Towards smart prosthetic hand: Adaptive probability based skeletan muscle fatigue model

Skeletal muscle force can be estimated using surface electromyographic (sEMG) signals. Usually, the surface location for the sensors is near the respective muscle motor unit points. Skeletal muscles generate a spatial EMG signal, which causes cross talk between different sEMG signal sensors. In this study, an array of three sEMG sensors is used to capture the information of muscle dynamics in terms of sEMG signals. The recorded sEMG signals are filtered utilizing optimized nonlinear Half-Gaussian Bayesian filters parameters, and the muscle force signal using a Chebyshev type-II filter. The filter optimization is accomplished using Genetic Algorithms. Three discrete time state-space muscle fatigue models are obtained using system identification and modal transformation for three sets of sensors for single motor unit. The outputs of these three muscle fatigue models are fused with a probabilistic Kullback Information Criterion (KIC) for model selection. The final fused output is estimated with an adaptive probability of KIC, which provides improved force estimates.