Browsing by Author "Dönmez, Hayriye"

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Konvolüsyonel sinir ağını kullanarak EMG sinyallerinin sınıflandırılması
(2020) Dönmez, Hayriye; Bakırcıoğlu, Kaan; Özkurt, Nalan
An electrical signal is produced by the contraction of the muscles, this electrical signal contain information about the muscles, the recording of these signals called electromyography (EMG). This information is often used in studies such as prosthetic arm, muscle damage detection and motion detection. Classifiers such as artificial neural networks, support vector machines are generally used for classification of EMG signals. Despite successful results with such methods the extraction of the features to be given to the classifiers and the selection of the features affect the classification success. In this thesis, it is aimed to increase the classification success of the daily used hand movements using the Convolutional Neural Networks (CNN), which is one of the machine learning methods. The advantage of the deep learning methods like CNN is that the relationships in big data are learned by the network. Firstly, the received EMG signals for forearm are windowed to increase the number of data and focus on the contraction points. Then, to compare the success rate, raw signals, Fourier transform of the signal, the root mean square and the Empirical Mode Decompositions (EMD) of the signals are given to four different CNN. Afterwards, to find the most efficient parameters, the results were obtained by dividing data set into three as 70% training set, 15% validation set and 15% test set. In order to test the performance of the system, 5-fold cross validation was applied. The best results are obtained from the CNN, which receive the EMD applied signal as input. Final results obtained with the cross validation is 95.90%, where 93.70% accuracy is reached without cross-validation. When the results were examined, it was seen that the designed CNN were successful in the classification of the EMG signals.