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Lookup NU author(s): Dr Sigrid DupanORCiD, Simon Stuttaford, Dr Matthew DysonORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Objective: Prosthesis control can be seen as a new skill to be learned. To enhance learning, both internal and augmented feedback are exploited. The latter represents external feedback sources that can be designed to enhance learning, e.g. biofeedback. Previous research has shown that augmented feedback protocols can be designed to induce retention by adhering to the guidance hypothesis, but it is not clear yet if that also results in transfer of those skills to prosthesis control. In this study, we test if a training paradigm optimised for retention allows for the transfer of myoelectric skill to prosthesis control. Approach: Twelve limb-intact participants learned a novel myoelectric skill during five one-hour training sessions. To induce retention of the novel myoelectric skill, we used a delayed feedback paradigm. Prosthesis transfer was tested through pre-and post-tests with a prosthesis. Prosthesis control tests included a grasp matching task, the modified box and blocks test, and an object manipulation task, requiring five grasps in total ('power', 'tripod', 'pointer', 'lateral grip', and 'hand open'). Main results: We found that prosthesis control improved significantly following five days of training. Importantly, the prosthesis control metrics were significantly related to the retention metric during training, but not to the prosthesis performance during the pre-test. Significance: This study shows that transfer of novel, abstract myoelectric control from a computer interface to prosthetic control is possible if the training paradigm is designed to induce retention. These results highlight the importance of approaching myoelectric and prosthetic skills from a skill acquisition standpoint, and open up new avenues for the design of prosthetic training protocols.
Author(s): Dupan S, Stuttaford S, Dyson M
Publication type: Article
Publication status: Published
Journal: Journal of Neural Engineering
Year: 2025
Volume: 22
Issue: 6
Online publication date: 23/12/2025
Acceptance date: 04/12/2025
Date deposited: 06/01/2026
ISSN (print): 1741-2560
ISSN (electronic): 1741-2552
Publisher: IOP Publishing Ltd
URL: https://doi.org/10.1088/1741-2552/ae2803
DOI: 10.1088/1741-2552/ae2803
Data Access Statement: The data cannot be made publicly available upon publication because they are not available in a format that is sufficiently accessible or reusable by other researchers. The data that support the findings of this study are available upon reasonable request from the authors.
PubMed id: 41343866
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