System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Adaptive multi sensor based nonlinear identification of skeletal muscle force
WSEAS TRANSACTIONS on SYSTEMS
ICS'10 Proceedings of the 14th WSEAS international conference on Systems: part of the 14th WSEAS CSCC multiconference - Volume I
A small sample model selection criterion based on Kullback's symmetric divergence
IEEE Transactions on Signal Processing
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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 pre-programmed 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.