The Combinations of Velocity, Position, Force and EMG Signals for Human Intention Prediction

Abstract

The accuracy of human intention prediction in target reaching motion was compared for various combinations of velocity, position, force and EMG signals by using autoregressive (AR) and Radial basis function neural network (RBFNN) model. From the analysis, we found that for simple tasks the accuracy of intention prediction is higher when velocity and position signals are used.