An adaptive multi sensor data fusion with hybrid nonlinear ARX and Wiener-Hammerstein models for skeletal muscle force estimation

Abstract

Skeletal muscle force can be estimated using surface electromyographic (sEMG) signals. Usually, the sEMG location for the sensors is near the respective muscle motor unit points. Skeletal muscles generate a temporal and spatial distributed EMG signal, which causes cross talk between different sEMG signal sensors. In this paper, an array of three sEMG sensors is used to capture the information of muscle dynamics in terms of sEMG signals and generated muscle force. The recorded sEMG signals are 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 nonlinear Auto Regressive eXogenous (ARX) and Wiener-Hammerstein models with different nonlinearity estimators/classes are obtained using system identification (SI) for three sets of sensor data. The outputs of these models are fused with a probabilistic Kullback Information Criterion (KIC) for model selection and an adaptive probability of KIC. First, the outputs are fused for the same sensor and for different models and then the final outputs from each sensor. The final fusion based output of three sensors provides good skeletal muscle force estimates.