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The Merits of Dynamic Data Acquisition for Realistic Myocontrol
Natural myocontrol is the intuitive control of a prosthetic limb via the user's voluntary muscular activations. This type of control is usually implemented by means of pattern recognition, which uses a set of training data to create a model that can decipher these muscular activations. A conseq...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7203421/ https://www.ncbi.nlm.nih.gov/pubmed/32426344 http://dx.doi.org/10.3389/fbioe.2020.00361 |
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author | Gigli, Andrea Gijsberts, Arjan Castellini, Claudio |
author_facet | Gigli, Andrea Gijsberts, Arjan Castellini, Claudio |
author_sort | Gigli, Andrea |
collection | PubMed |
description | Natural myocontrol is the intuitive control of a prosthetic limb via the user's voluntary muscular activations. This type of control is usually implemented by means of pattern recognition, which uses a set of training data to create a model that can decipher these muscular activations. A consequence of this approach is that the reliability of a myocontrol system depends on how representative this training data is for all types of signal variability that may be encountered when the amputee puts the prosthesis into real use. Myoelectric signals are indeed known to vary according to the position and orientation of the limb, among other factors, which is why it has become common practice to take this variability into account by acquiring training data in multiple body postures. To shed further light on this problem, we compare two ways of collecting data: while the subjects hold their limb statically in several positions one at a time, which is the traditional way, or while they dynamically move their limb at a constant pace through those same positions. Since our interest is to investigate any differences when controlling an actual prosthetic device, we defined an evaluation protocol that consisted of a series of complex, bimanual daily-living tasks. Fourteen intact participants performed these tasks while wearing prosthetic hands mounted on splints, which were controlled via either a statically or dynamically built myocontrol model. In both cases all subjects managed to complete all tasks and participants without previous experience in myoelectric control manifested a significant learning effect; moreover, there was no significant difference in the task completion times achieved with either model. When evaluated in a simulated scenario with traditional offline performance evaluation, on the other hand, the dynamically-trained system showed significantly better accuracy. Regardless of the setting, the dynamic data acquisition was faster, less tiresome, and better accepted by the users. We conclude that dynamic data acquisition is advantageous and confirm the limited relevance of offline analyses for online myocontrol performance. |
format | Online Article Text |
id | pubmed-7203421 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-72034212020-05-18 The Merits of Dynamic Data Acquisition for Realistic Myocontrol Gigli, Andrea Gijsberts, Arjan Castellini, Claudio Front Bioeng Biotechnol Bioengineering and Biotechnology Natural myocontrol is the intuitive control of a prosthetic limb via the user's voluntary muscular activations. This type of control is usually implemented by means of pattern recognition, which uses a set of training data to create a model that can decipher these muscular activations. A consequence of this approach is that the reliability of a myocontrol system depends on how representative this training data is for all types of signal variability that may be encountered when the amputee puts the prosthesis into real use. Myoelectric signals are indeed known to vary according to the position and orientation of the limb, among other factors, which is why it has become common practice to take this variability into account by acquiring training data in multiple body postures. To shed further light on this problem, we compare two ways of collecting data: while the subjects hold their limb statically in several positions one at a time, which is the traditional way, or while they dynamically move their limb at a constant pace through those same positions. Since our interest is to investigate any differences when controlling an actual prosthetic device, we defined an evaluation protocol that consisted of a series of complex, bimanual daily-living tasks. Fourteen intact participants performed these tasks while wearing prosthetic hands mounted on splints, which were controlled via either a statically or dynamically built myocontrol model. In both cases all subjects managed to complete all tasks and participants without previous experience in myoelectric control manifested a significant learning effect; moreover, there was no significant difference in the task completion times achieved with either model. When evaluated in a simulated scenario with traditional offline performance evaluation, on the other hand, the dynamically-trained system showed significantly better accuracy. Regardless of the setting, the dynamic data acquisition was faster, less tiresome, and better accepted by the users. We conclude that dynamic data acquisition is advantageous and confirm the limited relevance of offline analyses for online myocontrol performance. Frontiers Media S.A. 2020-04-30 /pmc/articles/PMC7203421/ /pubmed/32426344 http://dx.doi.org/10.3389/fbioe.2020.00361 Text en Copyright © 2020 Gigli, Gijsberts and Castellini. 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) 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 Gigli, Andrea Gijsberts, Arjan Castellini, Claudio The Merits of Dynamic Data Acquisition for Realistic Myocontrol |
title | The Merits of Dynamic Data Acquisition for Realistic Myocontrol |
title_full | The Merits of Dynamic Data Acquisition for Realistic Myocontrol |
title_fullStr | The Merits of Dynamic Data Acquisition for Realistic Myocontrol |
title_full_unstemmed | The Merits of Dynamic Data Acquisition for Realistic Myocontrol |
title_short | The Merits of Dynamic Data Acquisition for Realistic Myocontrol |
title_sort | merits of dynamic data acquisition for realistic myocontrol |
topic | Bioengineering and Biotechnology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7203421/ https://www.ncbi.nlm.nih.gov/pubmed/32426344 http://dx.doi.org/10.3389/fbioe.2020.00361 |
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