Cargando…

Estimation of the Continuous Pronation–Supination Movement by Using Multichannel EMG Signal Features and Kalman Filter: Application to Control an Exoskeleton

The Hill muscle model can be used to estimate the human joint angles during continuous movement. However, adopting this model requires the knowledge of many parameters, such as the length and speed of contraction of muscle fibers, which are liable to change with different individuals, leading to err...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhang, Lei, Long, Jingang, Zhao, RongGang, Cao, Haoyang, Zhang, Kai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8921927/
https://www.ncbi.nlm.nih.gov/pubmed/35299701
http://dx.doi.org/10.3389/fbioe.2021.771255
_version_ 1784669419390107648
author Zhang, Lei
Long, Jingang
Zhao, RongGang
Cao, Haoyang
Zhang, Kai
author_facet Zhang, Lei
Long, Jingang
Zhao, RongGang
Cao, Haoyang
Zhang, Kai
author_sort Zhang, Lei
collection PubMed
description The Hill muscle model can be used to estimate the human joint angles during continuous movement. However, adopting this model requires the knowledge of many parameters, such as the length and speed of contraction of muscle fibers, which are liable to change with different individuals, leading to errors in estimation. This study established the backpropagation neural network model based on surface electromyography (sEMG) features and human movement angle. First, the function of muscles in joint rotation is defined, and then, sensors are placed on muscle tissues to gain sEMG, and then, a relation model between the surface sEMG features and the joint angle is constructed. As integrated electromyography information cannot be well reflected through a single electromyography feature, a feature extraction method combining the time domain, frequency domain, and time–frequency domain was proposed. As the degree of freedom (DOF) of the pronation–supination movement was controlled by several muscles, it was difficult to make an angle prediction. A method of correcting the estimation error based on the Kalman filter was raised to cope with this problem. An exoskeleton robot with one DOF was designed and put into the tracking experiment. The results show that the proposed model was able to enhance the estimation of the joint angle during continuous pronation–supination movements.
format Online
Article
Text
id pubmed-8921927
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-89219272022-03-16 Estimation of the Continuous Pronation–Supination Movement by Using Multichannel EMG Signal Features and Kalman Filter: Application to Control an Exoskeleton Zhang, Lei Long, Jingang Zhao, RongGang Cao, Haoyang Zhang, Kai Front Bioeng Biotechnol Bioengineering and Biotechnology The Hill muscle model can be used to estimate the human joint angles during continuous movement. However, adopting this model requires the knowledge of many parameters, such as the length and speed of contraction of muscle fibers, which are liable to change with different individuals, leading to errors in estimation. This study established the backpropagation neural network model based on surface electromyography (sEMG) features and human movement angle. First, the function of muscles in joint rotation is defined, and then, sensors are placed on muscle tissues to gain sEMG, and then, a relation model between the surface sEMG features and the joint angle is constructed. As integrated electromyography information cannot be well reflected through a single electromyography feature, a feature extraction method combining the time domain, frequency domain, and time–frequency domain was proposed. As the degree of freedom (DOF) of the pronation–supination movement was controlled by several muscles, it was difficult to make an angle prediction. A method of correcting the estimation error based on the Kalman filter was raised to cope with this problem. An exoskeleton robot with one DOF was designed and put into the tracking experiment. The results show that the proposed model was able to enhance the estimation of the joint angle during continuous pronation–supination movements. Frontiers Media S.A. 2022-03-01 /pmc/articles/PMC8921927/ /pubmed/35299701 http://dx.doi.org/10.3389/fbioe.2021.771255 Text en Copyright © 2022 Zhang, Long, Zhao, Cao and Zhang. 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 Bioengineering and Biotechnology
Zhang, Lei
Long, Jingang
Zhao, RongGang
Cao, Haoyang
Zhang, Kai
Estimation of the Continuous Pronation–Supination Movement by Using Multichannel EMG Signal Features and Kalman Filter: Application to Control an Exoskeleton
title Estimation of the Continuous Pronation–Supination Movement by Using Multichannel EMG Signal Features and Kalman Filter: Application to Control an Exoskeleton
title_full Estimation of the Continuous Pronation–Supination Movement by Using Multichannel EMG Signal Features and Kalman Filter: Application to Control an Exoskeleton
title_fullStr Estimation of the Continuous Pronation–Supination Movement by Using Multichannel EMG Signal Features and Kalman Filter: Application to Control an Exoskeleton
title_full_unstemmed Estimation of the Continuous Pronation–Supination Movement by Using Multichannel EMG Signal Features and Kalman Filter: Application to Control an Exoskeleton
title_short Estimation of the Continuous Pronation–Supination Movement by Using Multichannel EMG Signal Features and Kalman Filter: Application to Control an Exoskeleton
title_sort estimation of the continuous pronation–supination movement by using multichannel emg signal features and kalman filter: application to control an exoskeleton
topic Bioengineering and Biotechnology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8921927/
https://www.ncbi.nlm.nih.gov/pubmed/35299701
http://dx.doi.org/10.3389/fbioe.2021.771255
work_keys_str_mv AT zhanglei estimationofthecontinuouspronationsupinationmovementbyusingmultichannelemgsignalfeaturesandkalmanfilterapplicationtocontrolanexoskeleton
AT longjingang estimationofthecontinuouspronationsupinationmovementbyusingmultichannelemgsignalfeaturesandkalmanfilterapplicationtocontrolanexoskeleton
AT zhaoronggang estimationofthecontinuouspronationsupinationmovementbyusingmultichannelemgsignalfeaturesandkalmanfilterapplicationtocontrolanexoskeleton
AT caohaoyang estimationofthecontinuouspronationsupinationmovementbyusingmultichannelemgsignalfeaturesandkalmanfilterapplicationtocontrolanexoskeleton
AT zhangkai estimationofthecontinuouspronationsupinationmovementbyusingmultichannelemgsignalfeaturesandkalmanfilterapplicationtocontrolanexoskeleton