Veri Madenciliği Süreç Modeli ile El Hareketlerinin Myoelektrik Kontrolü

Yüzey elektromiyogram (EMG) sinyali, zengin motor kontrol bilgilerini içeren bir non-ninvaziv ölçümdür. Myoelektrik sinyal olarak da adlandırılan bu sinyaller, Myoelektrik kontrol olarak bilinen güç protez kontrolü için önemli bir girdidir. Bu sinyaller durağan olmayan bir yapıya sahiptir. Bu nedenle bu sinyallerden anlamlı bir bilgi keşfi yapmak için iyi bir analiz yöntemine ihtiyaç vardır. Bu çalışmada, bu amaç için veri madenciliği tekniklerini kullanan bir karar destek sistemi geliştirilmiştir. Veri madenciliği metodolojisi olarak Çapraz Endüstri Standart Süreci (CRISP-DM) yaklaşımı kullanılmıştır. Veri hazırlama aşamasında entropi tabanlı öznitelikler kullanıldı. 8 kanal EMG sinyallerinin kullanıldığı çalışmada her kanaldan 8 entropi tabanlı öznitelik elde edildi. Modelleme aşamasında etkili ve hızlı bir sınıflandırma algoritması olan destek vektör makinesi (DVM) kullanılmıştır. Performans değerlendirme aşamasında sınıflandırma doğruluğu, kappa istatistik değeri, ortalama mutlak hata ve kök ortalama kare hatası ölçütleri kullanıldı. Deneysel sonuçlar, önerilen yöntem ile elde edilen sonuçların literatürdeki yöntemlerden daha iyi sonuçlar verdiğini göstermektedir. Geliştirilen bu sistem, ilgili alandaki uzman kişilere yardımcı olabilecek bir karar destek sistemi olarak kullanılabilir.Anahtar Kelimeler: Veri madenciliği, karar destek sistemleri, myoelektrik kontrol, EMG sınıflandırma, CRISP-DM modeli

Anahtar Kelimeler:

Veri madenciliği, myoelektrik kontrol, EMG sınıflandırma, CRISP-DM modeli

Myoelectric Control of Hand Movements Using Data Mining Process Model

Surface electromyography (EMG) signal is a noninvasive measurement with rich motor control information. These signals which are also called as Myoelectric signal, is an important input for power prostheses control known as myoelectric control. These signals have non-stationary structure. Therefore, a good analysis method is required to make a meaningful knowledge discovery. In this study, a decision support system which uses data mining techniques has been developed for this purpose. Cross Industry Standard Process for Data Mining (CRISP-DM) approach has been used as data mining methodology. Entropy-based features have been used during data preparation stage. In the study in which 8-channel EMG signals are used, 8 entropy-based features have obtained from each channel. Support vector machine which is a fast and effective classification algorithm has used in the modeling phase. Classification accuracy, kappa statistic value, mean absolute error (MAE) ve root square mean error (RMSE) have been used in performance evaluation stage. Experimental results show that the results obtained with the proposed method are better than the results obtained with the methods in the literature. The developed system can be used as a decision support system that could help to the experts in related field.

Keywords:

Data mining, myoelectric control, EMG classification, CRISP-DM model,

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