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The influence of common component on myoelectric pattern recognition

OBJECTIVE: Using the Twente Medical Systems international B.V. (TMSi) electromyography (EMG) system, active signal shielding was applied to clean signals and create data without interference and cable movement artifacts. TMSi, used in high-density surface EMG pattern recognition, controls myoelectri...

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Autores principales: Yao, Bo, Peng, Yun, Zhang, Xu, Zhang, Yingchun, Zhou, Ping, Pu, Jiangbo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7370808/
https://www.ncbi.nlm.nih.gov/pubmed/32208942
http://dx.doi.org/10.1177/0300060520903617
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author Yao, Bo
Peng, Yun
Zhang, Xu
Zhang, Yingchun
Zhou, Ping
Pu, Jiangbo
author_facet Yao, Bo
Peng, Yun
Zhang, Xu
Zhang, Yingchun
Zhou, Ping
Pu, Jiangbo
author_sort Yao, Bo
collection PubMed
description OBJECTIVE: Using the Twente Medical Systems international B.V. (TMSi) electromyography (EMG) system, active signal shielding was applied to clean signals and create data without interference and cable movement artifacts. TMSi, used in high-density surface EMG pattern recognition, controls myoelectric rehabilitation robots, yet few have studied how active signal shielding influences pattern recognition. This study aimed to investigate how active signal shielding used within the TMSi influenced motion pattern recognition. METHODS: Surface EMG of dominant side forearm and hand muscles was studied in eight healthy participants. The common component’s influence was accessed by the classification performance of wrist and hand functional movements. RESULTS: The classification performance of EMG signals with the common component was obviously lower than signals without the common component using one to five electrodes. Conversely, a higher motion classification performance was achieved using signals with the common component using over 12 electrodes. Optimal channel distribution was examined based on the sequential feed-forward selection method, showing that the common component affected the optimal channel location. CONCLUSIONS: Active signal shielding in the TMSi improved classification accuracy in motion pattern recognition when over 12 electrodes were used. The optimal channel distribution was related to the common component when using the TMSi.
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spelling pubmed-73708082020-07-29 The influence of common component on myoelectric pattern recognition Yao, Bo Peng, Yun Zhang, Xu Zhang, Yingchun Zhou, Ping Pu, Jiangbo J Int Med Res Special Issue: Application of emerging and advanced rehabilitation engineering techniques in stroke recovery OBJECTIVE: Using the Twente Medical Systems international B.V. (TMSi) electromyography (EMG) system, active signal shielding was applied to clean signals and create data without interference and cable movement artifacts. TMSi, used in high-density surface EMG pattern recognition, controls myoelectric rehabilitation robots, yet few have studied how active signal shielding influences pattern recognition. This study aimed to investigate how active signal shielding used within the TMSi influenced motion pattern recognition. METHODS: Surface EMG of dominant side forearm and hand muscles was studied in eight healthy participants. The common component’s influence was accessed by the classification performance of wrist and hand functional movements. RESULTS: The classification performance of EMG signals with the common component was obviously lower than signals without the common component using one to five electrodes. Conversely, a higher motion classification performance was achieved using signals with the common component using over 12 electrodes. Optimal channel distribution was examined based on the sequential feed-forward selection method, showing that the common component affected the optimal channel location. CONCLUSIONS: Active signal shielding in the TMSi improved classification accuracy in motion pattern recognition when over 12 electrodes were used. The optimal channel distribution was related to the common component when using the TMSi. SAGE Publications 2020-03-25 /pmc/articles/PMC7370808/ /pubmed/32208942 http://dx.doi.org/10.1177/0300060520903617 Text en © The Author(s) 2020 https://creativecommons.org/licenses/by-nc/4.0/ Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Special Issue: Application of emerging and advanced rehabilitation engineering techniques in stroke recovery
Yao, Bo
Peng, Yun
Zhang, Xu
Zhang, Yingchun
Zhou, Ping
Pu, Jiangbo
The influence of common component on myoelectric pattern recognition
title The influence of common component on myoelectric pattern recognition
title_full The influence of common component on myoelectric pattern recognition
title_fullStr The influence of common component on myoelectric pattern recognition
title_full_unstemmed The influence of common component on myoelectric pattern recognition
title_short The influence of common component on myoelectric pattern recognition
title_sort influence of common component on myoelectric pattern recognition
topic Special Issue: Application of emerging and advanced rehabilitation engineering techniques in stroke recovery
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7370808/
https://www.ncbi.nlm.nih.gov/pubmed/32208942
http://dx.doi.org/10.1177/0300060520903617
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