Adaptive Finger Angle Estimation from sEMG Data with Multiple Linear and Nonlinear Model Data Fusion

摘要

This paper presents a novel approach to control the motion of a smart prosthesis using surface electromyographic (sEMG) signals. Currently, all sEMG based prosthetic hands are controlled based on preprogrammed motion sets, which are initiated when some threshold value of the measured sEMG signal is reached. In this paper, we present an approach that utilizes System Identification (SI) in order to obtain a dynamic finger angle model. Such a model allows for instantaneous control for the finger motions. The algorithm presented relays on an array of nine sEMG sensors. The sEMG and angle data is filtered using a nonlinear Teager-Kaiser Energy (TKE) operator based nonlinear spatial filter and a Chebyshev type-Ⅱ filter respectively. The filtered signals are smoothed using a smoothing spline curve fitting. The smoothed sEMG data is used as input and the respective smoothed finger angle data is used as output for a system identification routine to obtain multiple linear and nonlinear models. To achieve better estimates of the finger angles, an adaptive probabilistic Kullback Information Criterion (KIC) for model selection based data fusion algorithm is applied to the linear and nonlinear model's outputs. Final fusion based output of this approach results in improved estimates of finger angles.