Real-time comparison of conventional direct control and pattern recognition myoelectric control in a two-dimensional Fitts' law style test

Abstract

Few studies have directly compared real-time control performance of pattern recognition to direct control for the next generation of myoelectric controlled upper limb prostheses. Many different implementations of pattern recognition control have been proposed, with minor differentiations in the feature sets and classifiers. An objective and generalizable evaluation tool quantifying the control performance, other than classification accuracy, is needed. This paper used the implementation of such a tool through the design of a target acquisition test, similar to a Fitts' law test, relating movement time of the target acquisition to the difficulty of the target, for a given control strategy. Performance metrics such as throughput (bits/sec), completion rate (%) and path efficiency (%) allow for a complete evaluation of the described strategies. We compared direct control and pattern recognition control with the proposed test and found that 1) the test was valid for control system evaluation by following Fitts' law with high coefficients of determination for both types of control and 2) that pattern recognition significantly outperformed direct control in throughput with similar completion rates and path efficiencies. In this framework, the present pilot study supports pattern recognition as a promising strategy and forms a basis for the development of a general and objective tool for the performance evaluation of upper limb control strategies.