Cargando…

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

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,...

Descripción completa

Detalles Bibliográficos
Autores principales: Nowak, Markus, Bongers, Raoul M., van der Sluis, Corry K., Albu-Schäffer, Alin, Castellini, Claudio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10082541/
https://www.ncbi.nlm.nih.gov/pubmed/37029432
http://dx.doi.org/10.1186/s12984-023-01171-2
_version_ 1785021334419406848
author Nowak, Markus
Bongers, Raoul M.
van der Sluis, Corry K.
Albu-Schäffer, Alin
Castellini, Claudio
author_facet Nowak, Markus
Bongers, Raoul M.
van der Sluis, Corry K.
Albu-Schäffer, Alin
Castellini, Claudio
author_sort Nowak, Markus
collection PubMed
description 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.
format Online
Article
Text
id pubmed-10082541
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-100825412023-04-09 Simultaneous assessment and training of an upper-limb amputee using incremental machine-learning-based myocontrol: a single-case experimental design Nowak, Markus Bongers, Raoul M. van der Sluis, Corry K. Albu-Schäffer, Alin Castellini, Claudio J Neuroeng Rehabil Research 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. BioMed Central 2023-04-07 /pmc/articles/PMC10082541/ /pubmed/37029432 http://dx.doi.org/10.1186/s12984-023-01171-2 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Nowak, Markus
Bongers, Raoul M.
van der Sluis, Corry K.
Albu-Schäffer, Alin
Castellini, Claudio
Simultaneous assessment and training of an upper-limb amputee using incremental machine-learning-based myocontrol: a single-case experimental design
title Simultaneous assessment and training of an upper-limb amputee using incremental machine-learning-based myocontrol: a single-case experimental design
title_full Simultaneous assessment and training of an upper-limb amputee using incremental machine-learning-based myocontrol: a single-case experimental design
title_fullStr Simultaneous assessment and training of an upper-limb amputee using incremental machine-learning-based myocontrol: a single-case experimental design
title_full_unstemmed Simultaneous assessment and training of an upper-limb amputee using incremental machine-learning-based myocontrol: a single-case experimental design
title_short Simultaneous assessment and training of an upper-limb amputee using incremental machine-learning-based myocontrol: a single-case experimental design
title_sort simultaneous assessment and training of an upper-limb amputee using incremental machine-learning-based myocontrol: a single-case experimental design
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10082541/
https://www.ncbi.nlm.nih.gov/pubmed/37029432
http://dx.doi.org/10.1186/s12984-023-01171-2
work_keys_str_mv AT nowakmarkus simultaneousassessmentandtrainingofanupperlimbamputeeusingincrementalmachinelearningbasedmyocontrolasinglecaseexperimentaldesign
AT bongersraoulm simultaneousassessmentandtrainingofanupperlimbamputeeusingincrementalmachinelearningbasedmyocontrolasinglecaseexperimentaldesign
AT vandersluiscorryk simultaneousassessmentandtrainingofanupperlimbamputeeusingincrementalmachinelearningbasedmyocontrolasinglecaseexperimentaldesign
AT albuschafferalin simultaneousassessmentandtrainingofanupperlimbamputeeusingincrementalmachinelearningbasedmyocontrolasinglecaseexperimentaldesign
AT castelliniclaudio simultaneousassessmentandtrainingofanupperlimbamputeeusingincrementalmachinelearningbasedmyocontrolasinglecaseexperimentaldesign