Closed-Loop Acquisition of Training Data Improves Myocontrol of a Prosthetic Hand

Abstract

Modern myocontrol of prosthetic upper limbs employs pattern recognition models to map the muscular activity of the residual limb onto control commands for the prosthesis. The quality of pattern-recognition-based myocontrol, and that of the resulting user experience, depend on the quality of the data used to build the model. Surprisingly, the prosthetic community has so far given marginal attention to this aspect, especially as far as the involvement of the user in the data acquisition process is concerned. This work shows that closed-loop data acquisition strategies using a feedback-aided approach outperform the standard open-loop acquisition by helping users detect areas of the input space that need more training data. The experiment was conducted in realistic settings, involving one prosthetic hand and tasks inspired by activities of daily living.

This work was partially supported by the DFG project Deep-Hand, CA1389/1-2.