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Autoencoder-based myoelectric controller for prosthetic hands

In the past, linear dimensionality-reduction techniques, such as Principal Component Analysis, have been used to simplify the myoelectric control of high-dimensional prosthetic hands. Nonetheless, their nonlinear counterparts, such as Autoencoders, have been shown to be more effective at compressing...

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Autores principales: Portnova-Fahreeva, Alexandra A., Rizzoglio, Fabio, Mussa-Ivaldi, Ferdinando A., Rombokas, Eric
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10331017/
https://www.ncbi.nlm.nih.gov/pubmed/37434753
http://dx.doi.org/10.3389/fbioe.2023.1134135
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author Portnova-Fahreeva, Alexandra A.
Rizzoglio, Fabio
Mussa-Ivaldi, Ferdinando A.
Rombokas, Eric
author_facet Portnova-Fahreeva, Alexandra A.
Rizzoglio, Fabio
Mussa-Ivaldi, Ferdinando A.
Rombokas, Eric
author_sort Portnova-Fahreeva, Alexandra A.
collection PubMed
description In the past, linear dimensionality-reduction techniques, such as Principal Component Analysis, have been used to simplify the myoelectric control of high-dimensional prosthetic hands. Nonetheless, their nonlinear counterparts, such as Autoencoders, have been shown to be more effective at compressing and reconstructing complex hand kinematics data. As a result, they have a potential of being a more accurate tool for prosthetic hand control. Here, we present a novel Autoencoder-based controller, in which the user is able to control a high-dimensional (17D) virtual hand via a low-dimensional (2D) space. We assess the efficacy of the controller via a validation experiment with four unimpaired participants. All the participants were able to significantly decrease the time it took for them to match a target gesture with a virtual hand to an average of [Formula: see text] and three out of four participants significantly improved path efficiency. Our results suggest that the Autoencoder-based controller has the potential to be used to manipulate high-dimensional hand systems via a myoelectric interface with a higher accuracy than PCA; however, more exploration needs to be done on the most effective ways of learning such a controller.
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spelling pubmed-103310172023-07-11 Autoencoder-based myoelectric controller for prosthetic hands Portnova-Fahreeva, Alexandra A. Rizzoglio, Fabio Mussa-Ivaldi, Ferdinando A. Rombokas, Eric Front Bioeng Biotechnol Bioengineering and Biotechnology In the past, linear dimensionality-reduction techniques, such as Principal Component Analysis, have been used to simplify the myoelectric control of high-dimensional prosthetic hands. Nonetheless, their nonlinear counterparts, such as Autoencoders, have been shown to be more effective at compressing and reconstructing complex hand kinematics data. As a result, they have a potential of being a more accurate tool for prosthetic hand control. Here, we present a novel Autoencoder-based controller, in which the user is able to control a high-dimensional (17D) virtual hand via a low-dimensional (2D) space. We assess the efficacy of the controller via a validation experiment with four unimpaired participants. All the participants were able to significantly decrease the time it took for them to match a target gesture with a virtual hand to an average of [Formula: see text] and three out of four participants significantly improved path efficiency. Our results suggest that the Autoencoder-based controller has the potential to be used to manipulate high-dimensional hand systems via a myoelectric interface with a higher accuracy than PCA; however, more exploration needs to be done on the most effective ways of learning such a controller. Frontiers Media S.A. 2023-06-26 /pmc/articles/PMC10331017/ /pubmed/37434753 http://dx.doi.org/10.3389/fbioe.2023.1134135 Text en Copyright © 2023 Portnova-Fahreeva, Rizzoglio, Mussa-Ivaldi and Rombokas. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Bioengineering and Biotechnology
Portnova-Fahreeva, Alexandra A.
Rizzoglio, Fabio
Mussa-Ivaldi, Ferdinando A.
Rombokas, Eric
Autoencoder-based myoelectric controller for prosthetic hands
title Autoencoder-based myoelectric controller for prosthetic hands
title_full Autoencoder-based myoelectric controller for prosthetic hands
title_fullStr Autoencoder-based myoelectric controller for prosthetic hands
title_full_unstemmed Autoencoder-based myoelectric controller for prosthetic hands
title_short Autoencoder-based myoelectric controller for prosthetic hands
title_sort autoencoder-based myoelectric controller for prosthetic hands
topic Bioengineering and Biotechnology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10331017/
https://www.ncbi.nlm.nih.gov/pubmed/37434753
http://dx.doi.org/10.3389/fbioe.2023.1134135
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