Time Delay Neural Network to Estimate the Elbow Joint Angle Based on Electromyography

Elbow joint angle estimation is essential in the field of biomechanical engineering especially for an apparatus based on myoelectric control. The purpose of this study is to develop a model of electromyography (EMG) signal to elbow joint angle estimation using time delay neural network (TDANN). The EMG signals were recorded only from biceps muscle from ten healthy male subjects. In order to obtain the features, the EMG signal is extracted for every 100 samples using sign slope change (SSC) features. The EMG features are used as the training data, in order the TDANN able to recognize the elbow joint angle. The results of this study reveal that the performance of the estimation is better if it is compared to the other studies. The RMSE values for the continuous and random motion are 14.97°±5.17° and 18.69°± 2.76°, respectively. The Pearson correlation coefficients are 0.87± 0.0087 and 0.78±0.11 for continuous and random motion, respectively. The results have confirmed the usefulness of the proposed method to estimate the elbow joint angle.