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Progressive unsupervised control of myoelectric upper limbs

Gigli, Andrea and Gijsberts, Arjan and Nowak, Markus and Vujaklija, Ivan and Castellini, Claudio (2023) Progressive unsupervised control of myoelectric upper limbs. Journal of Neural Engineering, 20 (6). Institute of Physics (IOP) Publishing. doi: 10.1088/1741-2552/ad0754. ISSN 1741-2560.

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Official URL: https://iopscience.iop.org/article/10.1088/1741-2552/ad0754

Abstract

Objective. Unsupervised myocontrol methods aim to create control models for myoelectric prostheses while avoiding the complications of acquiring reliable, regular, and sufficient labeled training data. A limitation of current unsupervised methods is that they fix the number of controlled prosthetic functions a priori, thus requiring an initial assessment of the user's motor skills and neglecting the development of novel motor skills over time. Approach. We developed a progressive unsupervised myocontrol (PUM) paradigm in which the user and the control model coadaptively identify distinct muscle synergies, which are then used to control arbitrarily associated myocontrol functions, each corresponding to a hand or wrist movement. The interaction starts with learning a single function and the user may request additional functions after mastering the available ones, which aligns the evolution of their motor skills with an increment in system complexity. We conducted a multi-session user study to evaluate PUM and compare it against a state-of-the-art non-progressive unsupervised alternative. Two participants with congenital upper-limb differences tested PUM, while ten non-disabled control participants tested either PUM or the non-progressive baseline. All participants engaged in myoelectric control of a virtual hand and wrist. Main results. PUM enabled autonomous learning of three myocontrol functions for participants with limb differences, and of all four available functions for non-disabled subjects, using both existing or newly identified muscle synergies. Participants with limb differences achieved similar success rates to non-disabled ones on myocontrol tests, but faced greater difficulties in internalizing new motor skills and exhibited slightly inferior movement quality. The performance was comparable with either PUM or the non-progressive baseline for the group of non-disabled participants. Significance. The PUM paradigm enables users to autonomously learn to operate the myocontrol system, adapts to the users' varied preexisting motor skills, and supports the further development of those skills throughout practice.

Item URL in elib:https://elib.dlr.de/202042/
Document Type:Article
Title:Progressive unsupervised control of myoelectric upper limbs
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Gigli, AndreaUNSPECIFIEDhttps://orcid.org/0000-0001-7049-485XUNSPECIFIED
Gijsberts, ArjanUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Nowak, MarkusUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Vujaklija, IvanAalto University, FinlandUNSPECIFIEDUNSPECIFIED
Castellini, ClaudioUNSPECIFIEDhttps://orcid.org/0000-0002-7346-2180150916943
Date:24 November 2023
Journal or Publication Title:Journal of Neural Engineering
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:20
DOI:10.1088/1741-2552/ad0754
Publisher:Institute of Physics (IOP) Publishing
ISSN:1741-2560
Status:Published
Keywords:coadaptive myocontrol, unsupervised myocontrol, muscle synergies, surface electromyography, motor skill learning
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Robotics
DLR - Research area:Raumfahrt
DLR - Program:R RO - Robotics
DLR - Research theme (Project):R - Terrestrial Assistance Robotics
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Robotics and Mechatronics (since 2013) > Cognitive Robotics
Deposited By: Gigli, Mr Andrea
Deposited On:17 Jan 2024 12:25
Last Modified:17 Jan 2024 12:25

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