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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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Main Authors:	Adiputra, Dimas, Abdul Rahman, Mohd. Azizi, Bahiuddin, Irfan, Ubaidillah, Ubaidillah, Imaduddin, Fitrian, Nazmi, Nurhazimah
Format:	Article
Language:	English
Published:	De Gruyter Open Ltd 2020
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/90921/1/DimasAdiputra2020_SensorNumberOptimizationUsingNeural.pdf http://eprints.utm.my/id/eprint/90921/ http://dx.doi.org/10.1515/eng-2021-0010
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spelling	my.utm.909212021-05-31T13:40:19Z http://eprints.utm.my/id/eprint/90921/ Sensor number optimization using neural network for ankle foot orthosis equipped with magnetorheological brake Adiputra, Dimas Abdul Rahman, Mohd. Azizi Bahiuddin, Irfan Ubaidillah, Ubaidillah Imaduddin, Fitrian Nazmi, Nurhazimah TK Electrical engineering. Electronics Nuclear engineering 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. De Gruyter Open Ltd 2020-01-01 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/90921/1/DimasAdiputra2020_SensorNumberOptimizationUsingNeural.pdf Adiputra, Dimas and Abdul Rahman, Mohd. Azizi and Bahiuddin, Irfan and Ubaidillah, Ubaidillah and Imaduddin, Fitrian and Nazmi, Nurhazimah (2020) Sensor number optimization using neural network for ankle foot orthosis equipped with magnetorheological brake. Open Engineering, 11 (1). pp. 91-101. ISSN 2391-5439 http://dx.doi.org/10.1515/eng-2021-0010 DOI:10.1515/eng-2021-0010
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
language	English
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Adiputra, Dimas Abdul Rahman, Mohd. Azizi Bahiuddin, Irfan Ubaidillah, 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 Abdul Rahman, Mohd. Azizi Bahiuddin, Irfan Ubaidillah, Ubaidillah Imaduddin, Fitrian Nazmi, Nurhazimah
author_facet	Adiputra, Dimas Abdul Rahman, Mohd. Azizi Bahiuddin, Irfan Ubaidillah, 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 Open Ltd
publishDate	2020
url	http://eprints.utm.my/id/eprint/90921/1/DimasAdiputra2020_SensorNumberOptimizationUsingNeural.pdf http://eprints.utm.my/id/eprint/90921/ http://dx.doi.org/10.1515/eng-2021-0010
_version_	1702169620649082880
score	13.211869

Sensor number optimization using neural network for ankle foot orthosis equipped with magnetorheological brake

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