Effect of User Practice on Prosthetic Finger Control With an Intuitive Myoelectric Decoder.

ABSTRACT: Machine learning-based myoelectric control is regarded as an intuitive paradigm, because of the mapping it creates between muscle co-activation patterns and prosthesis movements that aims to simulate the physiological pathways found in the human arm. Despite that, there has been evidence that closed-loop interaction with a classification-based interface results in user adaptation, which leads to performance improvement with experience. Recently, there has been a focus shift toward continuous prosthesis control, yet little is known about whether and how user adaptation affects myoelectric control performance in dexterous, intuitive tasks. We investigate the effect of short-term adaptation with independent finger position control by conducting real-time experiments with 10 able-bodied and tw

SUBMITTER: Krasoulis A 

PROVIDER: S-EPMC6747011 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature