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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my.utm.904402021-04-30T14:41:39Z http://eprints.utm.my/id/eprint/90440/ Analysis of EMG signals during stance and swing phases for controlling Magnetorheological Brake applications Nazmi, Nurhazimah Abdul Rahman, Mohd. Azizi Mazlan, Saiful Amri Adiputra, Dimas Bahiuddin, Irfan Shabdin, Muhammad Kashfi Abdul Razak, Nurul Afifah Mohammed Ariff, Mohd. Hatta T Technology (General) 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. De Gruyter Open Ltd 2020-01 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/90440/1/NurhazimahNazmi2020_AnalysisofEMGSignalsduringStanceandSwingPhases.pdf Nazmi, Nurhazimah and Abdul Rahman, Mohd. Azizi and Mazlan, Saiful Amri and Adiputra, Dimas and Bahiuddin, Irfan and Shabdin, Muhammad Kashfi and Abdul Razak, Nurul Afifah and Mohammed Ariff, Mohd. Hatta (2020) Analysis of EMG signals during stance and swing phases for controlling Magnetorheological Brake applications. Open Engineering, 11 (1). pp. 112-119. ISSN 2391-5439 http://dx.doi.org/10.1515/eng-2021-0009 |
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T Technology (General) Nazmi, Nurhazimah Abdul Rahman, Mohd. Azizi Mazlan, Saiful Amri Adiputra, Dimas Bahiuddin, Irfan Shabdin, Muhammad Kashfi Abdul Razak, Nurul Afifah Mohammed Ariff, Mohd. Hatta Analysis of EMG signals during stance and swing phases for controlling Magnetorheological Brake applications |
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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. |
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Article |
author |
Nazmi, Nurhazimah Abdul Rahman, Mohd. Azizi Mazlan, Saiful Amri Adiputra, Dimas Bahiuddin, Irfan Shabdin, Muhammad Kashfi Abdul Razak, Nurul Afifah Mohammed Ariff, Mohd. Hatta |
author_facet |
Nazmi, Nurhazimah Abdul Rahman, Mohd. Azizi Mazlan, Saiful Amri Adiputra, Dimas Bahiuddin, Irfan Shabdin, Muhammad Kashfi Abdul Razak, Nurul Afifah Mohammed Ariff, Mohd. Hatta |
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 Open Ltd |
publishDate |
2020 |
url |
http://eprints.utm.my/id/eprint/90440/1/NurhazimahNazmi2020_AnalysisofEMGSignalsduringStanceandSwingPhases.pdf http://eprints.utm.my/id/eprint/90440/ http://dx.doi.org/10.1515/eng-2021-0009 |
_version_ |
1698696936398258176 |
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13.159267 |