Myoelectric digit action decoding with multi-output, multi-class classification: an offline analysis.

ABSTRACT: The ultimate goal of machine learning-based myoelectric control is simultaneous and independent control of multiple degrees of freedom (DOFs), including wrist and digit artificial joints. For prosthetic finger control, regression-based methods are typically used to reconstruct position/velocity trajectories from surface electromyogram (EMG) signals. Unfortunately, such methods have thus far met with limited success. In this work, we propose action decoding, a paradigm-shifting approach for independent, multi-digit movement intent prediction based on multi-output, multi-class classification. At each moment in time, our algorithm decodes movement intent for each available DOF into one of three classes: open, close, or stall (i.e., no movement). Despite using a classifier as the decoder, arbi

SUBMITTER: Krasoulis A 

PROVIDER: S-EPMC7547112 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature