Functional electrical stimulation system for drop foot correction using a dynamic NARX neural network
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/1822/76481 |
Resumo: | Neurological diseases may reduce Tibialis Anterior (TA) muscle recruitment capacity causing gait disorders, such as drop foot (DF). The majority of DF patients still retain excitable nerves and muscles which makes Functional Electrical Stimulation (FES) an adequate technique to restore lost mobility. Recent studies suggest the need for developing personalized and assist-as-needed control strategies for wearable FES in order to promote natural and functional movements while reducing the early onset of fatigue. This study contributes to a real-time implementation of a trajectory tracking FES control strategy for personalized DF correction. This strategy combines a feedforward Non-Linear Autoregressive Neural Network with Exogenous inputs (NARXNN) with a feedback PD controller. This control strategy advances with a user-specific TA muscle model achieved by the NARXNN’s ability to model dynamic systems relying on the foot angle and angular velocity as inputs. A closed-loop, fully wearable stimulation system was achieved using an ISTim stimulator and wearable inertial sensor for electrical stimulation and user’s kinematic gait sensing, respectively. Results showed that the NARXNN architecture with 2 hidden layers and 10 neurons provided the highest performance for modelling the kinematic behaviour of the TA muscle. The proposed trajectory tracking control revealed a low discrepancy between real and reference foot trajectories (goodness of fit = 77.87%) and time-effectiveness for correctly stimulating the TA muscle towards a natural gait and DF correction. |
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spelling |
Functional electrical stimulation system for drop foot correction using a dynamic NARX neural networkClosed loop controlDrop footFunctional Electrical StimulationMuscle modellingNeural networkHuman-robot interfaceHybrid controlScience & TechnologyNeurological diseases may reduce Tibialis Anterior (TA) muscle recruitment capacity causing gait disorders, such as drop foot (DF). The majority of DF patients still retain excitable nerves and muscles which makes Functional Electrical Stimulation (FES) an adequate technique to restore lost mobility. Recent studies suggest the need for developing personalized and assist-as-needed control strategies for wearable FES in order to promote natural and functional movements while reducing the early onset of fatigue. This study contributes to a real-time implementation of a trajectory tracking FES control strategy for personalized DF correction. This strategy combines a feedforward Non-Linear Autoregressive Neural Network with Exogenous inputs (NARXNN) with a feedback PD controller. This control strategy advances with a user-specific TA muscle model achieved by the NARXNN’s ability to model dynamic systems relying on the foot angle and angular velocity as inputs. A closed-loop, fully wearable stimulation system was achieved using an ISTim stimulator and wearable inertial sensor for electrical stimulation and user’s kinematic gait sensing, respectively. Results showed that the NARXNN architecture with 2 hidden layers and 10 neurons provided the highest performance for modelling the kinematic behaviour of the TA muscle. The proposed trajectory tracking control revealed a low discrepancy between real and reference foot trajectories (goodness of fit = 77.87%) and time-effectiveness for correctly stimulating the TA muscle towards a natural gait and DF correction.This work has been supported by the Fundação para a Ciência e Tecnologia (FCT) with the Reference Scholarship under Grant SFRH/BD/147878/2019 and under the national support to R&D units grant, through the reference project UIDB/04436/2020 and UIDP/04436/2020. This work was supported by FCT, through IDMEC, under LAETA, project UIDB/50022/2020.Multidisciplinary Digital Publishing Institute (MDPI)Universidade do MinhoCarvalho, SimãoCorreia, AnaFigueiredo, JoanaMartins, Jorge M.Santos, Cristina2021-10-262021-10-26T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/1822/76481engCarvalho, S.; Correia, A.; Figueiredo, J.; Martins, J.M.; Santos, C.P. Functional Electrical Stimulation System for Drop Foot Correction Using a Dynamic NARX Neural Network. Machines 2021, 9, 253. https://doi.org/10.3390/machines91102532075-170210.3390/machines9110253253https://www.mdpi.com/2075-1702/9/11/253info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-21T12:26:42Zoai:repositorium.sdum.uminho.pt:1822/76481Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:21:11.036562Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Functional electrical stimulation system for drop foot correction using a dynamic NARX neural network |
