Analysis of EMG Signals during Stance and Swing Phases for Controlling Magnetorheological Brake applications

The development of ankle foot orthoses (AFO) for lower limb rehabilitation have received significant attention over the past decades. Recently, passive AFO equipped with magnetorheological brake had been developed based on ankle angle and electromyography (EMG) signals. Nonetheless, the EMG signals...

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Autores principales:	Nazmi Nurhazimah, Azizi Abdul Rahman Mohd, Amri Mazlan Saiful, Adiputra Dimas, Bahiuddin Irfan, Kashfi Shabdin Muhammad, Afifah Abdul Razak Nurul, Hatta Mohammed Ariff Mohd
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2020
Materias:	emg signals td features ann gait phases mr brake Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/bac4853821464b3f9ef9ac51ebe353dc
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spelling	oai:doaj.org-article:bac4853821464b3f9ef9ac51ebe353dc2021-12-05T14:10:46ZAnalysis of EMG Signals during Stance and Swing Phases for Controlling Magnetorheological Brake applications2391-543910.1515/eng-2021-0009https://doaj.org/article/bac4853821464b3f9ef9ac51ebe353dc2020-11-01T00:00:00Zhttps://doi.org/10.1515/eng-2021-0009https://doaj.org/toc/2391-5439The development of ankle foot orthoses (AFO) for lower limb rehabilitation have received significant attention over the past decades. Recently, passive AFO equipped with magnetorheological brake had been developed based on ankle angle and electromyography (EMG) signals. Nonetheless, the EMG signals were categorized in stance and swing phases through visual observation as the signals are stochastic. Therefore, this study aims to classify the pattern of EMG signals during stance and swing phases. Seven-time domains features will be extracted and fed into artificial neural network (ANN) as a classifier. Two different training algorithms of ANN namely Levenberg-Marquardt (LM) and Scaled Conjugate Gradient (SCG) will be applied. As number of inputs will affect the classification performance of ANN, different number of input features will be employed. In this study, three participants were recruited and walk on the treadmills for 60 seconds by constant the speed. The ANN model was designed with 2, 10, 12, and 14 inputs features with LM and SCG training algorithms. Then, the ANN was trained ten times and the performances of each inputs features were measured using classification rate of training, testing, validation and overall. This study found that all the inputs with LM training algorithm gained more than 2% average classification rate than SCG training algorithm. On the other hand, classification accuracy of 10, 12 and 14 inputs were 5% higher than 2 inputs. It can be concluded that LM training algorithm of ANN was performed better than SCG algorithm with at least 10 inputs.Nazmi NurhazimahAzizi Abdul Rahman MohdAmri Mazlan SaifulAdiputra DimasBahiuddin IrfanKashfi Shabdin MuhammadAfifah Abdul Razak NurulHatta Mohammed Ariff MohdDe Gruyterarticleemg signalstd featuresanngait phasesmr brakeEngineering (General). Civil engineering (General)TA1-2040ENOpen Engineering, Vol 11, Iss 1, Pp 112-119 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	emg signals td features ann gait phases mr brake Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	emg signals td features ann gait phases mr brake Engineering (General). Civil engineering (General) TA1-2040 Nazmi Nurhazimah Azizi Abdul Rahman Mohd Amri Mazlan Saiful Adiputra Dimas Bahiuddin Irfan Kashfi Shabdin Muhammad Afifah Abdul Razak Nurul Hatta Mohammed Ariff Mohd Analysis of EMG Signals during Stance and Swing Phases for Controlling Magnetorheological Brake applications
description	The development of ankle foot orthoses (AFO) for lower limb rehabilitation have received significant attention over the past decades. Recently, passive AFO equipped with magnetorheological brake had been developed based on ankle angle and electromyography (EMG) signals. Nonetheless, the EMG signals were categorized in stance and swing phases through visual observation as the signals are stochastic. Therefore, this study aims to classify the pattern of EMG signals during stance and swing phases. Seven-time domains features will be extracted and fed into artificial neural network (ANN) as a classifier. Two different training algorithms of ANN namely Levenberg-Marquardt (LM) and Scaled Conjugate Gradient (SCG) will be applied. As number of inputs will affect the classification performance of ANN, different number of input features will be employed. In this study, three participants were recruited and walk on the treadmills for 60 seconds by constant the speed. The ANN model was designed with 2, 10, 12, and 14 inputs features with LM and SCG training algorithms. Then, the ANN was trained ten times and the performances of each inputs features were measured using classification rate of training, testing, validation and overall. This study found that all the inputs with LM training algorithm gained more than 2% average classification rate than SCG training algorithm. On the other hand, classification accuracy of 10, 12 and 14 inputs were 5% higher than 2 inputs. It can be concluded that LM training algorithm of ANN was performed better than SCG algorithm with at least 10 inputs.
format	article
author	Nazmi Nurhazimah Azizi Abdul Rahman Mohd Amri Mazlan Saiful Adiputra Dimas Bahiuddin Irfan Kashfi Shabdin Muhammad Afifah Abdul Razak Nurul Hatta Mohammed Ariff Mohd
author_facet	Nazmi Nurhazimah Azizi Abdul Rahman Mohd Amri Mazlan Saiful Adiputra Dimas Bahiuddin Irfan Kashfi Shabdin Muhammad Afifah Abdul Razak Nurul Hatta Mohammed Ariff Mohd
author_sort	Nazmi Nurhazimah
title	Analysis of EMG Signals during Stance and Swing Phases for Controlling Magnetorheological Brake applications
title_short	Analysis of EMG Signals during Stance and Swing Phases for Controlling Magnetorheological Brake applications
title_full	Analysis of EMG Signals during Stance and Swing Phases for Controlling Magnetorheological Brake applications
title_fullStr	Analysis of EMG Signals during Stance and Swing Phases for Controlling Magnetorheological Brake applications
title_full_unstemmed	Analysis of EMG Signals during Stance and Swing Phases for Controlling Magnetorheological Brake applications
title_sort	analysis of emg signals during stance and swing phases for controlling magnetorheological brake applications
publisher	De Gruyter
publishDate	2020
url	https://doaj.org/article/bac4853821464b3f9ef9ac51ebe353dc
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Analysis of EMG Signals during Stance and Swing Phases for Controlling Magnetorheological Brake applications

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