Gait event detection based on EMG signals

Nurhazimah Nazmi

ISBN 10: 6202920947 / ISBN 13: 9786202920940

Verlag: LAP LAMBERT Academic Publishing Okt 2020, 2020

Beschreibung

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.

Bestandsnummer des Verkäufers 9786202920940

Bibliografische Details

• Titel: Gait event detection based on EMG signals
• Verlag: LAP LAMBERT Academic Publishing Okt 2020
• Veröffentlichungsdatum: 2020
• Sprache: Englisch
• ISBN-10: 6202920947
• ISBN-13: 9786202920940
• Bindung: Taschenbuch
• Zustand: Neu

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Inhaltsangabe

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.