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Synopsis
Exposure to physical therapy in rehabilitation shows a major interest in recent years for foot drop prevention by using ankle foot devices (AFO). In classifying the stance and swing phases, electromyography (EMG) signals were used to assist in utilising the AFO. Even though this approach has successfully controlled the actuator, classification model of EMG signals during stance and swing phases have not yet been discovered. Thus, a model to classify the stance and swing phases of EMG signals was proposed in this study. A model was developed by extracting the features using time domain (TD) and feeding it into artificial neural network (ANN) classifier. It was observed that Levenberg-Marquardt training algorithm of ANN with five TD features performed better than other features with an average percentage of classification accuracy of 87.4%. The outcome of this study could enhance the development of AFO and implementations in real time application were suggested for future applications.
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- PublisherLAP LAMBERT Academic Publishing
- Publication date2020
- ISBN 10 6202920947
- ISBN 13 9786202920940
- BindingPaperback
- LanguageEnglish
- Number of pages64
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Gait event detection based on EMG signals
Published by
LAP LAMBERT Academic Publishing Okt 2020, 2020
ISBN 10: 6202920947
ISBN 13: 9786202920940
New
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Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Exposure to physical therapy in rehabilitation shows a major interest in recent years for foot drop prevention by using ankle foot devices (AFO). In classifying the stance and swing phases, electromyography (EMG) signals were used to assist in utilising the AFO. Even though this approach has successfully controlled the actuator, classification model of EMG signals during stance and swing phases have not yet been discovered. Thus, a model to classify the stance and swing phases of EMG signals was proposed in this study. A model was developed by extracting the features using time domain (TD) and feeding it into artificial neural network (ANN) classifier. It was observed that Levenberg-Marquardt training algorithm of ANN with five TD features performed better than other features with an average percentage of classification accuracy of 87.4%. The outcome of this study could enhance the development of AFO and implementations in real time application were suggested for future applications. 64 pp. Englisch. Seller Inventory # 9786202920940
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Gait event detection based on EMG signals : Stance and swing phases
Published by
LAP LAMBERT Academic Publishing, 2020
ISBN 10: 6202920947
ISBN 13: 9786202920940
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Taschenbuch
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Exposure to physical therapy in rehabilitation shows a major interest in recent years for foot drop prevention by using ankle foot devices (AFO). In classifying the stance and swing phases, electromyography (EMG) signals were used to assist in utilising the AFO. Even though this approach has successfully controlled the actuator, classification model of EMG signals during stance and swing phases have not yet been discovered. Thus, a model to classify the stance and swing phases of EMG signals was proposed in this study. A model was developed by extracting the features using time domain (TD) and feeding it into artificial neural network (ANN) classifier. It was observed that Levenberg-Marquardt training algorithm of ANN with five TD features performed better than other features with an average percentage of classification accuracy of 87.4%. The outcome of this study could enhance the development of AFO and implementations in real time application were suggested for future applications. Seller Inventory # 9786202920940
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Gait event detection based on EMG signals: Stance and swing phases
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LAP LAMBERT Academic Publishing, 2020
ISBN 10: 6202920947
ISBN 13: 9786202920940
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Gait event detection based on EMG signals
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LAP LAMBERT Academic Publishing, 2020
ISBN 10: 6202920947
ISBN 13: 9786202920940
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Kartoniert / Broschiert. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Nazmi NurhazimahNurhazimah Nazmi (N. Nazmi) received Ph.D in Biomedical Engineering from Universiti Teknologi Malaysia (UTM) in 2018 and currently a senior lecturer at UTM. Her research interest include signal processing, machine lea. Seller Inventory # 410784980
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Gait event detection based on EMG signals: Stance and swing phases
Published by
LAP LAMBERT Academic Publishing, 2020
ISBN 10: 6202920947
ISBN 13: 9786202920940
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Gait event detection based on EMG signals: Stance and swing phases
Published by
LAP LAMBERT Academic Publishing, 2020
ISBN 10: 6202920947
ISBN 13: 9786202920940
New
Softcover
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Gait event detection based on EMG signals
Published by
LAP LAMBERT Academic Publishing Okt 2020, 2020
ISBN 10: 6202920947
ISBN 13: 9786202920940
New
Taschenbuch
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
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Taschenbuch. Condition: Neu. Neuware -Exposure to physical therapy in rehabilitation shows a major interest in recent years for foot drop prevention by using ankle foot devices (AFO). In classifying the stance and swing phases, electromyography (EMG) signals were used to assist in utilising the AFO. Even though this approach has successfully controlled the actuator, classification model of EMG signals during stance and swing phases have not yet been discovered. Thus, a model to classify the stance and swing phases of EMG signals was proposed in this study. A model was developed by extracting the features using time domain (TD) and feeding it into artificial neural network (ANN) classifier. It was observed that Levenberg-Marquardt training algorithm of ANN with five TD features performed better than other features with an average percentage of classification accuracy of 87.4%. The outcome of this study could enhance the development of AFO and implementations in real time application were suggested for future applications.Books on Demand GmbH, Überseering 33, 22297 Hamburg 64 pp. Englisch. Seller Inventory # 9786202920940
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