24,99 €
inkl. MwSt.
Versandfertig in 1-2 Wochen
12 °P sammeln
Andere Kunden interessierten sich auch für
![Evaluation of Techniques for the Decomposition of EMG Signals]( "Evaluation of Techniques for the Decomposition of EMG Signals")
Evaluation of Techniques for the Decomposition of EMG Signals29,99 €![MEMS Sensors for Measuring Gait Parameters]( "MEMS Sensors for Measuring Gait Parameters")
MEMS Sensors for Measuring Gait Parameters35,99 €![On the Design of a Highly Linear CMOS Programmable Transconductor]( "On the Design of a Highly Linear CMOS Programmable Transconductor")
On the Design of a Highly Linear CMOS Programmable Transconductor40,99 €![Identification and Decoding of EMG Signal Using SVM Classifier]( "Identification and Decoding of EMG Signal Using SVM Classifier")
Identification and Decoding of EMG Signal Using SVM Classifier44,99 €![ANN Based Lung Cancer Classification using Chest Radiographs]( "ANN Based Lung Cancer Classification using Chest Radiographs")
ANN Based Lung Cancer Classification using Chest Radiographs59,99 €![Single Event Upset in Dual- and Triple-Well SRAMs]( "Single Event Upset in Dual- and Triple-Well SRAMs")
Single Event Upset in Dual- and Triple-Well SRAMs35,99 €![An Approach to mitigate Single Event Latch Up and Soft Errors]( "An Approach to mitigate Single Event Latch Up and Soft Errors")
An Approach to mitigate Single Event Latch Up and Soft Errors35,99 €
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.
Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.