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Two ways to improve myoelectric control for a transhumeral amputee after targeted muscle reinnervation: a case study

BACKGROUND: Myoelectric control of multifunctional prostheses is challenging for individuals with high-level amputations due to insufficient surface electromyography (sEMG) signals. A surgical technique called targeted muscle reinnervation (TMR) has achieved impressive improvements in myoelectric co...

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Autores principales: Xu, Yang, Zhang, Dingguo, Wang, Yang, Feng, Juntao, Xu, Wendong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5946536/
https://www.ncbi.nlm.nih.gov/pubmed/29747672
http://dx.doi.org/10.1186/s12984-018-0376-9
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author Xu, Yang
Zhang, Dingguo
Wang, Yang
Feng, Juntao
Xu, Wendong
author_facet Xu, Yang
Zhang, Dingguo
Wang, Yang
Feng, Juntao
Xu, Wendong
author_sort Xu, Yang
collection PubMed
description BACKGROUND: Myoelectric control of multifunctional prostheses is challenging for individuals with high-level amputations due to insufficient surface electromyography (sEMG) signals. A surgical technique called targeted muscle reinnervation (TMR) has achieved impressive improvements in myoelectric control by providing more sEMG control signals. In this case, some channels of sEMG signals are coupled after TMR, which limits the performance of conventional amplitude-based control for upper-limb prostheses. METHODS: In this paper, two different ways (training and algorithms) were attempted to solve the problem in a transhumeral amputee after TMR. Firstly, effect of rehabilitation training on generating independent sEMG signals was investigated. The results indicated that some sEMG signals recorded were still coupled over the targeted muscles after rehabilitation training for about two months. Secondly, pattern recognition (PR) algorithm was then applied to classify the sEMG signals. In the second way, to further improve the real-time performance of prosthetic control, a post-processing method named as mean absolute value-based (MAV-based) threshold switches was utilized. RESULTS: Using the improved algorithms, substantial improvement was shown in a subset of the modified Action Research Arm Test (ARAT). Compared with common PR control without post-processing method, the total scores increased more than 18% with majority vote and more than 58% with MAV-based threshold switches. The amputee was able to finish all the tasks within the allotted time with the standard MAV-based threshold switches. Subjectively the amputee preferred the PR control with MAV-based threshold switches and reported it to be more accurate and much smoother both in experiment and practical use. CONCLUSIONS: Although the sEMG signals were still coupled after rehabilitation training on the TMR patient, the online performance of the prosthetic operation was improved through application of PR control with combination of the MAV-based threshold switches. TRIAL REGISTRATION: Retrospectively registered http://www.chictr.org.cn/showproj.aspx?proj=22058. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12984-018-0376-9) contains supplementary material, which is available to authorized users.
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spelling pubmed-59465362018-05-17 Two ways to improve myoelectric control for a transhumeral amputee after targeted muscle reinnervation: a case study Xu, Yang Zhang, Dingguo Wang, Yang Feng, Juntao Xu, Wendong J Neuroeng Rehabil Research BACKGROUND: Myoelectric control of multifunctional prostheses is challenging for individuals with high-level amputations due to insufficient surface electromyography (sEMG) signals. A surgical technique called targeted muscle reinnervation (TMR) has achieved impressive improvements in myoelectric control by providing more sEMG control signals. In this case, some channels of sEMG signals are coupled after TMR, which limits the performance of conventional amplitude-based control for upper-limb prostheses. METHODS: In this paper, two different ways (training and algorithms) were attempted to solve the problem in a transhumeral amputee after TMR. Firstly, effect of rehabilitation training on generating independent sEMG signals was investigated. The results indicated that some sEMG signals recorded were still coupled over the targeted muscles after rehabilitation training for about two months. Secondly, pattern recognition (PR) algorithm was then applied to classify the sEMG signals. In the second way, to further improve the real-time performance of prosthetic control, a post-processing method named as mean absolute value-based (MAV-based) threshold switches was utilized. RESULTS: Using the improved algorithms, substantial improvement was shown in a subset of the modified Action Research Arm Test (ARAT). Compared with common PR control without post-processing method, the total scores increased more than 18% with majority vote and more than 58% with MAV-based threshold switches. The amputee was able to finish all the tasks within the allotted time with the standard MAV-based threshold switches. Subjectively the amputee preferred the PR control with MAV-based threshold switches and reported it to be more accurate and much smoother both in experiment and practical use. CONCLUSIONS: Although the sEMG signals were still coupled after rehabilitation training on the TMR patient, the online performance of the prosthetic operation was improved through application of PR control with combination of the MAV-based threshold switches. TRIAL REGISTRATION: Retrospectively registered http://www.chictr.org.cn/showproj.aspx?proj=22058. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12984-018-0376-9) contains supplementary material, which is available to authorized users. BioMed Central 2018-05-10 /pmc/articles/PMC5946536/ /pubmed/29747672 http://dx.doi.org/10.1186/s12984-018-0376-9 Text en © The Author(s) 2018 Open Access This 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
Xu, Yang
Zhang, Dingguo
Wang, Yang
Feng, Juntao
Xu, Wendong
Two ways to improve myoelectric control for a transhumeral amputee after targeted muscle reinnervation: a case study
title Two ways to improve myoelectric control for a transhumeral amputee after targeted muscle reinnervation: a case study
title_full Two ways to improve myoelectric control for a transhumeral amputee after targeted muscle reinnervation: a case study
title_fullStr Two ways to improve myoelectric control for a transhumeral amputee after targeted muscle reinnervation: a case study
title_full_unstemmed Two ways to improve myoelectric control for a transhumeral amputee after targeted muscle reinnervation: a case study
title_short Two ways to improve myoelectric control for a transhumeral amputee after targeted muscle reinnervation: a case study
title_sort two ways to improve myoelectric control for a transhumeral amputee after targeted muscle reinnervation: a case study
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5946536/
https://www.ncbi.nlm.nih.gov/pubmed/29747672
http://dx.doi.org/10.1186/s12984-018-0376-9
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