Cargando…

Estimation of knee joint movement using single-channel sEMG signals with a feature-guided convolutional neural network

Estimating human motion intention, such as intent joint torque and movement, plays a crucial role in assistive robotics for ensuring efficient and safe human-robot interaction. For coupled human-robot systems, surface electromyography (sEMG) signal has been proven as an effective means for estimatin...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhang, Song, Lu, Jiewei, Huo, Weiguang, Yu, Ningbo, Han, Jianda
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9640579/
https://www.ncbi.nlm.nih.gov/pubmed/36386394
http://dx.doi.org/10.3389/fnbot.2022.978014
_version_ 1784825885937893376
author Zhang, Song
Lu, Jiewei
Huo, Weiguang
Yu, Ningbo
Han, Jianda
author_facet Zhang, Song
Lu, Jiewei
Huo, Weiguang
Yu, Ningbo
Han, Jianda
author_sort Zhang, Song
collection PubMed
description Estimating human motion intention, such as intent joint torque and movement, plays a crucial role in assistive robotics for ensuring efficient and safe human-robot interaction. For coupled human-robot systems, surface electromyography (sEMG) signal has been proven as an effective means for estimating human's intended movements. Usually, joint movement estimation uses sEMG signals measured from multiple muscles and needs many sEMG sensors placed on the human body, which may cause discomfort or result in mechanical/signal interference from wearable robots/environment during long-term routine use. Although the muscle synergy principle implies that it is possible to estimate human motion using sEMG signals from even one signal muscle, few studies investigated the feasibility of continuous motion estimation based on single-channel sEMG. In this study, a feature-guided convolutional neural network (FG-CNN) has been proposed to estimate human knee joint movement using single-channel sEMG. In the proposed FG-CNN, several handcrafted features have been fused into a CNN model to guide CNN feature extraction, and both handcrafted and CNN-extracted features were applied to a regression model, i.e., random forest regression, to estimate knee joint movements. Experiments with 8 healthy subjects were carried out, and sEMG signals measured from 6 muscles, i.e., vastus lateralis, vastus medialis, biceps femoris, semitendinosus, lateral or medial gastrocnemius (LG or MG), were separately evaluated for knee joint estimation using the proposed method. The experimental results demonstrated that the proposed FG-CNN method with single-channel sEMG signals from LG or MG can effectively estimate human knee joint movements. The average correlation coefficient between the measured and the estimated knee joint movements is 0.858 ± 0.085 for LG and 0.856 ± 0.057 for MG. Meanwhile, comparative studies showed that the combined handcrafted-CNN features outperform either the handcrafted features or the CNN features; the performance of the proposed signal-channel sEMG-based FG-CNN method is comparable to those of the traditional multi-channel sEMG-based methods. The outcomes of this study enable the possibility of developing a single-channel sEMG-based human-robot interface for knee joint movement estimation, which can facilitate the routine use of assistive robots.
format Online
Article
Text
id pubmed-9640579
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-96405792022-11-15 Estimation of knee joint movement using single-channel sEMG signals with a feature-guided convolutional neural network Zhang, Song Lu, Jiewei Huo, Weiguang Yu, Ningbo Han, Jianda Front Neurorobot Neuroscience Estimating human motion intention, such as intent joint torque and movement, plays a crucial role in assistive robotics for ensuring efficient and safe human-robot interaction. For coupled human-robot systems, surface electromyography (sEMG) signal has been proven as an effective means for estimating human's intended movements. Usually, joint movement estimation uses sEMG signals measured from multiple muscles and needs many sEMG sensors placed on the human body, which may cause discomfort or result in mechanical/signal interference from wearable robots/environment during long-term routine use. Although the muscle synergy principle implies that it is possible to estimate human motion using sEMG signals from even one signal muscle, few studies investigated the feasibility of continuous motion estimation based on single-channel sEMG. In this study, a feature-guided convolutional neural network (FG-CNN) has been proposed to estimate human knee joint movement using single-channel sEMG. In the proposed FG-CNN, several handcrafted features have been fused into a CNN model to guide CNN feature extraction, and both handcrafted and CNN-extracted features were applied to a regression model, i.e., random forest regression, to estimate knee joint movements. Experiments with 8 healthy subjects were carried out, and sEMG signals measured from 6 muscles, i.e., vastus lateralis, vastus medialis, biceps femoris, semitendinosus, lateral or medial gastrocnemius (LG or MG), were separately evaluated for knee joint estimation using the proposed method. The experimental results demonstrated that the proposed FG-CNN method with single-channel sEMG signals from LG or MG can effectively estimate human knee joint movements. The average correlation coefficient between the measured and the estimated knee joint movements is 0.858 ± 0.085 for LG and 0.856 ± 0.057 for MG. Meanwhile, comparative studies showed that the combined handcrafted-CNN features outperform either the handcrafted features or the CNN features; the performance of the proposed signal-channel sEMG-based FG-CNN method is comparable to those of the traditional multi-channel sEMG-based methods. The outcomes of this study enable the possibility of developing a single-channel sEMG-based human-robot interface for knee joint movement estimation, which can facilitate the routine use of assistive robots. Frontiers Media S.A. 2022-10-25 /pmc/articles/PMC9640579/ /pubmed/36386394 http://dx.doi.org/10.3389/fnbot.2022.978014 Text en Copyright © 2022 Zhang, Lu, Huo, Yu and Han. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Zhang, Song
Lu, Jiewei
Huo, Weiguang
Yu, Ningbo
Han, Jianda
Estimation of knee joint movement using single-channel sEMG signals with a feature-guided convolutional neural network
title Estimation of knee joint movement using single-channel sEMG signals with a feature-guided convolutional neural network
title_full Estimation of knee joint movement using single-channel sEMG signals with a feature-guided convolutional neural network
title_fullStr Estimation of knee joint movement using single-channel sEMG signals with a feature-guided convolutional neural network
title_full_unstemmed Estimation of knee joint movement using single-channel sEMG signals with a feature-guided convolutional neural network
title_short Estimation of knee joint movement using single-channel sEMG signals with a feature-guided convolutional neural network
title_sort estimation of knee joint movement using single-channel semg signals with a feature-guided convolutional neural network
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9640579/
https://www.ncbi.nlm.nih.gov/pubmed/36386394
http://dx.doi.org/10.3389/fnbot.2022.978014
work_keys_str_mv AT zhangsong estimationofkneejointmovementusingsinglechannelsemgsignalswithafeatureguidedconvolutionalneuralnetwork
AT lujiewei estimationofkneejointmovementusingsinglechannelsemgsignalswithafeatureguidedconvolutionalneuralnetwork
AT huoweiguang estimationofkneejointmovementusingsinglechannelsemgsignalswithafeatureguidedconvolutionalneuralnetwork
AT yuningbo estimationofkneejointmovementusingsinglechannelsemgsignalswithafeatureguidedconvolutionalneuralnetwork
AT hanjianda estimationofkneejointmovementusingsinglechannelsemgsignalswithafeatureguidedconvolutionalneuralnetwork