Effects of Confidence-Based Rejection on Usability and Error in Pattern Recognition-Based Myoelectric Control

Rejection of movements based on the confidence in the classification decision has previously been demonstrated to improve the usability of pattern recognition based myoelectric control. To this point, however, the optimal rejection threshold has been determined heuristically, and it is not known how different thresholds affect the tradeoff between error mitigation and false rejections in real-time closed-loop control. To answer this question, 24 able-bodied subjects completed a real-time Fitts' law-style virtual cursor control task using a support vector machine classifier. It was found that rejection improved information throughput at all thresholds, with the best performance coming at thresholds between 0.60 and 0.75. Two fundamental types of error were defined and identified: operator error (identifiable, repeatable behaviors, directly attributable to the user), and systemic error (other errors attributable to misclassification or noise). The incidence of both operator and systemic errors were found to decrease as rejection threshold increased. Moreover, while the incidence of all error types correlated strongly with path efficiency, only systemic errors correlated strongly with throughput and trial completion rate. Interestingly, more experienced users were found to commit as many errors as novice users, despite performing better in the Fitts' task, suggesting that there is more to usability than error prevention alone. Nevertheless, these results demonstrate the usability gains possible with rejection across a range of thresholds for both novice and experienced users alike.