Control of a robotic knee exoskeleton for assistance and rehabilitation based on motion intention from sEMG

Autores
Villa Parra, Ana Cecilia; Delisle Rodriguez, Denis; Botelho, Thomaz; Mayor, John Jairo Villarejo; Delis, Alberto López; Carelli Albarracin, Ricardo Oscar; Neto, Anselmo Frizera; Bastos, Teodiano Freire
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Introduction: This work presents the development of a novel robotic knee exoskeleton controlled by motion intention based on sEMG, which uses admittance control to assist people with reduced mobility and improve their locomotion. Clinical research remark that these devices working in constant interaction with the neuromuscular and skeletal human system improves functional compensation and rehabilitation. Hence, the users become an active part of the training/rehabilitation, facilitating their involvement and improving their neural plasticity. For recognition of the lower-limb motion intention and discrimination of knee movements, sEMG from both lower-limb and trunk are used, which implies a new approach to control robotic assistive devices. Methods: A control system that includes a stage for human-motion intention recognition (HMIR), based on techniques to classify motion classes related to knee joint were developed. For translation of the user’s intention to a desired state for the robotic knee exoskeleton, the system also includes a finite state machine and admittance, velocity and trajectory controllers with a function that allows stopping the movement according to the users intention. Results: The proposed HMIR showed an accuracy between 76% to 83% for lower-limb muscles, and 71% to 77% for trunk muscles to classify motor classes of lower-limb movements. Experimental results of the controller showed that the admittance controller proposed here offers knee support in 50% of the gait cycle and assists correctly the motion classes. Conclusion: The robotic knee exoskeleton introduced here is an alternative method to empower knee movements using sEMG signals from lower-limb and trunk muscles.
Fil: Villa Parra, Ana Cecilia. Universidade Federal do Espírito Santo; Brasil. Universidad Politécnica Salesiana; Ecuador
Fil: Delisle Rodriguez, Denis. Universidade Federal do Espírito Santo; Brasil. Universidad de Oriente; Cuba
Fil: Botelho, Thomaz. Universidade Federal do Espírito Santo; Brasil
Fil: Mayor, John Jairo Villarejo. Universidade Federal do Paraná; Brasil
Fil: Delis, Alberto López. Universidad de Oriente; Cuba
Fil: Carelli Albarracin, Ricardo Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
Fil: Neto, Anselmo Frizera. Universidade Federal do Espírito Santo; Brasil
Fil: Bastos, Teodiano Freire. Universidade Federal do Espírito Santo; Brasil
Materia
ADMITTANCE CONTROL
ELECTROMYOGRAPHY
ROBOTIC KNEE EXOSKELETON
TRUNK MUSCLES
USER INTENTION RECOGNITION
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/89234
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/89234
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Control of a robotic knee exoskeleton for assistance and rehabilitation based on motion intention from sEMGVilla Parra, Ana CeciliaDelisle Rodriguez, DenisBotelho, ThomazMayor, John Jairo VillarejoDelis, Alberto LópezCarelli Albarracin, Ricardo OscarNeto, Anselmo FrizeraBastos, Teodiano FreireADMITTANCE CONTROLELECTROMYOGRAPHYROBOTIC KNEE EXOSKELETONTRUNK MUSCLESUSER INTENTION RECOGNITIONhttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2Introduction: This work presents the development of a novel robotic knee exoskeleton controlled by motion intention based on sEMG, which uses admittance control to assist people with reduced mobility and improve their locomotion. Clinical research remark that these devices working in constant interaction with the neuromuscular and skeletal human system improves functional compensation and rehabilitation. Hence, the users become an active part of the training/rehabilitation, facilitating their involvement and improving their neural plasticity. For recognition of the lower-limb motion intention and discrimination of knee movements, sEMG from both lower-limb and trunk are used, which implies a new approach to control robotic assistive devices. Methods: A control system that includes a stage for human-motion intention recognition (HMIR), based on techniques to classify motion classes related to knee joint were developed. For translation of the user’s intention to a desired state for the robotic knee exoskeleton, the system also includes a finite state machine and admittance, velocity and trajectory controllers with a function that allows stopping the movement according to the users intention. Results: The proposed HMIR showed an accuracy between 76% to 83% for lower-limb muscles, and 71% to 77% for trunk muscles to classify motor classes of lower-limb movements. Experimental results of the controller showed that the admittance controller proposed here offers knee support in 50% of the gait cycle and assists correctly the motion classes. Conclusion: The robotic knee exoskeleton introduced here is an alternative method to empower knee movements using sEMG signals from lower-limb and trunk muscles.Fil: Villa Parra, Ana Cecilia. Universidade Federal do Espírito Santo; Brasil. Universidad