End-to-End Estimation of Hand- and Wrist Forces From Raw Intramuscular EMG Signals Using LSTM Networks

Processing myoelectrical activity in the forearm has for long been considered a promising framework to allow transradial amputees to control motorized prostheses. In spite of expectations, contemporary muscle–computer interfaces built for this purpose typically fail to satisfy one or more important...

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Autores principales:	Alexander E. Olsson, Nebojša Malešević, Anders Björkman, Christian Antfolk
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	iEMG force deep learning LSTM recurrent neural networks regression Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/55c4605ed76f4663bb731629dc6eb60f
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spelling	oai:doaj.org-article:55c4605ed76f4663bb731629dc6eb60f2021-11-17T05:32:59ZEnd-to-End Estimation of Hand- and Wrist Forces From Raw Intramuscular EMG Signals Using LSTM Networks1662-453X10.3389/fnins.2021.777329https://doaj.org/article/55c4605ed76f4663bb731629dc6eb60f2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fnins.2021.777329/fullhttps://doaj.org/toc/1662-453XProcessing myoelectrical activity in the forearm has for long been considered a promising framework to allow transradial amputees to control motorized prostheses. In spite of expectations, contemporary muscle–computer interfaces built for this purpose typically fail to satisfy one or more important desiderata, such as accuracy, robustness, and/or naturalness of control, in part due to difficulties in acquiring high-quality signals continuously outside laboratory conditions. In light of such problems, surgically implanted electrodes have been made a viable option that allows for long-term acquisition of intramuscular electromyography (iEMG) measurements of spatially precise origin. As it stands, the question of how information embedded in such signals is best extracted and combined across multiple channels remains open. This study presents and evaluates an approach to this end that uses deep neural networks based on the Long Short-Term Memory (LSTMs) architecture to regress forces exerted by multiple degrees of freedom (DoFs) from multichannel iEMG. Three deep learning models, representing three distinct regression strategies, were evaluated: (I) One-to-One, wherein each DoF is separately estimated by an LSTM model processing a single iEMG channel, (II) All-to-One, wherein each DoF is separately estimated by an LSTM model processing all iEMG channels, and (III) All-to-All, wherein a single LSTM model with access to all iEMG channels estimates all DoFs simultaneously. All models operate on raw iEMG, with no preliminary feature extraction required. When evaluated on a dataset comprising six iEMG channels with concurrent force measurements acquired from 14 subjects, all LSTM strategies were found to significantly outperform a baseline feature-based linear control regression method. This finding indicates that recurrent neural networks can learn to transform raw forearm iEMG signals directly into representations that correlate with forces exerted at the level of the hand to a greater degree than simple features do. Furthermore, the All-to-All and All-to-One strategies were found to exhibit better performance than the One-to-One strategy. This finding suggests that, in spite of the spatially local nature of signals, iEMG from muscles not directly actuating the relevant DoF can provide contextual information that aid in decoding motor intent.Alexander E. OlssonNebojša MaleševićAnders BjörkmanAnders BjörkmanAnders BjörkmanChristian AntfolkFrontiers Media S.A.articleiEMGforcedeep learningLSTMrecurrent neural networksregressionNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENFrontiers in Neuroscience, Vol 15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	iEMG force deep learning LSTM recurrent neural networks regression Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	iEMG force deep learning LSTM recurrent neural networks regression Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Alexander E. Olsson Nebojša Malešević Anders Björkman Anders Björkman Anders Björkman Christian Antfolk End-to-End Estimation of Hand- and Wrist Forces From Raw Intramuscular EMG Signals Using LSTM Networks
description	Processing myoelectrical activity in the forearm has for long been considered a promising framework to allow transradial amputees to control motorized prostheses. In spite of expectations, contemporary muscle–computer interfaces built for this purpose typically fail to satisfy one or more important desiderata, such as accuracy, robustness, and/or naturalness of control, in part due to difficulties in acquiring high-quality signals continuously outside laboratory conditions. In light of such problems, surgically implanted electrodes have been made a viable option that allows for long-term acquisition of intramuscular electromyography (iEMG) measurements of spatially precise origin. As it stands, the question of how information embedded in such signals is best extracted and combined across multiple channels remains open. This study presents and evaluates an approach to this end that uses deep neural networks based on the Long Short-Term Memory (LSTMs) architecture to regress forces exerted by multiple degrees of freedom (DoFs) from multichannel iEMG. Three deep learning models, representing three distinct regression strategies, were evaluated: (I) One-to-One, wherein each DoF is separately estimated by an LSTM model processing a single iEMG channel, (II) All-to-One, wherein each DoF is separately estimated by an LSTM model processing all iEMG channels, and (III) All-to-All, wherein a single LSTM model with access to all iEMG channels estimates all DoFs simultaneously. All models operate on raw iEMG, with no preliminary feature extraction required. When evaluated on a dataset comprising six iEMG channels with concurrent force measurements acquired from 14 subjects, all LSTM strategies were found to significantly outperform a baseline feature-based linear control regression method. This finding indicates that recurrent neural networks can learn to transform raw forearm iEMG signals directly into representations that correlate with forces exerted at the level of the hand to a greater degree than simple features do. Furthermore, the All-to-All and All-to-One strategies were found to exhibit better performance than the One-to-One strategy. This finding suggests that, in spite of the spatially local nature of signals, iEMG from muscles not directly actuating the relevant DoF can provide contextual information that aid in decoding motor intent.
format	article
author	Alexander E. Olsson Nebojša Malešević Anders Björkman Anders Björkman Anders Björkman Christian Antfolk
author_facet	Alexander E. Olsson Nebojša Malešević Anders Björkman Anders Björkman Anders Björkman Christian Antfolk
author_sort	Alexander E. Olsson
title	End-to-End Estimation of Hand- and Wrist Forces From Raw Intramuscular EMG Signals Using LSTM Networks
title_short	End-to-End Estimation of Hand- and Wrist Forces From Raw Intramuscular EMG Signals Using LSTM Networks
title_full	End-to-End Estimation of Hand- and Wrist Forces From Raw Intramuscular EMG Signals Using LSTM Networks
title_fullStr	End-to-End Estimation of Hand- and Wrist Forces From Raw Intramuscular EMG Signals Using LSTM Networks
title_full_unstemmed	End-to-End Estimation of Hand- and Wrist Forces From Raw Intramuscular EMG Signals Using LSTM Networks
title_sort	end-to-end estimation of hand- and wrist forces from raw intramuscular emg signals using lstm networks
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/55c4605ed76f4663bb731629dc6eb60f
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End-to-End Estimation of Hand- and Wrist Forces From Raw Intramuscular EMG Signals Using LSTM Networks

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