Original Research ARTICLE
Effect of user adaptation on prosthetic finger control with an intuitive myoelectric decoder
- 1University of Edinburgh, United Kingdom
- 2University of Edinburgh, United Kingdom
- 3Newcastle University, United Kingdom
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 towards 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 two transradial amputee subjects. We demonstrate that despite using an intuitive decoder, experience leads to significant improvements in performance. We argue that this is due to the lack of an utterly natural control scheme, which is mainly caused by differences in the anatomy of human and artificial hands, movement intent decoding inaccuracies, and lack of proprioception. Finally, we extend previous work in classification-based and wrist continuous control by verifying that offline analyses cannot reliably predict real-time performance, thereby reiterating the importance of validating myoelectric control algorithms with real-time experiments.
Keywords: Electromyogarphy, Myoelectric prosthesis, motor learning, regression, upper-limb prosthesis, Finger control
Received: 22 Mar 2019;
Accepted: 08 Aug 2019.
Edited by:Jose L. Contreras-Vidal, University of Houston, United States
Reviewed by:Kazutaka Takahashi, University of Chicago, United States
Sean K. Meehan, University of Waterloo, Canada
Copyright: © 2019 Krasoulis, Vijayakumar and Nazarpour. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Dr. Agamemnon Krasoulis, University of Edinburgh, Edinburgh, United Kingdom, firstname.lastname@example.org