Politécnica Salesiana; EcuadorFil: Delisle Rodriguez, Denis. Universidade Federal do Espírito Santo; Brasil. Universidad de Oriente; CubaFil: Botelho, Thomaz. Universidade Federal do Espírito Santo; BrasilFil: Mayor, John Jairo Villarejo. Universidade Federal do Paraná; BrasilFil: Delis, Alberto López. Universidad de Oriente; CubaFil: Carelli Albarracin, Ricardo Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Neto, Anselmo Frizera. Universidade Federal do Espírito Santo; BrasilFil: Bastos, Teodiano Freire. Universidade Federal do Espírito Santo; BrasilSociedade Brasileira de Engenharia Biomédica2018-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/89234Villa Parra, Ana Cecilia; Delisle Rodriguez, Denis; Botelho, Thomaz; Mayor, John Jairo Villarejo; Delis, Alberto López; et al.; Control of a robotic knee exoskeleton for assistance and rehabilitation based on motion intention from sEMG; Sociedade Brasileira de Engenharia Biomédica; Research on Biomedical Engineering; 34; 3; 9-2018; 198-2102446-47322446-4740CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1590/2446-4740.07417info:eu-repo/semantics/altIdentifier/url/http://ref.scielo.org/p4hpq5info:eu-repo/semantics/altIdentifier/url/https://www.rbejournal.org/article/doi/10.1590/2446-4740.07417info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:38:32Zoai:ri.conicet.gov.ar:11336/89234instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:38:32.501CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Control of a robotic knee exoskeleton for assistance and rehabilitation based on motion intention from sEMG
title Control of a robotic knee exoskeleton for assistance and rehabilitation based on motion intention from sEMG
spellingShingle Control of a robotic knee exoskeleton for assistance and rehabilitation based on motion intention from sEMG
Villa Parra, Ana Cecilia
ADMITTANCE CONTROL
ELECTROMYOGRAPHY
ROBOTIC KNEE EXOSKELETON
TRUNK MUSCLES
USER INTENTION RECOGNITION
title_short Control of a robotic knee exoskeleton for assistance and rehabilitation based on motion intention from sEMG
title_full Control of a robotic knee exoskeleton for assistance and rehabilitation based on motion intention from sEMG
title_fullStr Control of a robotic knee exoskeleton for assistance and rehabilitation based on motion intention from sEMG
title_full_unstemmed Control of a robotic knee exoskeleton for assistance and rehabilitation based on motion intention from sEMG
title_sort Control of a robotic knee exoskeleton for assistance and rehabilitation based on motion intention from sEMG
dc.creator.none.fl_str_mv Villa Parra, Ana Cecilia
Delisle Rodriguez, Denis
Botelho, Thomaz
Mayor, John Jairo Villarejo
Delis, Alberto López
Carelli Albarracin, Ricardo Oscar
Neto, Anselmo Frizera
Bastos, Teodiano Freire
author Villa Parra, Ana Cecilia
author_facet Villa Parra, Ana Cecilia
Delisle Rodriguez, Denis
Botelho, Thomaz
Mayor, John Jairo Villarejo
Delis, Alberto López
Carelli Albarracin, Ricardo Oscar
Neto, Anselmo Frizera
Bastos, Teodiano Freire
author_role author
author2 Delisle Rodriguez, Denis
Botelho, Thomaz
Mayor, John Jairo Villarejo
Delis, Alberto López
Carelli Albarracin, Ricardo Oscar
Neto, Anselmo Frizera
Bastos, Teodiano Freire
author2_role author
author
author
author
author
author
author
dc.subject.none.fl_str_mv ADMITTANCE CONTROL
ELECTROMYOGRAPHY
ROBOTIC KNEE EXOSKELETON
TRUNK MUSCLES
USER INTENTION RECOGNITION
topic ADMITTANCE CONTROL
ELECTROMYOGRAPHY
ROBOTIC KNEE EXOSKELETON
TRUNK MUSCLES
USER INTENTION RECOGNITION
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Introduction: This work presents the development of a novel robotic knee exoskeleton controlled by motion intention based on sEMG, which uses admittance control to assist people with reduced mobility and improve their locomotion. Clinical research remark that these devices working in constant interaction with the neuromuscular and skeletal human system improves functional compensation and rehabilitation. Hence, the users become an active part of the training/rehabilitation, facilitating their involvement and improving their neural plasticity. For recognition of the lower-limb motion intention and discrimination of knee movements, sEMG from both lower-limb and trunk are used, which implies a new approach to control robotic assistive devices. Methods: A control system that includes a stage for human-motion intention recognition (HMIR), based on techniques to classify motion classes related to knee joint were developed. For translation of the user’s intention to a desired state for the robotic knee exoskeleton, the system also includes a finite state machine and admittance, velocity and trajectory controllers with a function that allows stopping the movement according to the users intention. Results: The proposed HMIR showed an accuracy between 76% to 83% for lower-limb muscles, and 71% to 77% for trunk muscles to classify motor classes of lower-limb movements. Experimental results of the controller showed that the admittance controller proposed here offers knee support in 50% of the gait cycle and assists correctly the motion classes. Conclusion: The robotic knee exoskeleton introduced here is an alternative method to empower knee movements using sEMG signals from lower-limb and trunk muscles.
