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Online mapping of EMG signals into kinematics by autoencoding
BACKGROUND: In this paper, we propose a nonlinear minimally supervised method based on autoencoding (AEN) of EMG for myocontrol. The proposed method was tested against the state-of-the-art (SOA) control scheme using a Fitts’ law approach. METHODS: Seven able-bodied subjects performed a series of tar...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5850983/ https://www.ncbi.nlm.nih.gov/pubmed/29534764 http://dx.doi.org/10.1186/s12984-018-0363-1 |
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author | Vujaklija, Ivan Shalchyan, Vahid Kamavuako, Ernest N. Jiang, Ning Marateb, Hamid R. Farina, Dario |
author_facet | Vujaklija, Ivan Shalchyan, Vahid Kamavuako, Ernest N. Jiang, Ning Marateb, Hamid R. Farina, Dario |
author_sort | Vujaklija, Ivan |
collection | PubMed |
description | BACKGROUND: In this paper, we propose a nonlinear minimally supervised method based on autoencoding (AEN) of EMG for myocontrol. The proposed method was tested against the state-of-the-art (SOA) control scheme using a Fitts’ law approach. METHODS: Seven able-bodied subjects performed a series of target acquisition myoelectric control tasks using the AEN and SOA algorithms for controlling two degrees-of-freedom (radial/ulnar deviation and flexion/extension of the wrist), and their online performance was characterized by six metrics. RESULTS: Both methods allowed a completion rate close to 100%, however AEN outperformed SOA for all other performance metrics, e.g. it allowed to perform the tasks on average in half the time with respect to SOA. Moreover, the amount of information transferred by the proposed method in bit/s was nearly twice the throughput of SOA. CONCLUSIONS: These results show that autoencoders can map EMG signals into kinematics with the potential of providing intuitive and dexterous control of artificial limbs for amputees. |
format | Online Article Text |
id | pubmed-5850983 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-58509832018-03-21 Online mapping of EMG signals into kinematics by autoencoding Vujaklija, Ivan Shalchyan, Vahid Kamavuako, Ernest N. Jiang, Ning Marateb, Hamid R. Farina, Dario J Neuroeng Rehabil Research BACKGROUND: In this paper, we propose a nonlinear minimally supervised method based on autoencoding (AEN) of EMG for myocontrol. The proposed method was tested against the state-of-the-art (SOA) control scheme using a Fitts’ law approach. METHODS: Seven able-bodied subjects performed a series of target acquisition myoelectric control tasks using the AEN and SOA algorithms for controlling two degrees-of-freedom (radial/ulnar deviation and flexion/extension of the wrist), and their online performance was characterized by six metrics. RESULTS: Both methods allowed a completion rate close to 100%, however AEN outperformed SOA for all other performance metrics, e.g. it allowed to perform the tasks on average in half the time with respect to SOA. Moreover, the amount of information transferred by the proposed method in bit/s was nearly twice the throughput of SOA. CONCLUSIONS: These results show that autoencoders can map EMG signals into kinematics with the potential of providing intuitive and dexterous control of artificial limbs for amputees. BioMed Central 2018-03-13 /pmc/articles/PMC5850983/ /pubmed/29534764 http://dx.doi.org/10.1186/s12984-018-0363-1 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Vujaklija, Ivan Shalchyan, Vahid Kamavuako, Ernest N. Jiang, Ning Marateb, Hamid R. Farina, Dario Online mapping of EMG signals into kinematics by autoencoding |
title | Online mapping of EMG signals into kinematics by autoencoding |
title_full | Online mapping of EMG signals into kinematics by autoencoding |
title_fullStr | Online mapping of EMG signals into kinematics by autoencoding |
title_full_unstemmed | Online mapping of EMG signals into kinematics by autoencoding |
title_short | Online mapping of EMG signals into kinematics by autoencoding |
title_sort | online mapping of emg signals into kinematics by autoencoding |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5850983/ https://www.ncbi.nlm.nih.gov/pubmed/29534764 http://dx.doi.org/10.1186/s12984-018-0363-1 |
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