Evaluation of features used in electromyography classification
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Date
2021
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Abstract
Classification of electromyography (EMG) signals using machine learning has been studied for a long time. Today, this classification is tried to be made more accurate, fast and applicable by using the methods developed. However, beside this effort, it is suspected that researchers are using features without taking into account the effects on the classification performance, but often by influence of other researches. From this point of view, the effects of some features used in studies published in recent years on classification performance were tested and the results obtained were shared. In the experiments performed using a common method support vector machine (SVM), it was found that increasing the number of features does not always provide an increase in performance, even in some cases, it causes a decrease in accuracy rates.
Description
Alguner, Ayber Eray/0000-0003-0822-3957
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Keywords
Electromyography, Svm, Feature Evaluation
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Citation
Alguner, Ayber Eray; Ergezer, Halit (2021). "Evaluation of features used in electromyography classification", SIU 2021 - 29th IEEE Conference on Signal Processing and Communications Applications, Proceedings.
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29th IEEE Conference on Signal Processing and Communications Applications (SIU) -- JUN 09-11, 2021 -- ELECTR NETWORK