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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/66737
Ver los metadatos del registro completo
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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) |
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openAccess |
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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 |
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reponame:SEDICI (UNLP) instname:Universidad Nacional de La Plata instacron:UNLP |
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SEDICI (UNLP) - Universidad Nacional de La Plata |
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