Differentiating Type of Muscle Movement via AR Modeling and Neural Network Classification

The aim of this study is to classify electromyogram (EMG) signals for controlling multifunction proshetic devices. An articial neural network (ANN) implementation was used for this purpose. Autoregressive (AR) parameters of a1;a2;a3;a4 and their signal power obtained from dierent arm muscle motions were applied to the input of ANN, which is a multilayer perceptron. At the output layer, for 5000 iterations, six movements were distinguished at a high accuracy of 97.6%.

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