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Learning to walk with an adaptive gain proportional myoelectric controller for a robotic ankle exoskeleton

BACKGROUND: Robotic ankle exoskeletons can provide assistance to users and reduce metabolic power during walking. Our research group has investigated the use of proportional myoelectric control for controlling robotic ankle exoskeletons. Previously, these controllers have relied on a constant gain t...

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Autores principales: Koller, Jeffrey R., Jacobs, Daniel A., Ferris, Daniel P., Remy, C. David
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4634144/
https://www.ncbi.nlm.nih.gov/pubmed/26536868
http://dx.doi.org/10.1186/s12984-015-0086-5
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author Koller, Jeffrey R.
Jacobs, Daniel A.
Ferris, Daniel P.
Remy, C. David
author_facet Koller, Jeffrey R.
Jacobs, Daniel A.
Ferris, Daniel P.
Remy, C. David
author_sort Koller, Jeffrey R.
collection PubMed
description BACKGROUND: Robotic ankle exoskeletons can provide assistance to users and reduce metabolic power during walking. Our research group has investigated the use of proportional myoelectric control for controlling robotic ankle exoskeletons. Previously, these controllers have relied on a constant gain to map user’s muscle activity to actuation control signals. A constant gain may act as a constraint on the user, so we designed a controller that dynamically adapts the gain to the user’s myoelectric amplitude. We hypothesized that an adaptive gain proportional myoelectric controller would reduce metabolic energy expenditure compared to walking with the ankle exoskeleton unpowered because users could choose their preferred control gain. METHODS: We tested eight healthy subjects walking with the adaptive gain proportional myoelectric controller with bilateral ankle exoskeletons. The adaptive gain was updated each stride such that on average the user’s peak muscle activity was mapped to maximal power output of the exoskeleton. All subjects participated in three identical training sessions where they walked on a treadmill for 50 minutes (30 minutes of which the exoskeleton was powered) at 1.2 ms(-1). We calculated and analyzed metabolic energy consumption, muscle recruitment, inverse kinematics, inverse dynamics, and exoskeleton mechanics. RESULTS: Using our controller, subjects achieved a metabolic reduction similar to that seen in previous work in about a third of the training time. The resulting controller gain was lower than that seen in previous work (β=1.50±0.14 versus a constant β=2). The adapted gain allowed users more total ankle joint power than that of unassisted walking, increasing ankle power in exchange for a decrease in hip power. CONCLUSIONS: Our findings indicate that humans prefer to walk with greater ankle mechanical power output than their unassisted gait when provided with an ankle exoskeleton using an adaptive controller. This suggests that robotic assistance from an exoskeleton can allow humans to adopt gait patterns different from their normal choices for locomotion. In our specific experiment, subjects increased ankle power and decreased hip power to walk with a reduction in metabolic cost. Future exoskeleton devices that rely on proportional myolectric control are likely to demonstrate improved performance by including an adaptive gain. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12984-015-0086-5) contains supplementary material, which is available to authorized users.
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spelling pubmed-46341442015-11-06 Learning to walk with an adaptive gain proportional myoelectric controller for a robotic ankle exoskeleton Koller, Jeffrey R. Jacobs, Daniel A. Ferris, Daniel P. Remy, C. David J Neuroeng Rehabil Research BACKGROUND: Robotic ankle exoskeletons can provide assistance to users and reduce metabolic power during walking. Our research group has investigated the use of proportional myoelectric control for controlling robotic ankle exoskeletons. Previously, these controllers have relied on a constant gain to map user’s muscle activity to actuation control signals. A constant gain may act as a constraint on the user, so we designed a controller that dynamically adapts the gain to the user’s myoelectric amplitude. We hypothesized that an adaptive gain proportional myoelectric controller would reduce metabolic energy expenditure compared to walking with the ankle exoskeleton unpowered because users could choose their preferred control gain. METHODS: We tested eight healthy subjects walking with the adaptive gain proportional myoelectric controller with bilateral ankle exoskeletons. The adaptive gain was updated each stride such that on average the user’s peak muscle activity was mapped to maximal power output of the exoskeleton. All subjects participated in three identical training sessions where they walked on a treadmill for 50 minutes (30 minutes of which the exoskeleton was powered) at 1.2 ms(-1). We calculated and analyzed metabolic energy consumption, muscle recruitment, inverse kinematics, inverse dynamics, and exoskeleton mechanics. RESULTS: Using our controller, subjects achieved a metabolic reduction similar to that seen in previous work in about a third of the training time. The resulting controller gain was lower than that seen in previous work (β=1.50±0.14 versus a constant β=2). The adapted gain allowed users more total ankle joint power than that of unassisted walking, increasing ankle power in exchange for a decrease in hip power. CONCLUSIONS: Our findings indicate that humans prefer to walk with greater ankle mechanical power output than their unassisted gait when provided with an ankle exoskeleton using an adaptive controller. This suggests that robotic assistance from an exoskeleton can allow humans to adopt gait patterns different from their normal choices for locomotion. In our specific experiment, subjects increased ankle power and decreased hip power to walk with a reduction in metabolic cost. Future exoskeleton devices that rely on proportional myolectric control are likely to demonstrate improved performance by including an adaptive gain. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12984-015-0086-5) contains supplementary material, which is available to authorized users. BioMed Central 2015-11-04 /pmc/articles/PMC4634144/ /pubmed/26536868 http://dx.doi.org/10.1186/s12984-015-0086-5 Text en © Koller et al. 2015 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
Koller, Jeffrey R.
Jacobs, Daniel A.
Ferris, Daniel P.
Remy, C. David
Learning to walk with an adaptive gain proportional myoelectric controller for a robotic ankle exoskeleton
title Learning to walk with an adaptive gain proportional myoelectric controller for a robotic ankle exoskeleton
title_full Learning to walk with an adaptive gain proportional myoelectric controller for a robotic ankle exoskeleton
title_fullStr Learning to walk with an adaptive gain proportional myoelectric controller for a robotic ankle exoskeleton
title_full_unstemmed Learning to walk with an adaptive gain proportional myoelectric controller for a robotic ankle exoskeleton
title_short Learning to walk with an adaptive gain proportional myoelectric controller for a robotic ankle exoskeleton
title_sort learning to walk with an adaptive gain proportional myoelectric controller for a robotic ankle exoskeleton
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4634144/
https://www.ncbi.nlm.nih.gov/pubmed/26536868
http://dx.doi.org/10.1186/s12984-015-0086-5
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