Fil: Villa Parra, Ana Cecilia. Universidade Federal do Espírito Santo; Brasil. Universidad Politécnica Salesiana; Ecuador
Fil: Delisle Rodriguez, Denis. Universidade Federal do Espírito Santo; Brasil. Universidad de Oriente; Cuba
Fil: Botelho, Thomaz. Universidade Federal do Espírito Santo; Brasil
Fil: Mayor, John Jairo Villarejo. Universidade Federal do Paraná; Brasil
Fil: Delis, Alberto López. Universidad de Oriente; Cuba
Fil: Carelli Albarracin, Ricardo Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
Fil: Neto, Anselmo Frizera. Universidade Federal do Espírito Santo; Brasil
Fil: Bastos, Teodiano Freire. Universidade Federal do Espírito Santo; Brasil
description Introduction: This work presents the development of a novel robotic knee exoskeleton controlled by motion intention based on sEMG, which uses admittance control to assist people with reduced mobility and improve their locomotion. Clinical research remark that these devices working in constant interaction with the neuromuscular and skeletal human system improves functional compensation and rehabilitation. Hence, the users become an active part of the training/rehabilitation, facilitating their involvement and improving their neural plasticity. For recognition of the lower-limb motion intention and discrimination of knee movements, sEMG from both lower-limb and trunk are used, which implies a new approach to control robotic assistive devices. Methods: A control system that includes a stage for human-motion intention recognition (HMIR), based on techniques to classify motion classes related to knee joint were developed. For translation of the user’s intention to a desired state for the robotic knee exoskeleton, the system also includes a finite state machine and admittance, velocity and trajectory controllers with a function that allows stopping the movement according to the users intention. Results: The proposed HMIR showed an accuracy between 76% to 83% for lower-limb muscles, and 71% to 77% for trunk muscles to classify motor classes of lower-limb movements. Experimental results of the controller showed that the admittance controller proposed here offers knee support in 50% of the gait cycle and assists correctly the motion classes. Conclusion: The robotic knee exoskeleton introduced here is an alternative method to empower knee movements using sEMG signals from lower-limb and trunk muscles.
publishDate 2018
dc.date.none.fl_str_mv 2018-09
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
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://hdl.handle.net/11336/89234
Villa Parra, Ana Cecilia; Delisle Rodriguez, Denis; Botelho, Thomaz; Mayor, John Jairo Villarejo; Delis, Alberto López; et al.; Control of a robotic knee exoskeleton for assistance and rehabilitation based on motion intention from sEMG; Sociedade Brasileira de Engenharia Biomédica; Research on Biomedical Engineering; 34; 3; 9-2018; 198-210
2446-4732
2446-4740
CONICET Digital
CONICET
url http://hdl.handle.net/11336/89234
identifier_str_mv Villa Parra, Ana Cecilia; Delisle Rodriguez, Denis; Botelho, Thomaz; Mayor, John Jairo Villarejo; Delis, Alberto López; et al.; Control of a robotic knee exoskeleton for assistance and rehabilitation based on motion intention from sEMG; Sociedade Brasileira de Engenharia Biomédica; Research on Biomedical Engineering; 34; 3; 9-2018; 198-210
2446-4732
2446-4740
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1590/2446-4740.07417
info:eu-repo/semantics/altIdentifier/url/http://ref.scielo.org/p4hpq5
info:eu-repo/semantics/altIdentifier/url/https://www.rbejournal.org/article/doi/10.1590/2446-4740.07417
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Sociedade Brasileira de Engenharia Biomédica
publisher.none.fl_str_mv Sociedade Brasileira de Engenharia Biomédica
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1844613218243182592
score 13.070432

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