Functional Electrical Stimulation System for Drop Foot Correction Using a Dynamic NARX Neural Network

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...

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Autores principales:	Simão Carvalho, Ana Correia, Joana Figueiredo, Jorge M. Martins, Cristina P. Santos
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	closed loop control drop foot Functional Electrical Stimulation muscle modelling neural network human-robot interface Mechanical engineering and machinery TJ1-1570
Acceso en línea:	https://doaj.org/article/b149a50e32e740528021f64280788fee
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spelling	oai:doaj.org-article:b149a50e32e740528021f64280788fee2021-11-25T18:12:02ZFunctional Electrical Stimulation System for Drop Foot Correction Using a Dynamic NARX Neural Network10.3390/machines91102532075-1702https://doaj.org/article/b149a50e32e740528021f64280788fee2021-10-01T00:00:00Zhttps://www.mdpi.com/2075-1702/9/11/253https://doaj.org/toc/2075-1702Neurological 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.Simão CarvalhoAna CorreiaJoana FigueiredoJorge M. MartinsCristina P. SantosMDPI AGarticleclosed loop controldrop footFunctional Electrical Stimulationmuscle modellingneural networkhuman-robot interfaceMechanical engineering and machineryTJ1-1570ENMachines, Vol 9, Iss 253, p 253 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	closed loop control drop foot Functional Electrical Stimulation muscle modelling neural network human-robot interface Mechanical engineering and machinery TJ1-1570
spellingShingle	closed loop control drop foot Functional Electrical Stimulation muscle modelling neural network human-robot interface Mechanical engineering and machinery TJ1-1570 Simão Carvalho Ana Correia Joana Figueiredo Jorge M. Martins Cristina P. Santos Functional Electrical Stimulation System for Drop Foot Correction Using a Dynamic NARX Neural Network
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.
format	article
author	Simão Carvalho Ana Correia Joana Figueiredo Jorge M. Martins Cristina P. Santos
author_facet	Simão Carvalho Ana Correia Joana Figueiredo Jorge M. Martins Cristina P. Santos
author_sort	Simão Carvalho
title	Functional Electrical Stimulation System for Drop Foot Correction Using a Dynamic NARX Neural Network
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
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/b149a50e32e740528021f64280788fee
work_keys_str_mv	AT simaocarvalho functionalelectricalstimulationsystemfordropfootcorrectionusingadynamicnarxneuralnetwork AT anacorreia functionalelectricalstimulationsystemfordropfootcorrectionusingadynamicnarxneuralnetwork AT joanafigueiredo functionalelectricalstimulationsystemfordropfootcorrectionusingadynamicnarxneuralnetwork AT jorgemmartins functionalelectricalstimulationsystemfordropfootcorrectionusingadynamicnarxneuralnetwork AT cristinapsantos functionalelectricalstimulationsystemfordropfootcorrectionusingadynamicnarxneuralnetwork
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Functional Electrical Stimulation System for Drop Foot Correction Using a Dynamic NARX Neural Network

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