Wrist and grasp myocontrol: Simplifying the training phase

Abstract

The term “myocontrol” denotes, in the assistive robotics / machine learning community, the feed-forward control of a dexterous prosthetic device enforced by a disabled human subject, typically an amputee, using the activation of remnant muscles. Myocontrol relies on a human-machine interface (HMI), which converts muscle activation signals of diverse nature into control commands for the prosthetic device. Although novel kinds of HMIs are being explored, the traditional basis for myocontrol is surface electromyography (sEMG), a technique which records the electrical field emitted by the muscles when contracting. Due to the complexity of the HMI, it is desirable to shorten the calibration procedure as much as possible whenever the prosthetic device has two or more degrees of freedom (DOFs). In this paper we extend the Linearly Enhanced Training (LET) procedure, already employed in myocontrol of single fingers and their combinations, to myocontrol of two DOFs of the wrist plus the action of grasping (hand opening and closing). The LET principle, according to which combined simultaneous activation of more than one DOF are artificially modelled using a simple linear combination of single-DOF activations, was tested on six intact subjects engaged in wrist flexion, extension, pronation and grasping. The experimental results show that LET can solve this problem with a similar level of accuracy as in the case of single fingers. As well, the LET hyperparameters are shown to be invariant across subjects.