Evaluating task characteristics as predictors of continuous hand motion decoding performance

Abstract

Prosthetic hand control using biosignals requires robust decoding across diverse real-world conditions. We systematically evaluated how task characteristics influence muscular activity-based continuous hand motion decoding performance using the MyoKi database. Multiple linear regression with forward selection revealed that muscle fatigue modestly predicted task-wise decoding accuracy. Categorical task characteristics, such as grasp type, vertical task location, and force level, showed no significant effects on within-participant decoding performance in repeated measures ANOVAs, which demonstrates that the decoder maintains stable performance across diverse task characteristics. This is promising for prosthetic applications requiring robust performance across daily activities.