Performance comparison of artificial neural network and Gaussian mixture model in classifying hand motions by using sEMG signals

• Autorzy: Baspinar U. , Varol H. S. , Senyurek V. Y.
• Języki publikacji: EN

Abstrakty

EN

In this study, a home-made four channel sEMG amplifier circuit was designed for measuring of sEMG signals. The measured sEMG signals were recorded on to a computer with help of a DAQ board. The recorded sEMG signals were filtered first with a high-pass filter and afterwards a wavelet based filtering was applied to remove unwanted noises. Before applying of the wavelet based filtering, it was first determined which wavelet type, threshold selection rule and threshold would be suitable for the denoising process. As a second step, the recorded and denoised signals’ features were extracted. For classification of motions 8 time domain and 2 frequency domain features were used individually and in combinations. Lastly, seven different motions were classified and their classification performances were compared. In this study, classification rates of ANN and GMM classifiers were compared as regards features.

Słowa kluczowe

EN

hand motion classification; artificial neural network; Gaussian mixture model

PL

klasyfikacja ruchu dłoni; sztuczna sieć neuronowa; model mikstur gaussowskich

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2013
• Tom: Vol. 33, no. 1
• Strony: 33--45
• Opis fizyczny: Bibliogr. 22 poz., rys., tab., wykr.

Twórcy

autor

Baspinar U.

• Technical Education Faculty, Electronics & Computer Education Department, Marmara University, Istanbul, Turkey

autor

Varol H. S.

• Technical Education Faculty, Electronics & Computer Education Department, Marmara University, Istanbul, Turkey

autor

Senyurek V. Y.

• Technical Education Faculty, Electronics & Computer Education Department, Marmara University, Istanbul, Turkey

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