title |
Functional electrical stimulation system for drop foot correction using a dynamic NARX neural network |
spellingShingle |
Functional electrical stimulation system for drop foot correction using a dynamic NARX neural network Carvalho, Simão Closed loop control Drop foot Functional Electrical Stimulation Muscle modelling Neural network Human-robot interface Hybrid control Science & Technology |
title_short |
Functional electrical stimulation system for drop foot correction using a dynamic NARX neural network |
title_full |
Functional electrical stimulation system for drop foot correction using a dynamic NARX neural network |
title_fullStr |
Functional electrical stimulation system for drop foot correction using a dynamic NARX neural network |
title_full_unstemmed |
Functional electrical stimulation system for drop foot correction using a dynamic NARX neural network |
title_sort |
Functional electrical stimulation system for drop foot correction using a dynamic NARX neural network |
author |
Carvalho, Simão |
author_facet |
Carvalho, Simão Correia, Ana Figueiredo, Joana Martins, Jorge M. Santos, Cristina |
author_role |
author |
author2 |
Correia, Ana Figueiredo, Joana Martins, Jorge M. Santos, Cristina |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Carvalho, Simão Correia, Ana Figueiredo, Joana Martins, Jorge M. Santos, Cristina |
dc.subject.por.fl_str_mv |
Closed loop control Drop foot Functional Electrical Stimulation Muscle modelling Neural network Human-robot interface Hybrid control Science & Technology |
topic |
Closed loop control Drop foot Functional Electrical Stimulation Muscle modelling Neural network Human-robot interface Hybrid control Science & Technology |
description |
Neurological diseases may reduce Tibialis Anterior (TA) muscle recruitment capacity causing gait disorders, such as drop foot (DF). The majority of DF patients still retain excitable nerves and muscles which makes Functional Electrical Stimulation (FES) an adequate technique to restore lost mobility. Recent studies suggest the need for developing personalized and assist-as-needed control strategies for wearable FES in order to promote natural and functional movements while reducing the early onset of fatigue. This study contributes to a real-time implementation of a trajectory tracking FES control strategy for personalized DF correction. This strategy combines a feedforward Non-Linear Autoregressive Neural Network with Exogenous inputs (NARXNN) with a feedback PD controller. This control strategy advances with a user-specific TA muscle model achieved by the NARXNN’s ability to model dynamic systems relying on the foot angle and angular velocity as inputs. A closed-loop, fully wearable stimulation system was achieved using an ISTim stimulator and wearable inertial sensor for electrical stimulation and user’s kinematic gait sensing, respectively. Results showed that the NARXNN architecture with 2 hidden layers and 10 neurons provided the highest performance for modelling the kinematic behaviour of the TA muscle. The proposed trajectory tracking control revealed a low discrepancy between real and reference foot trajectories (goodness of fit = 77.87%) and time-effectiveness for correctly stimulating the TA muscle towards a natural gait and DF correction. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-10-26 2021-10-26T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/1822/76481 |
url |
http://hdl.handle.net/1822/76481 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Carvalho, S.; Correia, A.; Figueiredo, J.; Martins, J.M.; Santos, C.P. Functional Electrical Stimulation System for Drop Foot Correction Using a Dynamic NARX Neural Network. Machines 2021, 9, 253. https://doi.org/10.3390/machines9110253 2075-1702 10.3390/machines9110253 253 https://www.mdpi.com/2075-1702/9/11/253 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Multidisciplinary Digital Publishing Institute (MDPI) |
publisher.none.fl_str_mv |
Multidisciplinary Digital Publishing Institute (MDPI) |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799132677466488832 |