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Gaze, visual, myoelectric, and inertial data of grasps for intelligent prosthetics

A hand amputation is a highly disabling event, having severe physical and psychological repercussions on a person’s life. Despite extensive efforts devoted to restoring the missing functionality via dexterous myoelectric hand prostheses, natural and robust control usable in everyday life is still ch...

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Autores principales: Cognolato, Matteo, Gijsberts, Arjan, Gregori, Valentina, Saetta, Gianluca, Giacomino, Katia, Hager, Anne-Gabrielle Mittaz, Gigli, Andrea, Faccio, Diego, Tiengo, Cesare, Bassetto, Franco, Caputo, Barbara, Brugger, Peter, Atzori, Manfredo, Müller, Henning
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7010656/
https://www.ncbi.nlm.nih.gov/pubmed/32041965
http://dx.doi.org/10.1038/s41597-020-0380-3
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author Cognolato, Matteo
Gijsberts, Arjan
Gregori, Valentina
Saetta, Gianluca
Giacomino, Katia
Hager, Anne-Gabrielle Mittaz
Gigli, Andrea
Faccio, Diego
Tiengo, Cesare
Bassetto, Franco
Caputo, Barbara
Brugger, Peter
Atzori, Manfredo
Müller, Henning
author_facet Cognolato, Matteo
Gijsberts, Arjan
Gregori, Valentina
Saetta, Gianluca
Giacomino, Katia
Hager, Anne-Gabrielle Mittaz
Gigli, Andrea
Faccio, Diego
Tiengo, Cesare
Bassetto, Franco
Caputo, Barbara
Brugger, Peter
Atzori, Manfredo
Müller, Henning
author_sort Cognolato, Matteo
collection PubMed
description A hand amputation is a highly disabling event, having severe physical and psychological repercussions on a person’s life. Despite extensive efforts devoted to restoring the missing functionality via dexterous myoelectric hand prostheses, natural and robust control usable in everyday life is still challenging. Novel techniques have been proposed to overcome the current limitations, among them the fusion of surface electromyography with other sources of contextual information. We present a dataset to investigate the inclusion of eye tracking and first person video to provide more stable intent recognition for prosthetic control. This multimodal dataset contains surface electromyography and accelerometry of the forearm, and gaze, first person video, and inertial measurements of the head recorded from 15 transradial amputees and 30 able-bodied subjects performing grasping tasks. Besides the intended application for upper-limb prosthetics, we also foresee uses for this dataset to study eye-hand coordination in the context of psychophysics, neuroscience, and assistive robotics.
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spelling pubmed-70106562020-02-11 Gaze, visual, myoelectric, and inertial data of grasps for intelligent prosthetics Cognolato, Matteo Gijsberts, Arjan Gregori, Valentina Saetta, Gianluca Giacomino, Katia Hager, Anne-Gabrielle Mittaz Gigli, Andrea Faccio, Diego Tiengo, Cesare Bassetto, Franco Caputo, Barbara Brugger, Peter Atzori, Manfredo Müller, Henning Sci Data Data Descriptor A hand amputation is a highly disabling event, having severe physical and psychological repercussions on a person’s life. Despite extensive efforts devoted to restoring the missing functionality via dexterous myoelectric hand prostheses, natural and robust control usable in everyday life is still challenging. Novel techniques have been proposed to overcome the current limitations, among them the fusion of surface electromyography with other sources of contextual information. We present a dataset to investigate the inclusion of eye tracking and first person video to provide more stable intent recognition for prosthetic control. This multimodal dataset contains surface electromyography and accelerometry of the forearm, and gaze, first person video, and inertial measurements of the head recorded from 15 transradial amputees and 30 able-bodied subjects performing grasping tasks. Besides the intended application for upper-limb prosthetics, we also foresee uses for this dataset to study eye-hand coordination in the context of psychophysics, neuroscience, and assistive robotics. Nature Publishing Group UK 2020-02-10 /pmc/articles/PMC7010656/ /pubmed/32041965 http://dx.doi.org/10.1038/s41597-020-0380-3 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files associated with this article.
spellingShingle Data Descriptor
Cognolato, Matteo
Gijsberts, Arjan
Gregori, Valentina
Saetta, Gianluca
Giacomino, Katia
Hager, Anne-Gabrielle Mittaz
Gigli, Andrea
Faccio, Diego
Tiengo, Cesare
Bassetto, Franco
Caputo, Barbara
Brugger, Peter
Atzori, Manfredo
Müller, Henning
Gaze, visual, myoelectric, and inertial data of grasps for intelligent prosthetics
title Gaze, visual, myoelectric, and inertial data of grasps for intelligent prosthetics
title_full Gaze, visual, myoelectric, and inertial data of grasps for intelligent prosthetics
title_fullStr Gaze, visual, myoelectric, and inertial data of grasps for intelligent prosthetics
title_full_unstemmed Gaze, visual, myoelectric, and inertial data of grasps for intelligent prosthetics
title_short Gaze, visual, myoelectric, and inertial data of grasps for intelligent prosthetics
title_sort gaze, visual, myoelectric, and inertial data of grasps for intelligent prosthetics
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7010656/
https://www.ncbi.nlm.nih.gov/pubmed/32041965
http://dx.doi.org/10.1038/s41597-020-0380-3
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