Control of a robotic knee exoskeleton for assistance and rehabilitation based on motion intention from sEMG
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Research on Biomedical Engineering (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2446-47402018000300198 |
Resumo: | 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. |
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Research on Biomedical Engineering (Online) |
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Control of a robotic knee exoskeleton for assistance and rehabilitation based on motion intention from sEMGRobotic knee exoskeletonElectromyographyTrunk musclesUser intention recognitionAdmittance control 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.Sociedade Brasileira de Engenharia Biomédica2018-09-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S2446-47402018000300198Research on Biomedical Engineering v.34 n.3 2018reponame:Research on Biomedical Engineering (Online)instname:Sociedade Brasileira de Engenharia Biomédica (SBEB)instacron:SBEB10.1590/2446-4740.07417info:eu-repo/semantics/openAccessVilla-Parra,Ana CeciliaDelisle-Rodriguez,DenisBotelho,ThomazMayor,John Jairo VillarejoDelis,Alberto LópezCarelli,RicardoFrizera Neto,AnselmoBastos,Teodiano Freireeng2018-10-30T00:00:00Zoai:scielo:S2446-47402018000300198Revistahttp://www.rbejournal.org/https://old.scielo.br/oai/scielo-oai.php||rbe@rbejournal.org2446-47402446-4732opendoar:2018-10-30T00:00Research on Biomedical Engineering (Online) - Sociedade Brasileira de Engenharia Biomédica (SBEB)false |
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 Robotic knee exoskeleton Electromyography Trunk muscles User intention recognition Admittance control |
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 |
author |
Villa-Parra,Ana Cecilia |
author_facet |
Villa-Parra,Ana Cecilia Delisle-Rodriguez,Denis Botelho,Thomaz Mayor,John Jairo Villarejo Delis,Alberto López Carelli,Ricardo Frizera Neto,Anselmo Bastos,Teodiano Freire |
author_role |
author |
author2 |
Delisle-Rodriguez,Denis Botelho,Thomaz Mayor,John Jairo Villarejo Delis,Alberto López Carelli,Ricardo Frizera Neto,Anselmo Bastos,Teodiano Freire |
author2_role |
author author author author author author author |
dc.contributor.author.fl_str_mv |
Villa-Parra,Ana Cecilia Delisle-Rodriguez,Denis Botelho,Thomaz Mayor,John Jairo Villarejo Delis,Alberto López Carelli,Ricardo Frizera Neto,Anselmo Bastos,Teodiano Freire |
dc.subject.por.fl_str_mv |
Robotic knee exoskeleton Electromyography Trunk muscles User intention recognition Admittance control |
topic |
Robotic knee exoskeleton Electromyography Trunk muscles User intention recognition Admittance control |
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 |
2018-09-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2446-47402018000300198 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2446-47402018000300198 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/2446-4740.07417 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
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 |
Research on Biomedical Engineering v.34 n.3 2018 reponame:Research on Biomedical Engineering (Online) instname:Sociedade Brasileira de Engenharia Biomédica (SBEB) instacron:SBEB |
instname_str |
Sociedade Brasileira de Engenharia Biomédica (SBEB) |
instacron_str |
SBEB |
institution |
SBEB |
reponame_str |
Research on Biomedical Engineering (Online) |
collection |
Research on Biomedical Engineering (Online) |
repository.name.fl_str_mv |
Research on Biomedical Engineering (Online) - Sociedade Brasileira de Engenharia Biomédica (SBEB) |
repository.mail.fl_str_mv |
||rbe@rbejournal.org |
_version_ |
1752126288987947008 |