The endpointstiffness of the humanarm has been long recognized as a key factor in the smooth contact between humans and environment. And the endpointstiffness of the humanarm is highly correlated with the surface ...
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The endpointstiffness of the humanarm has been long recognized as a key factor in the smooth contact between humans and environment. And the endpointstiffness of the humanarm is highly correlated with the surface electromyography(semg) produced by the contraction of the muscles. In this paper, the geneexpressionprogramming(GEP) Algorithm is proposed to estimate the endpointstiffness of humanarmbased on s EMG. This paper improves the traditional decoding method of GEP. Instead of generating an expression tree, it is directly decoded by looking for the effective length of the gene. And experimental results show that nonlinear models such as GEP models in this paper have higher correlation and lower RMSE(root mean square error) than regression stiffnessusing linear regression models. Selecting different feature of EMG signals, the correlation coefficient and the root mean square error of the model is very different. For the GEP model in this paper, WPTSVD(Wavelet Package Transform Singular Value Decomposion) and WTSVD(Wavelet Transform Singular Value Decomposion) are selected as the feature of s EMG signals have high performances and the correlation can reach 57% ±12.1%.
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