Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Vujaklija, Ivan, Shalchyan, Vahid, Kamavuako, Ernest N., Jiang, Ning, Marateb, Hamid R., Farina, Dario
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
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
_version_ 1783306316033294336
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
work_keys_str_mv AT vujaklijaivan onlinemappingofemgsignalsintokinematicsbyautoencoding
AT shalchyanvahid onlinemappingofemgsignalsintokinematicsbyautoencoding
AT kamavuakoernestn onlinemappingofemgsignalsintokinematicsbyautoencoding
AT jiangning onlinemappingofemgsignalsintokinematicsbyautoencoding
AT maratebhamidr onlinemappingofemgsignalsintokinematicsbyautoencoding
AT farinadario onlinemappingofemgsignalsintokinematicsbyautoencoding