EVALUATING THE PERFORMANCE OF EMG BASED BACK PROPAGATION NEURAL NETWORK (BPNN) TIME DOMAIN FEATURES AND TIME ADVANCE FEATURES FOR KNEE JOINT ANGLE ESTIMATION

One of the challenges in lower limb rehabilitation robot design and control for assistance as needed robot is the interaction between the robot and the user. This can be overcome by quantifying the amount of assistance needed by the patient. Which attributed to the precision and accuracy of the estimated knee joint angle by the controller. In this research, the back propagation neural network (BPNN) was trained to estimate the amount of compensation needed by the patients who served as input to the robot. The Electromyogram (EMG) signals, time domain (TD) and time advance domain (TAD) were considered and root means square (RMS), mean absolute value (MAV), variance (VAR) and standard deviation (STD) features were extracted from each domain. Fourteen healthy subjects accepted to validate the experimental setup. However, the best eight subjects data were consider for this article. The features were used separately for training BPNN and the performance of each feature was evaluated. The EMG signals of the protagonist muscle (vastus lateralis) and antagonist muscles (semitendinosus) were used as the input signal to the BPNN after processing the signals. The electro-goniometer reading of the knee joint angle was used as the output to the network. The features were used separately for training BPNN and the performance of each feature was evaluated. The TAD RMSE values were significantly reduced compared to TD with 37.34% , 35.32% , 34.87% and 31.22% for RMS, MAV, VAR and STD features respectively. The result shows that the TAD features have better performance for estimating the knee joint angle.