Grasping force estimation using state-space model and Kalman filter

The grip force required to handle an object depends on it mass and the friction between the skin and the object. The control of grip force in myoelectric prosthesis is crucial for handling objects adequately. The current paper proposes a method for improving the estimation of grip force in myoelectric prosthesis based on surface myoelectric (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 and the measured grasping force was the output. The performance of the proposed method was evaluated using the normalized root-mean-squared-error (NRMSE) and the square of Pearson's correlation coefficient ($ R^2 $). In this study, the CC and NRMSE values were 0.92$ \pm $ 0.0319 and 0.723$ \pm $ 0.0563, respectively. The performance of the system was superior to results obtained with a recurrent nonlinear autoregressive exogenous (NARX)-based neural network and the multi-layer perceptron (MLP) network. The results confirmed that the method is an excellent tool for real-time applications with hand prostheses.