis suitably modified to account for exoskeletal inertia. The model was developed and tested with the OpenSim biomechanical modeling suite. (3) Results: Preliminary data demonstrate the efficacy of the controller in swing-leg simulations and its ability to learn to balance muscular and motor contributions to improve performance and accomplish consistent stepping. In particular, the controller took 15 iterations to achieve the desired outcome with 0.3% error. MDPI 2022-02-12 /pmc/articles/PMC8869465/ /pubmed/35200424 http://dx.doi.org/10.3390/bioengineering9020071 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Makowski, Nathaniel S.
Fitzpatrick, Marshaun N.
Triolo, Ronald J.
Reyes, Ryan-David
Quinn, Roger D.
Audu, Musa
Biologically Inspired Optimal Terminal Iterative Learning Control for the Swing Phase of Gait in a Hybrid Neuroprosthesis: A Modeling Study
title Biologically Inspired Optimal Terminal Iterative Learning Control for the Swing Phase of Gait in a Hybrid Neuroprosthesis: A Modeling Study
title_full Biologically Inspired Optimal Terminal Iterative Learning Control for the Swing Phase of Gait in a Hybrid Neuroprosthesis: A Modeling Study
title_fullStr Biologically Inspired Optimal Terminal Iterative Learning Control for the Swing Phase of Gait in a Hybrid Neuroprosthesis: A Modeling Study
title_full_unstemmed Biologically Inspired Optimal Terminal Iterative Learning Control for the Swing Phase of Gait in a Hybrid Neuroprosthesis: A Modeling Study
title_short Biologically Inspired Optimal Terminal Iterative Learning Control for the Swing Phase of Gait in a Hybrid Neuroprosthesis: A Modeling Study
title_sort biologically inspired optimal terminal iterative learning control for the swing phase of gait in a hybrid neuroprosthesis: a modeling study
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8869465/
https://www.ncbi.nlm.nih.gov/pubmed/35200424
http://dx.doi.org/10.3390/bioengineering9020071
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