Hand kinematics estimation to control prosthetic devices: a nonlinear approach for simultaneous and proportional estimation of 15 DoFs

Abstract

The new generation of prosthetic devices are based on simultaneous and proportional estimation of kinematics from recorded surface electromyographic (sEMG) signals of the desire limb. In this paper we applied Generalized Regression Neural Network (GRNN), a non-linear system identification approach, to estimate fingers kinematics (15 Degrees of Freedom) from sEMG signals. The parameters were optimized based on training data of 40 subjects during 9 hand’s principal movements. In order to reduce the input parameters of the model in a feature selection, suitable features such as auto regressive coefficients, zero crossing, slope sign change, waveform length, root mean square, and discrete wavelet transform were computed from sEMG signal. The performance of the estimation was assessed based on Pearson correlation coefficient or R-value index. The average overall Rvalue for 15 DoFs in all the subjects was 87.84±5.02%, comparable with the state of the art approaches in the literature. As the proposed method and set-up use dataglove to record kinematic information, thus has more realistic data acquisition protocol which has potential to be used in clinical setting to provide fast, accurate, and intuitive simultaneous and proportional control strategy for myoelectric hand prostheses.