Impedance-Based Feedforward Learning Control for Natural Interaction between a Prosthetic Hand and the Environment

Abstract

Robotic prosthetic hands or arms often do not apply appropriate force and pressure and also do not have appropriate tactile and proprioceptive feedbacks as accurately and precisely as a human, which make the prosthetic arms less user-friendly and inconvenient. The lack of human-like tactile and proprioceptive feedbacks may also cause serious safety problems in the interaction between a prosthetic arm and the environment. This paper proposes a supervised learning-based solution to this problem associated with a support vector machine (SVM) classifier, which is to create a method that allows the synthetic hand or a prosthetic arm to apply forces to the environment (and react to the forces applied by the environment on the prosthetic arm in the form of tactile and proprioceptive forces or pressures) properly. As part of the entire goal, we create a glove instrumented with piezoelectric tactile sensors that fits over one of the hands, applies forces on the environment (an object grasped by a human subject wearing the glove) and records the applied forces/pressures along with proprioceptive and tactile feedbacks. In a simple user study, we subjectively evaluate the interaction between the environment and the human hand wearing the glove. Based on the user study results and the measured forces, we then outline a supervised learning algorithm to be applied with a support vector machine to classify the natural and unnatural interactions between the glove (potential prosthetic arm) and the object (environment). The learned (trained) algorithm is then proposed to be used to develop feedforward learning control for achieving human-like natural interactions between the prosthetic arm and the environment.