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Simultaneous assessment and training of an upper-limb amputee using incremental machine-learning-based myocontrol: a single-case experimental design

Nowak, Markus und Bongers, Raoul M. und van der Sluis, Corry K. und Castellini, Claudio und Albu-Schäffer, Alin (2023) Simultaneous assessment and training of an upper-limb amputee using incremental machine-learning-based myocontrol: a single-case experimental design. Journal of NeuroEngineering and Rehabilitation, 20 (39). Springer Nature. doi: 10.1186/s12984-023-01171-2. ISSN 1743-0003.

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Offizielle URL: https://jneuroengrehab.biomedcentral.com/articles/10.1186/s12984-023-01171-2

Kurzfassung

Background Machine-learning-based myocontrol of prosthetic devices suffers from a high rate of abandonment due to dissatisfaction with the training procedure and with the reliability of day-to-day control. Incremental myocontrol is a promising approach as it allows on-demand updating of the system, thus enforcing continuous interaction with the user. Nevertheless, a long-term study assessing the efficacy of incremental myocontrol is still missing, partially due to the lack of an adequate tool to do so. In this work we close this gap and report about a person with upper-limb absence who learned to control a dexterous hand prosthesis using incremental myocontrol through a novel functional assessment protocol called SATMC (Simultaneous Assessment and Training of Myoelectric Control). Methods The participant was fitted with a custom-made prosthetic setup with a controller based on Ridge Regression with Random Fourier Features (RR-RFF), a non-linear, incremental machine learning method, used to build and progressively update the myocontrol system. During a 13-month user study, the participant performed increasingly complex daily-living tasks, requiring fine bimanual coordination and manipulation with a multi-fingered hand prosthesis, in a realistic laboratory setup. The SATMC was used both to compose the tasks and continually assess the participant's progress. Patient satisfaction was measured using Visual Analog Scales. Results Over the course of the study, the participant progressively improved his performance both objectively, e.g., the time required to complete each task became shorter, and subjectively, meaning that his satisfaction improved. The SATMC actively supported the improvement of the participant by progressively increasing the difficulty of the tasks in a structured way. In combination with the incremental RR-RFF allowing for small adjustments when required, the participant was capable of reliably using four actions of the prosthetic hand to perform all required tasks at the end of the study. Conclusions Incremental myocontrol enabled an upper-limb amputee to reliably control a dexterous hand prosthesis while providing a subjectively satisfactory experience. The SATMC can be an effective tool to this aim.

elib-URL des Eintrags:https://elib.dlr.de/194687/
Dokumentart:Zeitschriftenbeitrag
Titel:Simultaneous assessment and training of an upper-limb amputee using incremental machine-learning-based myocontrol: a single-case experimental design
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Nowak, Markusmarkus.nowak (at) dlr.dehttps://orcid.org/0000-0002-0840-5155NICHT SPEZIFIZIERT
Bongers, Raoul M.University of Groningen, The NetherlandsNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
van der Sluis, Corry K.University of Groningen, The NetherlandsNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Castellini, Claudioclaudio.castellini (at) dlr.dehttps://orcid.org/0000-0002-7346-2180NICHT SPEZIFIZIERT
Albu-Schäffer, AlinAlin.Albu-Schaeffer (at) dlr.dehttps://orcid.org/0000-0001-5343-9074142115936
Datum:7 April 2023
Erschienen in:Journal of NeuroEngineering and Rehabilitation
Referierte Publikation:Ja
Open Access:Ja
Gold Open Access:Ja
In SCOPUS:Ja
In ISI Web of Science:Ja
Band:20
DOI:10.1186/s12984-023-01171-2
Verlag:Springer Nature
ISSN:1743-0003
Status:veröffentlicht
Stichwörter:Hand prosthesis, Machine-learning control, Myocontrol, Training, Single-case experimental design
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, R - Intelligente Mobilität (RM) [RO]
Standort: Oberpfaffenhofen
Institute & Einrichtungen:Institut für Robotik und Mechatronik (ab 2013)
Institut für Robotik und Mechatronik (ab 2013) > Kognitive Robotik
Hinterlegt von: Nowak, Markus
Hinterlegt am:02 Mai 2023 16:39
Letzte Änderung:11 Sep 2023 13:25

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