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Influence of muscle fatigue on electromyogram–kinematic correlation during robot-assisted upper limb training
INTRODUCTION: Studies on adaptive robot-assisted upper limb training interactions do not often consider the implications of muscle fatigue sufficiently. METHODS: To explore this, we initially assessed muscle fatigue in 10 healthy subjects using two electromyogram features, namely average power and m...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7079312/ https://www.ncbi.nlm.nih.gov/pubmed/32206337 http://dx.doi.org/10.1177/2055668320903014 |
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author | Poyil, Azeemsha T Steuber, Volker Amirabdollahian, Farshid |
author_facet | Poyil, Azeemsha T Steuber, Volker Amirabdollahian, Farshid |
author_sort | Poyil, Azeemsha T |
collection | PubMed |
description | INTRODUCTION: Studies on adaptive robot-assisted upper limb training interactions do not often consider the implications of muscle fatigue sufficiently. METHODS: To explore this, we initially assessed muscle fatigue in 10 healthy subjects using two electromyogram features, namely average power and median power frequency, during an assist-as-needed interaction with HapticMaster robot. Since robotic assistance resulted in a variable fatigue profile across participants, a completely tiring experiment, without a robot in the loop, was also designed to confirm the results. RESULTS: A significant increase in average power and a decrease in median frequency were observed in the most active muscles. Average power in the frequency band of 0.8–2.5 Hz and median frequency in the band of 20–450 Hz are potential fatigue indicators. Also, comparing the Spearman’s correlation coefficients (between the electromyogram average power and the kinematic force) across trials indicated that correlation was reduced as individual muscles were fatigued. CONCLUSIONS: Confirming fatigue indicators, this study concludes that robotic assistance based on user’s performance resulted in lesser muscle fatigue, which caused an increase in electromyogram–force correlation. We now intend to utilise the electromyogram and kinematic features for auto-adaptation of therapeutic human–robot interactions. |
format | Online Article Text |
id | pubmed-7079312 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-70793122020-03-23 Influence of muscle fatigue on electromyogram–kinematic correlation during robot-assisted upper limb training Poyil, Azeemsha T Steuber, Volker Amirabdollahian, Farshid J Rehabil Assist Technol Eng Original Article INTRODUCTION: Studies on adaptive robot-assisted upper limb training interactions do not often consider the implications of muscle fatigue sufficiently. METHODS: To explore this, we initially assessed muscle fatigue in 10 healthy subjects using two electromyogram features, namely average power and median power frequency, during an assist-as-needed interaction with HapticMaster robot. Since robotic assistance resulted in a variable fatigue profile across participants, a completely tiring experiment, without a robot in the loop, was also designed to confirm the results. RESULTS: A significant increase in average power and a decrease in median frequency were observed in the most active muscles. Average power in the frequency band of 0.8–2.5 Hz and median frequency in the band of 20–450 Hz are potential fatigue indicators. Also, comparing the Spearman’s correlation coefficients (between the electromyogram average power and the kinematic force) across trials indicated that correlation was reduced as individual muscles were fatigued. CONCLUSIONS: Confirming fatigue indicators, this study concludes that robotic assistance based on user’s performance resulted in lesser muscle fatigue, which caused an increase in electromyogram–force correlation. We now intend to utilise the electromyogram and kinematic features for auto-adaptation of therapeutic human–robot interactions. SAGE Publications 2020-03-16 /pmc/articles/PMC7079312/ /pubmed/32206337 http://dx.doi.org/10.1177/2055668320903014 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 | Original Article Poyil, Azeemsha T Steuber, Volker Amirabdollahian, Farshid Influence of muscle fatigue on electromyogram–kinematic correlation during robot-assisted upper limb training |
title | Influence of muscle fatigue on electromyogram–kinematic correlation during robot-assisted upper limb training |
title_full | Influence of muscle fatigue on electromyogram–kinematic correlation during robot-assisted upper limb training |
title_fullStr | Influence of muscle fatigue on electromyogram–kinematic correlation during robot-assisted upper limb training |
title_full_unstemmed | Influence of muscle fatigue on electromyogram–kinematic correlation during robot-assisted upper limb training |
title_short | Influence of muscle fatigue on electromyogram–kinematic correlation during robot-assisted upper limb training |
title_sort | influence of muscle fatigue on electromyogram–kinematic correlation during robot-assisted upper limb training |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7079312/ https://www.ncbi.nlm.nih.gov/pubmed/32206337 http://dx.doi.org/10.1177/2055668320903014 |
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