Evaluating internal model strength and performance of myoelectric prosthesis control strategies

Abstract

On-going developments in myoelectric prosthesis control have provided prosthesis users with an assortment of control strategies that vary in reliability and performance. Many studies have focused on improving performance by providing feedback to the user but have overlooked the effect of this feedback on internal model development, which is key to improve long-term performance. In this paper, the strength of internal models developed for two commonly used myoelectric control strategies: Raw control with raw feedback (using a regression-based approach) and filtered control with filtered feedback (using a classifier-based approach), were evaluated using two psychometric measures: Trial-by-trial adaptation and just-noticeable difference. The performance of both strategies was also evaluated using Schmidt's style target acquisition task. Results obtained from 24 able-bodied subjects showed that although filtered control with filtered feedback had better short-term performance in path efficiency (p < 0.05), raw control with raw feedback resulted in stronger internal model development (p < 0.05), which may lead to better long-term performance. Despite inherent noise in the control signals of the regression controller, these findings suggest that rich feedback associated with regression control may be used to improve human understanding of the myoelectric control system.