Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi

Yazarlar: Abdullah Çalışkan

Konular:Bilgisayar Bilimleri, Bilgi Sistemleri

DOI:10.28948/ngumuh.542973

Anahtar Kelimeler:Electromyography,Feature extraction,Classification,Finger movements

Özet:    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 paper

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