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Progressive unsupervised control of myoelectric upper limbs

Gigli, Andrea und Gijsberts, Arjan und Nowak, Markus und Vujaklija, Ivan und 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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Offizielle URL: https://iopscience.iop.org/article/10.1088/1741-2552/ad0754

Kurzfassung

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.

elib-URL des Eintrags:https://elib.dlr.de/202042/
Dokumentart:Zeitschriftenbeitrag
Titel:Progressive unsupervised control of myoelectric upper limbs
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Gigli, AndreaAndrea.Gigli (at) dlr.dehttps://orcid.org/0000-0001-7049-485XNICHT SPEZIFIZIERT
Gijsberts, ArjanNICHT SPEZIFIZIERTNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Nowak, Markusmarkus.nowak (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Vujaklija, IvanAalto University, FinlandNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Castellini, ClaudioClaudio.Castellini (at) dlr.dehttps://orcid.org/0000-0002-7346-2180150916943
Datum:24 November 2023
Erschienen in:Journal of Neural Engineering
Referierte Publikation:Ja
Open Access:Ja
Gold Open Access:Nein
In SCOPUS:Ja
In ISI Web of Science:Ja
Band:20
DOI:10.1088/1741-2552/ad0754
Verlag:Institute of Physics (IOP) Publishing
ISSN:1741-2560
Status:veröffentlicht
Stichwörter:coadaptive myocontrol, unsupervised myocontrol, muscle synergies, surface electromyography, motor skill learning
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Raumfahrt
HGF - Programmthema:Robotik
DLR - Schwerpunkt:Raumfahrt
DLR - Forschungsgebiet:R RO - Robotik
DLR - Teilgebiet (Projekt, Vorhaben):R - Terrestrische Assistenz-Robotik
Standort: Oberpfaffenhofen
Institute & Einrichtungen:Institut für Robotik und Mechatronik (ab 2013) > Kognitive Robotik
Hinterlegt von: Gigli, Mr Andrea
Hinterlegt am:17 Jan 2024 12:25
Letzte Änderung:17 Jan 2024 12:25

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