Fractional Integration Based Feature Extractor for EMG Signals

Year 2022, , 132 - 138, 30.04.2022

İbrahim Ethem Saçu

https://doi.org/10.17694/bajece.899088

Abstract

Electromyography (EMG) signals have been extensively used for
identification of finger movements, hand gestures
and physical activities. In the classification of EMG signals, the
performance of the classifier is widely determined by the feature
extraction methods. Thus, plenty of feature extraction methods
based on time, histogram and frequency domain have been
reported in literature. However, these methods have several
drawbacks such as high time complexity, high computation demand
and user supplied parameters. To overcome these deficiencies, in
this work, a new feature extraction method has been proposed
to classify EMG signals taken from two different data sets finger
movements (FM) and physical actions (PA). While FM data set
includes 14 different finger movements, PA data set involves
20 different physical activities. The proposed method is based
on numerical fractional integration of time series EMG signals
with different fractional-orders. K Nearest Neighborhood (KNN)
classifier with 8-fold cross validation has been employed for
prediction of EMG signals. The derived fractional features can
give better results than the two commonly used time domain
features, notably, mean absolute value (MAV) and waveform
length (WL) in terms of accuracy. The experimental results are
also supported by statistical analysis results.

Keywords

Fractional integration, feature extraction, EMG, signal processing

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