Adaptive multi sensor based nonlinear identification of skeletal muscle force

Skeletal muscle force and surface electromyographic (sEMG) signals are closely related. Hence, the later can be used for the force estimation. Usually, the location for the sEMG sensors is near the respective muscle motor unit points. EMG signals generated by skeletal muscles are temporal and spatially distributed which results in cross talk that is recorded by different sEMG sensors. This research focuses on modeling muscle dynamics in terms of sEMG signals and the generated muscle force. Here, an array of three sEMG sensors is used to capture the information of the muscle dynamics in terms of sEMG signals and generated muscle force. Optimized nonlinear Half-Gaussian Bayesian filters and a Chebyshev type-II filter are used for the filtration of the sEMG signals and the muscle force signal, respectively. A Genetic Algorithm is used for the optimization of the filter parameters. sEMG and skeletal muscle force is modeled using multi nonlinear Auto Regressive eXogenous (ARX) and Wiener-Hammerstein models with different nonlinearity estimators/classes using System Identification (SI) for three sets of sensor data. An adaptive probabilistic Kullback Information Criterion (KIC) for model selection is applied to obtain the fusion based skeletal muscle force for each sensor first and then for the final outputs from each sensor. The approach yields good skeletal muscle force estimates.