  The grip force required to handle an object depends on the object's mass and
the friction coefficient of its surface. The control of grip force in
myoelectric prosthesis is crucial for handling objects adequately. In the
current paper we propose a new method for improving the proportional and
continuous grasping force estimation to improve control systems for myoelectric
prosthesis based on surface electromyography (sEMG) recordings. For this
purpose, we develop an approach based on multivariable system identification in
the state-space (SS) and continuous force estimation with Kalman Filter (KF).
The sEMG recordings of ten healthy individuals performing a grip task were used
as data set for model identification. The root mean square (RMS), the mean
absolute value (MAV), and the waveform length (WL) extracted from the sEMG
signals were used at the model's input while the measured grasping force was
the output. The performance of the method was evaluated with the normalized
root-mean-squared-error (NRMSE) and the square of the Pearson's correlation
coefficient ($ R^2 $). We found the $R^2$ and NRMSE values were 0.92$ \pm $
0.0319 and 0.723$ \pm $ 0.0563, respectively. The performance of the proposed
technique was superior to the results obtained with other regression models,
such as the recurrent nonlinear autoregressive exogenous (NARX)-based neural
network, the multi-layer perceptron (MLP) network and the linear discriminant
analysis (LDA) with a quadratic polynomial fitting (QPF). The results confirm
that the method is adequate for real-time applications with myoelectric hand
prostheses.
