Classification of agressive action EMG signals by AR based k-NN method

The fields of EMG signal processing technology has been effective in the application of prosthetic control and clinical medicine or sport science. The main purpose of this study is to classify two aggressive action EMG signals which are taken from different people, according to their texture feature vectors. The physical action EMG set is derived from UCI database. The power spectral density (PSD) estimation of EMG signals is calculated by using AR Burg Method. The texture feature vectors of EMG signals are extracted by applying statistical methods to PSD maps of each signal. PSD based feature vectors are given to different type of k-NN classifier as inputs and the performance results of each system are compared. Finally, the best average performance is observed as 97.92 % in k=7, 9 and 10 neighbors structure of k-NN classifier.