Myoelectric digit action decoding with multi-output, multi-class classification: an offline analysis

Abstract The ultimate goal of machine learning-based myoelectric control is simultaneous and independent control of multiple degrees of freedom (DOFs), including wrist and digit artificial joints. For prosthetic finger control, regression-based methods are typically used to reconstruct position/velo...

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Autores principales:	Agamemnon Krasoulis, Kianoush Nazarpour
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ae3e769ae891484ca5e2ce83d4b4cd4c
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spelling	oai:doaj.org-article:ae3e769ae891484ca5e2ce83d4b4cd4c2021-12-02T18:37:06ZMyoelectric digit action decoding with multi-output, multi-class classification: an offline analysis10.1038/s41598-020-72574-72045-2322https://doaj.org/article/ae3e769ae891484ca5e2ce83d4b4cd4c2020-10-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-72574-7https://doaj.org/toc/2045-2322Abstract The ultimate goal of machine learning-based myoelectric control is simultaneous and independent control of multiple degrees of freedom (DOFs), including wrist and digit artificial joints. For prosthetic finger control, regression-based methods are typically used to reconstruct position/velocity trajectories from surface electromyogram (EMG) signals. Unfortunately, such methods have thus far met with limited success. In this work, we propose action decoding, a paradigm-shifting approach for independent, multi-digit movement intent prediction based on multi-output, multi-class classification. At each moment in time, our algorithm decodes movement intent for each available DOF into one of three classes: open, close, or stall (i.e., no movement). Despite using a classifier as the decoder, arbitrary hand postures are possible with our approach. We analyse a public dataset previously recorded and published by us, comprising measurements from 10 able-bodied and two transradial amputee participants. We demonstrate the feasibility of using our proposed action decoding paradigm to predict movement action for all five digits as well as rotation of the thumb. We perform a systematic offline analysis by investigating the effect of various algorithmic parameters on decoding performance, such as feature selection and choice of classification algorithm and multi-output strategy. The outcomes of the offline analysis presented in this study will be used to inform the real-time implementation of our algorithm. In the future, we will further evaluate its efficacy with real-time control experiments involving upper-limb amputees.Agamemnon KrasoulisKianoush NazarpourNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-10 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Agamemnon Krasoulis Kianoush Nazarpour Myoelectric digit action decoding with multi-output, multi-class classification: an offline analysis
description	Abstract The ultimate goal of machine learning-based myoelectric control is simultaneous and independent control of multiple degrees of freedom (DOFs), including wrist and digit artificial joints. For prosthetic finger control, regression-based methods are typically used to reconstruct position/velocity trajectories from surface electromyogram (EMG) signals. Unfortunately, such methods have thus far met with limited success. In this work, we propose action decoding, a paradigm-shifting approach for independent, multi-digit movement intent prediction based on multi-output, multi-class classification. At each moment in time, our algorithm decodes movement intent for each available DOF into one of three classes: open, close, or stall (i.e., no movement). Despite using a classifier as the decoder, arbitrary hand postures are possible with our approach. We analyse a public dataset previously recorded and published by us, comprising measurements from 10 able-bodied and two transradial amputee participants. We demonstrate the feasibility of using our proposed action decoding paradigm to predict movement action for all five digits as well as rotation of the thumb. We perform a systematic offline analysis by investigating the effect of various algorithmic parameters on decoding performance, such as feature selection and choice of classification algorithm and multi-output strategy. The outcomes of the offline analysis presented in this study will be used to inform the real-time implementation of our algorithm. In the future, we will further evaluate its efficacy with real-time control experiments involving upper-limb amputees.
format	article
author	Agamemnon Krasoulis Kianoush Nazarpour
author_facet	Agamemnon Krasoulis Kianoush Nazarpour
author_sort	Agamemnon Krasoulis
title	Myoelectric digit action decoding with multi-output, multi-class classification: an offline analysis
title_short	Myoelectric digit action decoding with multi-output, multi-class classification: an offline analysis
title_full	Myoelectric digit action decoding with multi-output, multi-class classification: an offline analysis
title_fullStr	Myoelectric digit action decoding with multi-output, multi-class classification: an offline analysis
title_full_unstemmed	Myoelectric digit action decoding with multi-output, multi-class classification: an offline analysis
title_sort	myoelectric digit action decoding with multi-output, multi-class classification: an offline analysis
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/ae3e769ae891484ca5e2ce83d4b4cd4c
work_keys_str_mv	AT agamemnonkrasoulis myoelectricdigitactiondecodingwithmultioutputmulticlassclassificationanofflineanalysis AT kianoushnazarpour myoelectricdigitactiondecodingwithmultioutputmulticlassclassificationanofflineanalysis
_version_	1718377820756901888

Myoelectric digit action decoding with multi-output, multi-class classification: an offline analysis

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