gait event detection based von nazmi nurhazimah (7 Ergebnisse)

Taschenbuch. Zustand: 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.

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Kartoniert / Broschiert. Zustand: 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.

Taschenbuch. Zustand: 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.

Taschenbuch. Zustand: 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.

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