User Training for Pattern Recognition-Based Myoelectric Prostheses: Improving Phantom Limb Movement Consistency and Distinguishability

We assessed the ability of four transradial amputees to control a virtual prosthesis capable of nine classes of movement both before and after a two-week training period. Subjects attended eight one-on-one training sessions that focused on improving the consistency and distinguishability of their hand and wrist movements using visual biofeedback from a virtual prosthesis. The virtual environment facilitated the precise quantification of three prosthesis control measures. During a final evaluation, the subject population saw an average increase in movement completion percentage from 70.8% to 99.0%, an average improvement in normalized movement completion time from 1.47 to 1.13, and an average increase in movement classifier accuracy from 77.5% to 94.4% (p<;0.001). Additionally, all four subjects were reevaluated after eight elapsed hours without retraining the classifier, and all subjects demonstrated minimal decreases in performance. Our analysis of the underlying sources of improvement for each subject examined the sizes and separation of high-dimensional data clusters and revealed that each subject formed a unique and effective strategy for improving the consistency and/or distinguishability of his or her phantom limb movements. This is the first longitudinal study designed to examine the effects of user training in the implementation of pattern recognition-based myoelectric prostheses.