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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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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. |
format | Online Article Text |
id | pubmed-7248695 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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