Makine öğrenmesi algoritmaları kullanılarak elektromiyografi tabanlı el hareketi sınıflandırması ve özellik çıkarımı

Year 2023, , 1621 - 1633, 01.12.2023

Ekin Ekinci , Zeynep Garip , Kasım Serbest

https://doi.org/10.2339/politeknik.1348121

Cited By: 2

Abstract

El hareketlerinin sınıflandırılması özellikle ortez ve protez kontrolü, insan-makine etkileşimi ve telerehabilitasyon uygulamaları için önem arz etmektedir. Görüntü işleme tekniklerine dayalı hareket analizi yöntemleri ile el hareketlerinin belirlenmesi uzun yıllardır uygulanmaktadır. Günümüzde üzerinde durulan araştırma alanlarından biri kas kasılmasından faydalanarak el hareketlerinin belirlenmesidir. Son yıllarda el ve kol kaslarından alınan sEMG (yüzey elektromiyografi) verileri yardımıyla el hareketlerinin sınıflandırılması üzerine çalışmalar hız kazanmıştır. Bizim çalışmamızda 4 gönüllü katılımcı üzerinden toplanan Ekstansör digitorum ve Fleksör carpi radialis kaslarının EMG verileri kullanılarak elin açık (parmakların ekstansiyonu) ve kapalı (parmakların fleksiyonu) konumları tahmin edilmiştir. EMG sinyallerini doğru bir şekilde ayırt etmek için varyans, standart sapma, kök ortalama kare, ortalama enerji, minimum ve maksimum özellikler gibi çeşitli istatistiksel ölçütler kullanılmıştır. Bu ilave özelliklerle birlikte elde edilen veri seti Destek Vektör Makinesi (SVM), K-En Yakın Komşu (KNN), Karar Ağacı (DT) ve Gaussian Naive Bayes (GNB) algoritmalarına uygulanmış ve elin açık-kapalı konumları sınıflandırılmıştır. Sınıflandırma başarısı en yüksek algoritmanın SVM (max. 73.1%), en düşük algoritmanın ise KNN (min. 55.9%) olduğu görülmüştür. Gelecekteki çalışmalarda tahmin doğruluğunu daha da artırmak için daha fazla kas grubundan veri toplanması önerilmektedir.

Keywords

EMG, el kasları, ikili sınıflandırma, destek vektör makineleri

Electromyography based hand movement classification and feature extraction using machine learning algorithms

Abstract

The categorization of hand gestures holds significant importance in controlling orthotic and prosthetic devices, enabling human-machine interaction, and facilitating telerehabilitation applications. For many years, methods of motion analysis based on image processing techniques have been employed to detect hand motions. However, recent research has focused on utilizing muscle contraction for detecting hand movements. Specifically, there has been an increase in studies that classify hand movements using surface electromyography (sEMG) data from the muscles of the hand and arm. In our study, we estimated the open (extension of the fingers) and closed (flexion of the fingers) positions of the hand by analyzing EMG data obtained from 4 volunteer participants' Extensor digitorum and Flexor carpi radialis muscles. In order to accurately discriminate EMG signals, various statistical measures such as variance, standard deviation, root mean square, average energy, minimum and maximum features were utilized. The dataset containing these additional features was then subjected to classification algorithms including Support Vector Machines (SVM), K Nearest Neighbour (KNN), Decision Tree (DT), and Gaussian Naive Bayes (GNB) for the purpose of classifying hand positions into open or closed states. Among the tested algorithms, SVM achieved the highest success rate with a maximum accuracy of 73.1%, while KNN yielded the lowest success rate at a minimum accuracy of 55.9%. To further enhance prediction accuracy in future studies, it is suggested that data from a larger set of muscles be collected.

Keywords

EMG, hand mucle, binary classification, support vector machines (SVM)

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