A comparison of classification based confidence metrics for use in the design of myoelectric control systems.

Abstract

In many pattern recognition applications, confidence scores are used to extract more information than discrete class membership alone, yet they have not traditionally been leveraged in myoelectric control. In this work, the confidence scores of eight common classification schemes were examined. Their role in rejecting inadvertent motions is investigated, and the tradeoffs observed in the design of rejection capable control schemes are demonstrated. It is shown that the distribution of confidences can varying greatly between classifiers, even when classification performance is similar. As a specific example, an ensemble of support vector machines in a one against one configuration (SVM1vs1) outperforms the previously reported LDAR myoelectric pattern recognition rejection scheme in terms of accuracy-rejection curves (ARC) and false acceptance/rejection (FAR) curves.