Semi-supervised adaptation of upper-limb myoelectric pattern recognition prosthesis control through virtual gameplay

Advanced machine learning algorithms can adapt to variation in new data inputs. Such adaptive algorithms have been employed on myoelectric pattern recognition control systems to improve upper-limb prosthesis performance. When training their control system, prosthesis users typically attempt to make consistent and repeatable muscle contractions. However, minimizing input data variation does not always resemble realistic usage scenarios as several factors (muscle fatigue, limb position, electrode shift, etc.) can contribute to changes in the characteristics of the muscle signals that could lead to poor controller performance. While it may be difficult to account for all the possible variation, prosthesis users may benefit from training that better mimics real-life prosthesis use. This paper investigates the use of virtual games, developed for practicing specific aspects of myoelectric prosthesis control, to adapt a linear discriminant analysis (LDA) model in a semi-supervised manner. Results from offline analysis of virtual game data collected across two weeks showed that classification error rates were better for 7 out of 10 prosthesis users when applying an adaptive LDA model compared to a traditional non-adaptive LDA model. We also compare these results to an alternative model in which we apply a heuristic set of rules to identify and relabel “misclassified” predicted outputs during virtual game play before evaluating the classification performance of an adaptive LDA classifier with re-labeled inputs. Virtual games are a promising clinical tool which can be applied to better learn the user's control preferences under simulated use conditions. Further development of this work could impact daily prosthesis use and performance for those who use myoelectric pattern recognition-controlled prostheses.