A comparison of open-loop and closed-loop adaptive calibration for pattern recognition based myoelectric control.

Abstract

This study presented a closed-loop adaptive calibration (CLAC) scheme where subjects could get instantaneous feedback of their movements and alter their motions immediately to update the model parameters to enhance its ability. The real-time performance was compared between the conventional open-loop calibration (OLC) and the presented CLAC based on three metrics (motion-selection time, motion-completion time and motion-completion rate). The CLAC performed slightly better than the OLC, but the difference was not significant. This was the first study designed to investigate the effects of CLAC for pattern recognition-based myoelectric control (discrete movement). The CLAC could be potentially applied in the multiuser interface to make the adaptation of the common model to a novel user efficiently and flexibly.