Online unsupervised adaptation of latent representation for myoelectric control during user-decoder co-adaptation
Author(s)
Type
Journal Article
Abstract
Myoelectric control interfaces, which map electromyographic (EMG) signals into control commands for external devices, have applications in active prosthesis control. However, the statistical characteristics of EMG signals change over time (e.g., because of changes in the electrode location), which makes interfaces based on static mapping unstable. Thus the user-decoder co-adaptation is needed during online operations. Nevertheless, current online decoder adaptation approaches present several practical challenges, such as expensive data labeling and slow convergence. Thus we introduce an unsupervised decoder adaptation method that converges rapidly. We use an autoencoder to extract motor intent representation in the latent manifold space rather than the sensor space, and further introduce an online unsupervised adaptation scheme based on Moore-Penrose Inverse, a noniterative approach suited for fast network re-training, to track the evolving manifold. A validation experiment first showed that the convergence time of the proposed adaptation scheme was reduced to about 50% of that for state-of-the-art methods. Online experiments further evaluated cursor and prosthetic hand control by the proposed myocontrol interface, where perturbations were representatively introduced by shifting the electrodes. Results showed that our scheme reached comparable improvements in robustness as supervised counterparts. Moreover, in a cup relocation test with a prosthetic hand, the completion time in the post-adaptation phase with electrode shift was comparable to that in the baseline phase without shift. These results suggest that our method effectively improves the accessibility and reliability of decoder adaptation, which has the potential to reduce the translational gap of myoelectric control interfaces by effective co-adaptation during operation.
Date Issued
2025-02-26
Date Acceptance
2025-02-22
Citation
IEEE transactions on neural systems and rehabilitation engineering, 2025, 33, pp.1026-1037
ISSN
1534-4320
Publisher
IEEE
Start Page
1026
End Page
1037
Journal / Book Title
IEEE transactions on neural systems and rehabilitation engineering
Volume
33
Copyright Statement
© 2025 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
License URL
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/40031560
Subjects
decoder adaptation
elec- trode shift
Engineering
Engineering, Biomedical
INTERFACE
Life Sciences & Biomedicine
Myoelectric control
online manifold learning
Rehabilitation
ROBUST
Science & Technology
Technology
unsupervised autoencoder
Publication Status
Published
Coverage Spatial
United States
Date Publish Online
2025-02-26