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Gaussian Mixture Models for Control of Quasi-Passive Spinal Exoskeletons

Research and development of active and passive exoskeletons for preventing work related injuries has steadily increased in the last decade. Recently, new types of quasi-passive designs have been emerging. These exoskeletons use passive viscoelastic elements, such as springs and dampers, to provide s...

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Detalles Bibliográficos
Autores principales: Jamšek, Marko, Petrič, Tadej, Babič, Jan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7248695/
https://www.ncbi.nlm.nih.gov/pubmed/32397455
http://dx.doi.org/10.3390/s20092705
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author Jamšek, Marko
Petrič, Tadej
Babič, Jan
author_facet Jamšek, Marko
Petrič, Tadej
Babič, Jan
author_sort Jamšek, Marko
collection PubMed
description Research and development of active and passive exoskeletons for preventing work related injuries has steadily increased in the last decade. Recently, new types of quasi-passive designs have been emerging. These exoskeletons use passive viscoelastic elements, such as springs and dampers, to provide support to the user, while using small actuators only to change the level of support or to disengage the passive elements. Control of such devices is still largely unexplored, especially the algorithms that predict the movement of the user, to take maximum advantage of the passive viscoelastic elements. To address this issue, we developed a new control scheme consisting of Gaussian mixture models (GMM) in combination with a state machine controller to identify and classify the movement of the user as early as possible and thus provide a timely control output for the quasi-passive spinal exoskeleton. In a leave-one-out cross-validation procedure, the overall accuracy for providing support to the user was [Formula: see text] % (mean ± s.d.) with a sensitivity and specificity of [Formula: see text] % and [Formula: see text] % respectively. The results of this study indicate that our approach is a promising tool for the control of quasi-passive spinal exoskeletons.
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spelling pubmed-72486952020-08-13 Gaussian Mixture Models for Control of Quasi-Passive Spinal Exoskeletons Jamšek, Marko Petrič, Tadej Babič, Jan Sensors (Basel) Article Research and development of active and passive exoskeletons for preventing work related injuries has steadily increased in the last decade. Recently, new types of quasi-passive designs have been emerging. These exoskeletons use passive viscoelastic elements, such as springs and dampers, to provide support to the user, while using small actuators only to change the level of support or to disengage the passive elements. Control of such devices is still largely unexplored, especially the algorithms that predict the movement of the user, to take maximum advantage of the passive viscoelastic elements. To address this issue, we developed a new control scheme consisting of Gaussian mixture models (GMM) in combination with a state machine controller to identify and classify the movement of the user as early as possible and thus provide a timely control output for the quasi-passive spinal exoskeleton. In a leave-one-out cross-validation procedure, the overall accuracy for providing support to the user was [Formula: see text] % (mean ± s.d.) with a sensitivity and specificity of [Formula: see text] % and [Formula: see text] % respectively. The results of this study indicate that our approach is a promising tool for the control of quasi-passive spinal exoskeletons. MDPI 2020-05-09 /pmc/articles/PMC7248695/ /pubmed/32397455 http://dx.doi.org/10.3390/s20092705 Text en © 2020 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 (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ).
spellingShingle Article
Jamšek, Marko
Petrič, Tadej
Babič, Jan
Gaussian Mixture Models for Control of Quasi-Passive Spinal Exoskeletons
title Gaussian Mixture Models for Control of Quasi-Passive Spinal Exoskeletons
title_full Gaussian Mixture Models for Control of Quasi-Passive Spinal Exoskeletons
title_fullStr Gaussian Mixture Models for Control of Quasi-Passive Spinal Exoskeletons
title_full_unstemmed Gaussian Mixture Models for Control of Quasi-Passive Spinal Exoskeletons
title_short Gaussian Mixture Models for Control of Quasi-Passive Spinal Exoskeletons
title_sort gaussian mixture models for control of quasi-passive spinal exoskeletons
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7248695/
https://www.ncbi.nlm.nih.gov/pubmed/32397455
http://dx.doi.org/10.3390/s20092705
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