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Integrating computer vision to prosthetic hand control with sEMG: Preliminary results in grasp classification

The myoelectric prosthesis is a promising tool to restore the hand abilities of amputees, but the classification accuracy of surface electromyography (sEMG) is not high enough for real-time application. Researchers proposed integrating sEMG signals with another feature that is not affected by amputa...

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Autores principales: Wang, Shuo, Zheng, Jingjing, Huang, Ziwei, Zhang, Xiaoqin, Prado da Fonseca, Vinicius, Zheng, Bin, Jiang, Xianta
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9538562/
https://www.ncbi.nlm.nih.gov/pubmed/36212614
http://dx.doi.org/10.3389/frobt.2022.948238
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author Wang, Shuo
Zheng, Jingjing
Huang, Ziwei
Zhang, Xiaoqin
Prado da Fonseca, Vinicius
Zheng, Bin
Jiang, Xianta
author_facet Wang, Shuo
Zheng, Jingjing
Huang, Ziwei
Zhang, Xiaoqin
Prado da Fonseca, Vinicius
Zheng, Bin
Jiang, Xianta
author_sort Wang, Shuo
collection PubMed
description The myoelectric prosthesis is a promising tool to restore the hand abilities of amputees, but the classification accuracy of surface electromyography (sEMG) is not high enough for real-time application. Researchers proposed integrating sEMG signals with another feature that is not affected by amputation. The strong coordination between vision and hand manipulation makes us consider including visual information in prosthetic hand control. In this study, we identified a sweet period during the early reaching phase in which the vision data could yield a higher accuracy in classifying the grasp patterns. Moreover, the visual classification results from the sweet period could be naturally integrated with sEMG data collected during the grasp phase. After the integration, the accuracy of grasp classification increased from 85.5% (only sEMG) to 90.06% (integrated). Knowledge gained from this study encourages us to further explore the methods for incorporating computer vision into myoelectric data to enhance the movement control of prosthetic hands.
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spelling pubmed-95385622022-10-08 Integrating computer vision to prosthetic hand control with sEMG: Preliminary results in grasp classification Wang, Shuo Zheng, Jingjing Huang, Ziwei Zhang, Xiaoqin Prado da Fonseca, Vinicius Zheng, Bin Jiang, Xianta Front Robot AI Robotics and AI The myoelectric prosthesis is a promising tool to restore the hand abilities of amputees, but the classification accuracy of surface electromyography (sEMG) is not high enough for real-time application. Researchers proposed integrating sEMG signals with another feature that is not affected by amputation. The strong coordination between vision and hand manipulation makes us consider including visual information in prosthetic hand control. In this study, we identified a sweet period during the early reaching phase in which the vision data could yield a higher accuracy in classifying the grasp patterns. Moreover, the visual classification results from the sweet period could be naturally integrated with sEMG data collected during the grasp phase. After the integration, the accuracy of grasp classification increased from 85.5% (only sEMG) to 90.06% (integrated). Knowledge gained from this study encourages us to further explore the methods for incorporating computer vision into myoelectric data to enhance the movement control of prosthetic hands. Frontiers Media S.A. 2022-09-23 /pmc/articles/PMC9538562/ /pubmed/36212614 http://dx.doi.org/10.3389/frobt.2022.948238 Text en Copyright © 2022 Wang, Zheng, Huang, Zhang, Prado da Fonseca, Zheng and Jiang. 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 Robotics and AI
Wang, Shuo
Zheng, Jingjing
Huang, Ziwei
Zhang, Xiaoqin
Prado da Fonseca, Vinicius
Zheng, Bin
Jiang, Xianta
Integrating computer vision to prosthetic hand control with sEMG: Preliminary results in grasp classification
title Integrating computer vision to prosthetic hand control with sEMG: Preliminary results in grasp classification
title_full Integrating computer vision to prosthetic hand control with sEMG: Preliminary results in grasp classification
title_fullStr Integrating computer vision to prosthetic hand control with sEMG: Preliminary results in grasp classification
title_full_unstemmed Integrating computer vision to prosthetic hand control with sEMG: Preliminary results in grasp classification
title_short Integrating computer vision to prosthetic hand control with sEMG: Preliminary results in grasp classification
title_sort integrating computer vision to prosthetic hand control with semg: preliminary results in grasp classification
topic Robotics and AI
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9538562/
https://www.ncbi.nlm.nih.gov/pubmed/36212614
http://dx.doi.org/10.3389/frobt.2022.948238
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