User adaptation in Myoelectric Man-Machine Interfaces

Abstract State of the art clinical hand prostheses are controlled in a simple and limited way that allows the activation of one function at a time. More advanced laboratory approaches, based on machine learning, offer a significant increase in functionality, but their clinical impact is limited, mai...

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Autores principales:	Janne M. Hahne, Marko Markovic, Dario Farina
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/3415318875e1482d9f59bb951700536c
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id	oai:doaj.org-article:3415318875e1482d9f59bb951700536c
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spelling	oai:doaj.org-article:3415318875e1482d9f59bb951700536c2021-12-02T11:52:57ZUser adaptation in Myoelectric Man-Machine Interfaces10.1038/s41598-017-04255-x2045-2322https://doaj.org/article/3415318875e1482d9f59bb951700536c2017-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-04255-xhttps://doaj.org/toc/2045-2322Abstract State of the art clinical hand prostheses are controlled in a simple and limited way that allows the activation of one function at a time. More advanced laboratory approaches, based on machine learning, offer a significant increase in functionality, but their clinical impact is limited, mainly due to lack of reliability. In this study, we analyse two conceptually different machine learning approaches, focusing on their robustness and performance in a closed loop application. A classification (finite number of classes) and a regression (continuous mapping) based projection of EMG into external commands were applied while artificially introducing non-stationarities in the EMG signals. When tested on ten able-bodied individuals and one transradial amputee, the two methods were similarly influenced by non-stationarities when tested offline. However, in online tests, where the user could adapt his muscle activation patterns to the changed conditions, the regression-based approach was significantly less influenced by the changes in signal features than the classification approach. This observation demonstrates, on the one hand, the importance of online tests with users in the loop for assessing the performance of myocontrol approaches. On the other hand, it also demonstrates that regression allows for a better user correction of control commands than classification.Janne M. HahneMarko MarkovicDario FarinaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-10 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Janne M. Hahne Marko Markovic Dario Farina User adaptation in Myoelectric Man-Machine Interfaces
description	Abstract State of the art clinical hand prostheses are controlled in a simple and limited way that allows the activation of one function at a time. More advanced laboratory approaches, based on machine learning, offer a significant increase in functionality, but their clinical impact is limited, mainly due to lack of reliability. In this study, we analyse two conceptually different machine learning approaches, focusing on their robustness and performance in a closed loop application. A classification (finite number of classes) and a regression (continuous mapping) based projection of EMG into external commands were applied while artificially introducing non-stationarities in the EMG signals. When tested on ten able-bodied individuals and one transradial amputee, the two methods were similarly influenced by non-stationarities when tested offline. However, in online tests, where the user could adapt his muscle activation patterns to the changed conditions, the regression-based approach was significantly less influenced by the changes in signal features than the classification approach. This observation demonstrates, on the one hand, the importance of online tests with users in the loop for assessing the performance of myocontrol approaches. On the other hand, it also demonstrates that regression allows for a better user correction of control commands than classification.
format	article
author	Janne M. Hahne Marko Markovic Dario Farina
author_facet	Janne M. Hahne Marko Markovic Dario Farina
author_sort	Janne M. Hahne
title	User adaptation in Myoelectric Man-Machine Interfaces
title_short	User adaptation in Myoelectric Man-Machine Interfaces
title_full	User adaptation in Myoelectric Man-Machine Interfaces
title_fullStr	User adaptation in Myoelectric Man-Machine Interfaces
title_full_unstemmed	User adaptation in Myoelectric Man-Machine Interfaces
title_sort	user adaptation in myoelectric man-machine interfaces
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/3415318875e1482d9f59bb951700536c
work_keys_str_mv	AT jannemhahne useradaptationinmyoelectricmanmachineinterfaces AT markomarkovic useradaptationinmyoelectricmanmachineinterfaces AT dariofarina useradaptationinmyoelectricmanmachineinterfaces
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User adaptation in Myoelectric Man-Machine Interfaces

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