A hybrid adaptive data fusion with linear and nonlinear models for skeletal muscle force estimation

Position and force control are two critical aspects of prosthetic control. Surface electromyographic (sEMG) signals can be used for skeletal muscle force estimation. In this paper, skeletal muscle is considered as a system and System Identification (SI) is used to model sEMG and skeletal muscle force. The recorded sEMG signal is filtered utilizing optimized nonlinear Half-Gaussian Bayesian filter, and a Chebyshev type-II filter prepares the muscle force signal. The filter optimization is accomplished using Genetic Algorithm (GA). Multi-linear and nonlinear models are obtained with sEMG as input and skeletal muscle force of a human hand as an output. The outputs of these models are fused with a probabilistic Kullback Information Criterion (KIC) for model selection and an adaptive probability of KIC. This approach gives good estimate of the skeletal muscle force.