%0 Journal Article
%T IMPROVEMENT OF THE HYBRID FEATURE EXTRACTION METHOD FOR EMG SIGNALS
%A Abdullah £¿al£¿£¿kan
%J -
%D 2019
%X This study aims to discuss classification of 14 different finger movements from EMG signals by using new feature extraction technique. The detection/classification of finger movements consists of 3 main steps including, preprocessing, feature extraction and classification steps. In classification of EMG signals, the performance of the classifier directly depends on feature extraction methods, including, time, histogram and frequency-based methods. However, these feature extraction methods have several drawbacks including, high time complexity, high computation demand, user supplied parameters, etc. In this paper, a new feature extraction method has been proposed for the classification of finger movements from EMG signals to overcome these problems. The proposed method based on hybridization of 2-time domain feature extraction techniques. The use of this method resulted in an accuracy of 97.48% after 10-fold-cross-validation. The experimental results supported with statistical analysis show that proposed method is better than 9 feature extraction methods investigated in this pape
%K elektromiyografi
%K £¿znitelik £¿£¿karma
%K s£¿n£¿fland£¿rma
%K parmak hareketleri
%U http://dergipark.org.tr/ngumuh/issue/47481/542973