a nonlinear method to estimate simultaneous force pattern generated by hand fingers; application in prosthetic hand

In this study a method has been introduced to map the features extracted from the recorded electromyogram signals from the forearm and the force generated by the fingers. in order to simultaneously record of semg signals and the force produced by fingers, 9 requested movements of fingers conducted by 10 healthy people. estimation was done for 6 degrees of freedom (dof) and generalized regression neural network (grnn) was selected for system training. the optimal parameters, including the length of the time windows, the parameters of the neural network, and the characteristics of the semg signal were calculated to improve the performance of the estimate. the performance was obtained based on r^2 criterion. the total value of r^2 for 6 dof was 92.8±5.2% that obtained by greedy looking system parameters in all the subjects. the result shows that proposed method can be significant in simultaneous myoelectric control.