Proportional myoelectric prosthetic hand control using multi-regression model estimator with pattern classifier selector

摘要

Myoelectric hand prostheses using pattern recognition control scheme lack simultaneous motion, and perform robotic unnatural inter-pattern motion. Accordingly, the use of regression models for the estimation of hand kinematics proportionally to sEMG (surface electromyography) signals has proved more simultaneous and natural motion. The objective of this study is to introduce proportional speed control on robotic hand motion, where each finger has its own estimator model to achieve non-robotic performance of the hand. Each finger has four regression models to cover the motion of the finger over four patterns. A pattern recognition classifier is trained to classify four hand gestures, accordingly, the regression models of the fingers is to be altered according to the classifier decision. Commercial sEMG sensing armband was used in the acquisition of training data that can be used later in the development of the prosthetic control system. The reproduction of data for linear (least-square fitted model) and non-linear (ANN) regression models are investigated, where the ANN proved better reproducibility of finger speeds. The models also are trained on reduced RMS features, where the selected features are only the channels that are allocated over the active muscles during performing the patterns which resulted reproducibility of 89.27±1.92%. These results demonstrate the robustness of the multi-regression models system over wide range of motion.