A multi-variate approach to predicting myoelectric control usability
University of New Brunswick
Pattern recognition techniques leveraging the use of electromyography (EMG) signals have become a popular approach to provide intuitive control of myoelectric devices. Performance of these control interfaces is commonly quantified using offline classification accuracy, despite researchers having shown that this metric is a poor indicator of usability. Several attempts have been made to find alternative training metrics that better correlate with online performance. Moderate correlations have been identified in some cases; however, the relationship between offline training and online usability has yet to be fully defined in the literature. The following work attempts to bridge this information divide by exploring combinations of offline training metrics capable of predicting myoelectric control usability. The results indicate that linear combinations of three offline training metrics provide superior predictive power of future online performance. Additionally, the role of feedback presented to the user during training is explored to determine its effect on performance and predictability. The results of this study suggest that properly designed feedback mechanisms can influence both the quality of the training metrics and the predictive ability of the developed linear models.