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A dynamical model improves reconstruction of handwriting from multichannel electromyographic recordings
In recent years, several assistive devices have been proposed to reconstruct arm and hand movements from electromyographic (EMG) activity. Although simple to implement and potentially useful to augment many functions, such myoelectric devices still need improvement before they become practical. Here...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4624865/ https://www.ncbi.nlm.nih.gov/pubmed/26578856 http://dx.doi.org/10.3389/fnins.2015.00389 |
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author | Okorokova, Elizaveta Lebedev, Mikhail Linderman, Michael Ossadtchi, Alex |
author_facet | Okorokova, Elizaveta Lebedev, Mikhail Linderman, Michael Ossadtchi, Alex |
author_sort | Okorokova, Elizaveta |
collection | PubMed |
description | In recent years, several assistive devices have been proposed to reconstruct arm and hand movements from electromyographic (EMG) activity. Although simple to implement and potentially useful to augment many functions, such myoelectric devices still need improvement before they become practical. Here we considered the problem of reconstruction of handwriting from multichannel EMG activity. Previously, linear regression methods (e.g., the Wiener filter) have been utilized for this purpose with some success. To improve reconstruction accuracy, we implemented the Kalman filter, which allows to fuse two information sources: the physical characteristics of handwriting and the activity of the leading hand muscles, registered by the EMG. Applying the Kalman filter, we were able to convert eight channels of EMG activity recorded from the forearm and the hand muscles into smooth reconstructions of handwritten traces. The filter operates in a causal manner and acts as a true predictor utilizing the EMGs from the past only, which makes the approach suitable for real-time operations. Our algorithm is appropriate for clinical neuroprosthetic applications and computer peripherals. Moreover, it is applicable to a broader class of tasks where predictive myoelectric control is needed. |
format | Online Article Text |
id | pubmed-4624865 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-46248652015-11-17 A dynamical model improves reconstruction of handwriting from multichannel electromyographic recordings Okorokova, Elizaveta Lebedev, Mikhail Linderman, Michael Ossadtchi, Alex Front Neurosci Neuroscience In recent years, several assistive devices have been proposed to reconstruct arm and hand movements from electromyographic (EMG) activity. Although simple to implement and potentially useful to augment many functions, such myoelectric devices still need improvement before they become practical. Here we considered the problem of reconstruction of handwriting from multichannel EMG activity. Previously, linear regression methods (e.g., the Wiener filter) have been utilized for this purpose with some success. To improve reconstruction accuracy, we implemented the Kalman filter, which allows to fuse two information sources: the physical characteristics of handwriting and the activity of the leading hand muscles, registered by the EMG. Applying the Kalman filter, we were able to convert eight channels of EMG activity recorded from the forearm and the hand muscles into smooth reconstructions of handwritten traces. The filter operates in a causal manner and acts as a true predictor utilizing the EMGs from the past only, which makes the approach suitable for real-time operations. Our algorithm is appropriate for clinical neuroprosthetic applications and computer peripherals. Moreover, it is applicable to a broader class of tasks where predictive myoelectric control is needed. Frontiers Media S.A. 2015-10-29 /pmc/articles/PMC4624865/ /pubmed/26578856 http://dx.doi.org/10.3389/fnins.2015.00389 Text en Copyright © 2015 Okorokova, Lebedev, Linderman and Ossadtchi. http://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) or licensor 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 | Neuroscience Okorokova, Elizaveta Lebedev, Mikhail Linderman, Michael Ossadtchi, Alex A dynamical model improves reconstruction of handwriting from multichannel electromyographic recordings |
title | A dynamical model improves reconstruction of handwriting from multichannel electromyographic recordings |
title_full | A dynamical model improves reconstruction of handwriting from multichannel electromyographic recordings |
title_fullStr | A dynamical model improves reconstruction of handwriting from multichannel electromyographic recordings |
title_full_unstemmed | A dynamical model improves reconstruction of handwriting from multichannel electromyographic recordings |
title_short | A dynamical model improves reconstruction of handwriting from multichannel electromyographic recordings |
title_sort | dynamical model improves reconstruction of handwriting from multichannel electromyographic recordings |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4624865/ https://www.ncbi.nlm.nih.gov/pubmed/26578856 http://dx.doi.org/10.3389/fnins.2015.00389 |
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