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Recalibration of myoelectric control with active learning

Lookup NU author(s): Dr Agamemnon Krasoulis, Professor Kianoush Nazarpour



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Copyright © 2022 Szymaniak, Krasoulis and Nazarpour. Introduction: Improving the robustness of myoelectric control to work over many months without the need for recalibration could reduce prosthesis abandonment. Current approaches rely on post-hoc error detection to verify the certainty of a decoder's prediction using predefined threshold value. Since the decoder is fixed, the performance decline over time is inevitable. Other approaches such as supervised recalibration and unsupervised self-recalibration entail limitations in scaling up and computational resources. The objective of this paper is to study active learning as a scalable, human-in-the-loop framework, to improve the robustness of myoelectric control. Method: Active learning and linear discriminate analysis methods were used to create an iterative learning process, to modify decision boundaries based on changes in the data. We simulated a real-time scenario. We exploited least confidence, smallest margin and entropy reduction sampling strategies in single and batch-mode sample selection. Optimal batch-mode sampling was considered using ranked batch-mode active learning. Results: With only 3.2 min of data carefully selected by the active learner, the decoder outperforms random sampling by 4–5 and ~2% for able-bodied and people with limb difference, respectively. We observed active learning strategies to systematically and significantly enhance the decoders adaptation while optimizing the amount of training data on a class-specific basis. Smallest margin and least confidence uncertainty were shown to be the most supreme. Discussion: We introduce for the first time active learning framework for long term adaptation in myoelectric control. This study simulates closed-loop environment in an offline manner and proposes a pipeline for future real-time deployment.

Publication metadata

Author(s): Szymaniak K, Krasoulis A, Nazarpour K

Publication type: Article

Publication status: Published

Journal: Frontiers in Neurorobotics

Year: 2022

Volume: 16

Online publication date: 15/12/2022

Acceptance date: 21/02/2022

Date deposited: 12/01/2023

ISSN (electronic): 1662-5218

Publisher: Frontiers Media SA


DOI: 10.3389/fnbot.2022.1061201


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