Differentiating type of muscle movement via AR modeling and neural network classification

Simple item page
dc.contributor.authorKarlik Bekir
dc.date.accessioned2024-07-22T08:25:48Z
dc.date.available2024-07-22T08:25:48Z
dc.date.issued1999
dc.description.abstractThe aim of this study is to classify electromyogram (EMG) signals for controlling multifunction proshetic devices. An artificial neural network (ANN) implementation was used for this purpose. Autoregressive (AR) parameters of a1, a2, a3, a4 and their signal power obtained from different arm muscle motions were applied to the input of ANN, which is a multilayer perceptron. At the output layer, for 5000 iterations, six movements were distinguished at a high accuracy of 97.6%.
dc.identifier.issn13000632
dc.identifier.urihttp://akademikarsiv.cbu.edu.tr:4000/handle/123456789/20590
dc.language.isoEnglish
dc.subjectClassification (of information)
dc.subjectElectromyography
dc.subjectMuscle
dc.subjectNeural networks
dc.subjectRegression analysis
dc.subjectAutoregressive (AR) parameters
dc.subjectMyoelectric signals
dc.subjectProsthetics
dc.titleDifferentiating type of muscle movement via AR modeling and neural network classification
dc.typeArticle

Files

Collections

Scopus Koleksiyonu