Algorithm of Myoelectric Signals Processing for the Control of Prosthetic Robotic Hands

Autores
Russo, Rodrigo E.; Fernández, Juana; Rivera, Raúl; Kuzman, Melisa G.; López, Juan; Gemin, Walter; Revuelta, Miguel Ángel
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The development of robotic hand prosthetic aims to give back people with disabilities, the ability to recover the functionality needed to manipulate the objects of their daily environment. The electrical signals sent by the brain through the nervous system are associated with the type of movement that the limbs must execute. Myoelectric sensors are non-intrusive devices that allow the capture of electrical signals from the peripheral nervous system. The relationship between the signals originated in the brain tending to generate an action and the myoelectric ones as a result of them, are weakly correlated. For this reason, it is necessary to study their interaction in order to develop the algorithms that allow recognizing orders and transform them into commands that activate the corresponding movements of the prosthesis. The present work shows the development of a prosthesis based on the design of an artificial hand Open Bionics to produce the movements, the MyoWare Muscle sensor for the capture of myoelectric signals (EMG) and the algorithm that allows to identify orders associated with three types of movement. Arduino Nano module performs the acquisition and control processes to meet the size and consumption requirements of this application.
Facultad de Informática
Materia
Ciencias Informáticas
Robotics
EMG
prosthesis
Arduino
hand
bionic
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/66737

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spelling Algorithm of Myoelectric Signals Processing for the Control of Prosthetic Robotic HandsRusso, Rodrigo E.Fernández, JuanaRivera, RaúlKuzman, Melisa G.López, JuanGemin, WalterRevuelta, Miguel ÁngelCiencias InformáticasRoboticsEMGprosthesisArduinohandbionicThe development of robotic hand prosthetic aims to give back people with disabilities, the ability to recover the functionality needed to manipulate the objects of their daily environment. The electrical signals sent by the brain through the nervous system are associated with the type of movement that the limbs must execute. Myoelectric sensors are non-intrusive devices that allow the capture of electrical signals from the peripheral nervous system. The relationship between the signals originated in the brain tending to generate an action and the myoelectric ones as a result of them, are weakly correlated. For this reason, it is necessary to study their interaction in order to develop the algorithms that allow recognizing orders and transform them into commands that activate the corresponding movements of the prosthesis. The present work shows the development of a prosthesis based on the design of an artificial hand Open Bionics to produce the movements, the MyoWare Muscle sensor for the capture of myoelectric signals (EMG) and the algorithm that allows to identify orders associated with three types of movement. Arduino Nano module performs the acquisition and control processes to meet the size and consumption requirements of this application.Facultad de Informática2018-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf28-34http://sedici.unlp.edu.ar/handle/10915/66737enginfo:eu-repo/semantics/altIdentifier/issn/1666-6038info:eu-repo/semantics/altIdentifier/doi/10.24215/16666038.18.e04info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc/4.0/Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-10-15T11:02:03Zoai:sedici.unlp.edu.ar:10915/66737Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-15 11:02:04.155SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Algorithm of Myoelectric Signals Processing for the Control of Prosthetic Robotic Hands
title Algorithm of Myoelectric Signals Processing for the Control of Prosthetic Robotic Hands
spellingShingle Algorithm of Myoelectric Signals Processing for the Control of Prosthetic Robotic Hands
Russo, Rodrigo E.
Ciencias Informáticas
Robotics
EMG
prosthesis
Arduino
hand
bionic
title_short Algorithm of Myoelectric Signals Processing for the Control of Prosthetic Robotic Hands
title_full Algorithm of Myoelectric Signals Processing for the Control of Prosthetic Robotic Hands
title_fullStr Algorithm of Myoelectric Signals Processing for the Control of Prosthetic Robotic Hands
title_full_unstemmed Algorithm of Myoelectric Signals Processing for the Control of Prosthetic Robotic Hands
title_sort Algorithm of Myoelectric Signals Processing for the Control of Prosthetic Robotic Hands
dc.creator.none.fl_str_mv Russo, Rodrigo E.
Fernández, Juana
Rivera, Raúl
Kuzman, Melisa G.
López, Juan
Gemin, Walter
Revuelta, Miguel Ángel
author Russo, Rodrigo E.
author_facet Russo, Rodrigo E.
Fernández, Juana
Rivera, Raúl
Kuzman, Melisa G.
López, Juan
Gemin, Walter
Revuelta, Miguel Ángel
author_role author
author2 Fernández, Juana
Rivera, Raúl
Kuzman, Melisa G.
López, Juan
Gemin, Walter
Revuelta, Miguel Ángel
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Robotics
EMG
prosthesis
Arduino
hand
bionic
topic Ciencias Informáticas
Robotics
EMG
prosthesis
Arduino
hand
bionic
dc.description.none.fl_txt_mv The development of robotic hand prosthetic aims to give back people with disabilities, the ability to recover the functionality needed to manipulate the objects of their daily environment. The electrical signals sent by the brain through the nervous system are associated with the type of movement that the limbs must execute. Myoelectric sensors are non-intrusive devices that allow the capture of electrical signals from the peripheral nervous system. The relationship between the signals originated in the brain tending to generate an action and the myoelectric ones as a result of them, are weakly correlated. For this reason, it is necessary to study their interaction in order to develop the algorithms that allow recognizing orders and transform them into commands that activate the corresponding movements of the prosthesis. The present work shows the development of a prosthesis based on the design of an artificial hand Open Bionics to produce the movements, the MyoWare Muscle sensor for the capture of myoelectric signals (EMG) and the algorithm that allows to identify orders associated with three types of movement. Arduino Nano module performs the acquisition and control processes to meet the size and consumption requirements of this application.
Facultad de Informática
description The development of robotic hand prosthetic aims to give back people with disabilities, the ability to recover the functionality needed to manipulate the objects of their daily environment. The electrical signals sent by the brain through the nervous system are associated with the type of movement that the limbs must execute. Myoelectric sensors are non-intrusive devices that allow the capture of electrical signals from the peripheral nervous system. The relationship between the signals originated in the brain tending to generate an action and the myoelectric ones as a result of them, are weakly correlated. For this reason, it is necessary to study their interaction in order to develop the algorithms that allow recognizing orders and transform them into commands that activate the corresponding movements of the prosthesis. The present work shows the development of a prosthesis based on the design of an artificial hand Open Bionics to produce the movements, the MyoWare Muscle sensor for the capture of myoelectric signals (EMG) and the algorithm that allows to identify orders associated with three types of movement. Arduino Nano module performs the acquisition and control processes to meet the size and consumption requirements of this application.
publishDate 2018
dc.date.none.fl_str_mv 2018-04
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Articulo
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/66737
url http://sedici.unlp.edu.ar/handle/10915/66737
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/issn/1666-6038
info:eu-repo/semantics/altIdentifier/doi/10.24215/16666038.18.e04
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc/4.0/
Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc/4.0/
Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
dc.format.none.fl_str_mv application/pdf
28-34
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
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institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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