An Ensemble Classifier for Finger Movement Recognition using EMG Signals

Yıl 2019, , 96 - 99, 31.12.2019

İlker Ali Özkan

https://doi.org/10.18100/ijamec.659781

Cited By: 2

Öz

Electromyography
(EMG) signals that obtained by electrodes connected to the forearm are the
monitoring of the muscles by the electrical method. These signals are quite
useful during the use of prosthesis as a source signal to the moving prosthesis.
Therefore, it is essential that classifying the EMG signals with high accuracy
by analyzing. This study aims that classifying the individual and combined
finger movements using surface EMG signals taken from the surface of the human
forearm. EMG signals that belong to 10 different finger movements obtained from
eight subjects were used. Firstly, EMG signals have been split into segments by
the windowing process, and temporal feature vectors are formed by applying
various feature extraction methods to these segments.  Feature vectors have been classified with the
ensemble bagged tree algorithm, which is a combination of classifiers, to
obtain the correct classification decision. As a result of 10-fold
cross-validation, with the proposed method, 96.6% overall classification
accuracy was achieved. The results obtained show that the ensemble classifier
can be used successfully in determining finger movements when compared with
similar studies.

Anahtar Kelimeler

Ensemble Classifier, Bagged Tree, Electromyography, Feature Extraction, Finger Movement

Kaynakça

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