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Electromyography data for non-invasive naturally-controlled robotic hand prostheses
Recent advances in rehabilitation robotics suggest that it may be possible for hand-amputated subjects to recover at least a significant part of the lost hand functionality. The control of robotic prosthetic hands using non-invasive techniques is still a challenge in real life: myoelectric prosthese...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4421935/ https://www.ncbi.nlm.nih.gov/pubmed/25977804 http://dx.doi.org/10.1038/sdata.2014.53 |
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author | Atzori, Manfredo Gijsberts, Arjan Castellini, Claudio Caputo, Barbara Hager, Anne-Gabrielle Mittaz Elsig, Simone Giatsidis, Giorgio Bassetto, Franco Müller, Henning |
author_facet | Atzori, Manfredo Gijsberts, Arjan Castellini, Claudio Caputo, Barbara Hager, Anne-Gabrielle Mittaz Elsig, Simone Giatsidis, Giorgio Bassetto, Franco Müller, Henning |
author_sort | Atzori, Manfredo |
collection | PubMed |
description | Recent advances in rehabilitation robotics suggest that it may be possible for hand-amputated subjects to recover at least a significant part of the lost hand functionality. The control of robotic prosthetic hands using non-invasive techniques is still a challenge in real life: myoelectric prostheses give limited control capabilities, the control is often unnatural and must be learned through long training times. Meanwhile, scientific literature results are promising but they are still far from fulfilling real-life needs. This work aims to close this gap by allowing worldwide research groups to develop and test movement recognition and force control algorithms on a benchmark scientific database. The database is targeted at studying the relationship between surface electromyography, hand kinematics and hand forces, with the final goal of developing non-invasive, naturally controlled, robotic hand prostheses. The validation section verifies that the data are similar to data acquired in real-life conditions, and that recognition of different hand tasks by applying state-of-the-art signal features and machine-learning algorithms is possible. |
format | Online Article Text |
id | pubmed-4421935 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-44219352015-05-14 Electromyography data for non-invasive naturally-controlled robotic hand prostheses Atzori, Manfredo Gijsberts, Arjan Castellini, Claudio Caputo, Barbara Hager, Anne-Gabrielle Mittaz Elsig, Simone Giatsidis, Giorgio Bassetto, Franco Müller, Henning Sci Data Data Descriptor Recent advances in rehabilitation robotics suggest that it may be possible for hand-amputated subjects to recover at least a significant part of the lost hand functionality. The control of robotic prosthetic hands using non-invasive techniques is still a challenge in real life: myoelectric prostheses give limited control capabilities, the control is often unnatural and must be learned through long training times. Meanwhile, scientific literature results are promising but they are still far from fulfilling real-life needs. This work aims to close this gap by allowing worldwide research groups to develop and test movement recognition and force control algorithms on a benchmark scientific database. The database is targeted at studying the relationship between surface electromyography, hand kinematics and hand forces, with the final goal of developing non-invasive, naturally controlled, robotic hand prostheses. The validation section verifies that the data are similar to data acquired in real-life conditions, and that recognition of different hand tasks by applying state-of-the-art signal features and machine-learning algorithms is possible. Nature Publishing Group 2014-12-23 /pmc/articles/PMC4421935/ /pubmed/25977804 http://dx.doi.org/10.1038/sdata.2014.53 Text en Copyright © 2014, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0 This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0 Metadata associated with this Data Descriptor is available at http://www.nature.com/sdata/ and is released under the CC0 waiver to maximize reuse. |
spellingShingle | Data Descriptor Atzori, Manfredo Gijsberts, Arjan Castellini, Claudio Caputo, Barbara Hager, Anne-Gabrielle Mittaz Elsig, Simone Giatsidis, Giorgio Bassetto, Franco Müller, Henning Electromyography data for non-invasive naturally-controlled robotic hand prostheses |
title | Electromyography data for non-invasive naturally-controlled robotic hand prostheses |
title_full | Electromyography data for non-invasive naturally-controlled robotic hand prostheses |
title_fullStr | Electromyography data for non-invasive naturally-controlled robotic hand prostheses |
title_full_unstemmed | Electromyography data for non-invasive naturally-controlled robotic hand prostheses |
title_short | Electromyography data for non-invasive naturally-controlled robotic hand prostheses |
title_sort | electromyography data for non-invasive naturally-controlled robotic hand prostheses |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4421935/ https://www.ncbi.nlm.nih.gov/pubmed/25977804 http://dx.doi.org/10.1038/sdata.2014.53 |
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