Recalibration of myoelectric control with active learning

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....

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Detalles Bibliográficos
Autores principales: Szymaniak, Katarzyna, Krasoulis, Agamemnon, Nazarpour, Kianoush
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Neuroscience
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9797496/
https://www.ncbi.nlm.nih.gov/pubmed/36590085
http://dx.doi.org/10.3389/fnbot.2022.1061201

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9797496/
https://www.ncbi.nlm.nih.gov/pubmed/36590085
http://dx.doi.org/10.3389/fnbot.2022.1061201

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