Stochastic sEMG processor based manipulator control toward man-machine interface with minimal electro-mechanical delay

Inspired from Hogan's myoelectric processor in 1980, this study presents a stochastic sEMG processing method to estimate the muscle activation level for manipulator control. Hogan's previous study showed the feasibility to estimate the muscle activation level with multi-channel sEMG under static force condition. However, it is difficult to continuously estimate muscle activation during dynamic contraction because of the nonlinear effects by the time-varying nature of sEMG. To enhance the performance of high SNR and rapid response in force-varying contraction, we propose a new method with statistical analysis extended from a whitening method of Hogan's study. The signals from eight sEMG channels were used to estimate the muscle activation level during isometric force-varying contractions. Experimentally, a two-DoF manipulator was controlled by input signals from the estimated muscle activation signal.