Sensor Number Optimization Using Neural Network for Ankle Foot Orthosis Equipped with Magnetorheological Brake

A passive controlled ankle foot orthosis (PICAFO) used a passive actuator such as Magnetorheological (MR) brake to control the ankle stiffness. The PICAFO used two kinds of sensors, such as Electromyography (EMG) signal and ankle position (two inputs) to determine the amount of stiffness (one output...

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Autores principales:	Adiputra Dimas, Azizi Abdul Rahman Mohd, Bahiuddin Irfan, Ubaidillah, Imaduddin Fitrian, Nazmi Nurhazimah
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2020
Materias:	magnetorheological brake damping stiffness sensor numbers machine learning nonlinear autoregressive exogenous Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/f53a34b89b794a90bcd0c6d5645d6300
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id	oai:doaj.org-article:f53a34b89b794a90bcd0c6d5645d6300
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spelling	oai:doaj.org-article:f53a34b89b794a90bcd0c6d5645d63002021-12-05T14:10:46ZSensor Number Optimization Using Neural Network for Ankle Foot Orthosis Equipped with Magnetorheological Brake2391-543910.1515/eng-2021-0010https://doaj.org/article/f53a34b89b794a90bcd0c6d5645d63002020-11-01T00:00:00Zhttps://doi.org/10.1515/eng-2021-0010https://doaj.org/toc/2391-5439A passive controlled ankle foot orthosis (PICAFO) used a passive actuator such as Magnetorheological (MR) brake to control the ankle stiffness. The PICAFO used two kinds of sensors, such as Electromyography (EMG) signal and ankle position (two inputs) to determine the amount of stiffness (one output) to be generated by the MR brake. As the overall weight and design of an orthotic device must be optimized, the sensor numbers on PICAFO wanted to be reduced. To do that, a machine learning approach was implemented to simplify the previous stiffness function. In this paper, Non-linear Autoregressive Exogeneous (NARX) neural network were used to generate the simplified function. A total of 2060 data were used to build the network with detail such as 1309 training data, 281 validation data, 281 testing data 1, and 189 testing data 2. Three training algorithms were used such as Levenberg-Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient. The result shows that the function can be simplified into one input (ankle position) – one output (stiffness). Optimized result was shown by the NARX neural network with 15 hidden layers and trained using Bayesian Regularization with delay 2. In this case, the testing data shows R-value of 0.992 and MSE of 19.16.Adiputra DimasAzizi Abdul Rahman MohdBahiuddin IrfanUbaidillahImaduddin FitrianNazmi NurhazimahDe Gruyterarticlemagnetorheological brakedamping stiffnesssensor numbersmachine learningnonlinear autoregressive exogenousEngineering (General). Civil engineering (General)TA1-2040ENOpen Engineering, Vol 11, Iss 1, Pp 91-101 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	magnetorheological brake damping stiffness sensor numbers machine learning nonlinear autoregressive exogenous Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	magnetorheological brake damping stiffness sensor numbers machine learning nonlinear autoregressive exogenous Engineering (General). Civil engineering (General) TA1-2040 Adiputra Dimas Azizi Abdul Rahman Mohd Bahiuddin Irfan Ubaidillah Imaduddin Fitrian Nazmi Nurhazimah Sensor Number Optimization Using Neural Network for Ankle Foot Orthosis Equipped with Magnetorheological Brake
description	A passive controlled ankle foot orthosis (PICAFO) used a passive actuator such as Magnetorheological (MR) brake to control the ankle stiffness. The PICAFO used two kinds of sensors, such as Electromyography (EMG) signal and ankle position (two inputs) to determine the amount of stiffness (one output) to be generated by the MR brake. As the overall weight and design of an orthotic device must be optimized, the sensor numbers on PICAFO wanted to be reduced. To do that, a machine learning approach was implemented to simplify the previous stiffness function. In this paper, Non-linear Autoregressive Exogeneous (NARX) neural network were used to generate the simplified function. A total of 2060 data were used to build the network with detail such as 1309 training data, 281 validation data, 281 testing data 1, and 189 testing data 2. Three training algorithms were used such as Levenberg-Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient. The result shows that the function can be simplified into one input (ankle position) – one output (stiffness). Optimized result was shown by the NARX neural network with 15 hidden layers and trained using Bayesian Regularization with delay 2. In this case, the testing data shows R-value of 0.992 and MSE of 19.16.
format	article
author	Adiputra Dimas Azizi Abdul Rahman Mohd Bahiuddin Irfan Ubaidillah Imaduddin Fitrian Nazmi Nurhazimah
author_facet	Adiputra Dimas Azizi Abdul Rahman Mohd Bahiuddin Irfan Ubaidillah Imaduddin Fitrian Nazmi Nurhazimah
author_sort	Adiputra Dimas
title	Sensor Number Optimization Using Neural Network for Ankle Foot Orthosis Equipped with Magnetorheological Brake
title_short	Sensor Number Optimization Using Neural Network for Ankle Foot Orthosis Equipped with Magnetorheological Brake
title_full	Sensor Number Optimization Using Neural Network for Ankle Foot Orthosis Equipped with Magnetorheological Brake
title_fullStr	Sensor Number Optimization Using Neural Network for Ankle Foot Orthosis Equipped with Magnetorheological Brake
title_full_unstemmed	Sensor Number Optimization Using Neural Network for Ankle Foot Orthosis Equipped with Magnetorheological Brake
title_sort	sensor number optimization using neural network for ankle foot orthosis equipped with magnetorheological brake
publisher	De Gruyter
publishDate	2020
url	https://doaj.org/article/f53a34b89b794a90bcd0c6d5645d6300
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Sensor Number Optimization Using Neural Network for Ankle Foot Orthosis Equipped with Magnetorheological Brake

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