Single channel EMG classification using DWT and SVM

Cherrih, Hachemi; Talha, Malika Kedir; Zairi, Hadjer; Meddah, Karim

doi:10.1109/ihsh51661.2021.9378707

Cited by 10 publications

(1 citation statement)

References 12 publications

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“…Our research group has previously approximated spectral analysis of EMG signals and extracted features using reflection coefficients [15] and classified the results with SVM, without any feature reduction. It is shown in [16] that discrete wavelet transform followed by principal component analysis (PCA) and SVM could enhance the accuracy of classifications. Several other EMG classifiers have been applied using decision trees [17], random forest [18], KNN [14], [19], Naïve Bayes classifier [20], and SVM [15], [17], [21].…”

Section: Introductionmentioning

confidence: 99%

EMG Signal Classification Using Reflection Coefficients and Extreme Value Machine

Azhiri,

Esmaeili,

Jafarzadeh

et al. 2021

Preprint

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Electromyography is a promising approach to the gesture recognition of humans if an efficient classifier with a high accuracy is available. In this paper, we propose to utilize Extreme Value Machine (EVM) as a high performance algorithm for the classification of EMG signals. We employ reflection coefficients obtained from an Autoregressive (AR) model to train a set of classifiers. Our experimental results indicate that EVM has better accuracy in comparison to the conventional classifiers approved in the literature based on K-Nearest Neighbors (KNN) and Support Vector Machine (SVM).

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