Traditional electromyopgrahic (EMG) measurements are based on single sensor information. Due to the arrangement of skeletal muscle fibers for hand motions, cross talk is an inherent problem when inferring motion/force potentials from EMG data. This paper studies means of using sensor arrays to infer better motion/force potential for prosthetic hands. In particular, a surface electromyographic (sEMG) sensor array is used to investigate multiple model fusion techniques. This paper provides a comparison between three statistical model selection criteria. The sEMG signals are pre-processed using four filters, Butterworth, Chebyshev type-II, as well as Bayesian filters such as the Exponential and Half-Gaussian filter. Output Error (OE) models were extracted from sEMG data and hand force data and compared using a Bayesian based fusion model. The four different filters effect were quantified based on the OE models performance in matching the actual measured data. The comparison indicates a preference for using the sensor fusion technique with preprocessed EMG data using the Half-Gaussian Bayesian filter and the Kullback Information Criterion (KIC).

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