Neural network classification of supraspinatus muscle electromyography feature signal

In this paper, we propose to classify supraspinatus muscle sEMG feature data to be contraction and relaxation data type. We use lightweight computational process feature extraction using moving average of absolute value and moving sum of absolute value. We use Neural Network with two hidden layer to classify sEMG feature. Two sEMG feature data are extracted successfully. Neural Network can classify input feature into contraction and relaxation data type with 99.8% accuracy. 99.8% accuracy are obtained with 1100 epoch of first experiment, and 577 epoch of second experiment.