A novel framework for designing a multi-DoF prosthetic wrist control using machine learning

Abstract Prosthetic arms can significantly increase the upper limb function of individuals with upper limb loss, however despite the development of various multi-DoF prosthetic arms the rate of prosthesis abandonment is still high. One of the major challenges is to design a multi-DoF controller that...

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Autores principales:	Chinmay P. Swami, Nicholas Lenhard, Jiyeon Kang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/953a1e54993e49fdbb8bfe8df58aa54d
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spelling	oai:doaj.org-article:953a1e54993e49fdbb8bfe8df58aa54d2021-12-02T16:17:34ZA novel framework for designing a multi-DoF prosthetic wrist control using machine learning10.1038/s41598-021-94449-12045-2322https://doaj.org/article/953a1e54993e49fdbb8bfe8df58aa54d2021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-94449-1https://doaj.org/toc/2045-2322Abstract Prosthetic arms can significantly increase the upper limb function of individuals with upper limb loss, however despite the development of various multi-DoF prosthetic arms the rate of prosthesis abandonment is still high. One of the major challenges is to design a multi-DoF controller that has high precision, robustness, and intuitiveness for daily use. The present study demonstrates a novel framework for developing a controller leveraging machine learning algorithms and movement synergies to implement natural control of a 2-DoF prosthetic wrist for activities of daily living (ADL). The data was collected during ADL tasks of ten individuals with a wrist brace emulating the absence of wrist function. Using this data, the neural network classifies the movement and then random forest regression computes the desired velocity of the prosthetic wrist. The models were trained/tested with ADLs where their robustness was tested using cross-validation and holdout data sets. The proposed framework demonstrated high accuracy (F-1 score of 99% for the classifier and Pearson’s correlation of 0.98 for the regression). Additionally, the interpretable nature of random forest regression was used to verify the targeted movement synergies. The present work provides a novel and effective framework to develop an intuitive control for multi-DoF prosthetic devices.Chinmay P. SwamiNicholas LenhardJiyeon KangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Chinmay P. Swami Nicholas Lenhard Jiyeon Kang A novel framework for designing a multi-DoF prosthetic wrist control using machine learning
description	Abstract Prosthetic arms can significantly increase the upper limb function of individuals with upper limb loss, however despite the development of various multi-DoF prosthetic arms the rate of prosthesis abandonment is still high. One of the major challenges is to design a multi-DoF controller that has high precision, robustness, and intuitiveness for daily use. The present study demonstrates a novel framework for developing a controller leveraging machine learning algorithms and movement synergies to implement natural control of a 2-DoF prosthetic wrist for activities of daily living (ADL). The data was collected during ADL tasks of ten individuals with a wrist brace emulating the absence of wrist function. Using this data, the neural network classifies the movement and then random forest regression computes the desired velocity of the prosthetic wrist. The models were trained/tested with ADLs where their robustness was tested using cross-validation and holdout data sets. The proposed framework demonstrated high accuracy (F-1 score of 99% for the classifier and Pearson’s correlation of 0.98 for the regression). Additionally, the interpretable nature of random forest regression was used to verify the targeted movement synergies. The present work provides a novel and effective framework to develop an intuitive control for multi-DoF prosthetic devices.
format	article
author	Chinmay P. Swami Nicholas Lenhard Jiyeon Kang
author_facet	Chinmay P. Swami Nicholas Lenhard Jiyeon Kang
author_sort	Chinmay P. Swami
title	A novel framework for designing a multi-DoF prosthetic wrist control using machine learning
title_short	A novel framework for designing a multi-DoF prosthetic wrist control using machine learning
title_full	A novel framework for designing a multi-DoF prosthetic wrist control using machine learning
title_fullStr	A novel framework for designing a multi-DoF prosthetic wrist control using machine learning
title_full_unstemmed	A novel framework for designing a multi-DoF prosthetic wrist control using machine learning
title_sort	novel framework for designing a multi-dof prosthetic wrist control using machine learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/953a1e54993e49fdbb8bfe8df58aa54d
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A novel framework for designing a multi-DoF prosthetic wrist control using machine learning

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