Prediction Speed of Hand Open-Close By Using Neural Network

Abstract

In this paper, an prediction speed method of hand open-close by using the Artificial Neural Network (ANN) surface electromyography (sEMG) signal is presented. The first step of this method is to analyze sEMG signal detected from the subject's right upper forearm and extract features using the mean absolute value (MAV), the root mean square (RMS), the variance (VAR), the standart deviation (STD), the median frekans of power spectrum (MDF), the mean frekans of PS (MNF), the maximum frekans of PS (MAXF). The second step is to import the feature values into an ANN to identify the speed of hand open-close (SHOC). Based on the results of experiments, it is concluded that this method is effective in prediction of SHOC.