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Simulation of facial expressions using person-specific sEMG signals controlling a biomechanical face model

PURPOSE: Functional inoperability in advanced oral cancer is difficult to assess preoperatively. To assess functions of lips and tongue, biomechanical models are required. Apart from adjusting generic models to individual anatomy, muscle activation patterns (MAPs) driving patient-specific functional...

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Autores principales: Eskes, Merijn, Balm, Alfons J. M., van Alphen, Maarten J. A., Smeele, Ludi E., Stavness, Ian, van der Heijden, Ferdinand
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5754395/
https://www.ncbi.nlm.nih.gov/pubmed/28861702
http://dx.doi.org/10.1007/s11548-017-1659-5
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author Eskes, Merijn
Balm, Alfons J. M.
van Alphen, Maarten J. A.
Smeele, Ludi E.
Stavness, Ian
van der Heijden, Ferdinand
author_facet Eskes, Merijn
Balm, Alfons J. M.
van Alphen, Maarten J. A.
Smeele, Ludi E.
Stavness, Ian
van der Heijden, Ferdinand
author_sort Eskes, Merijn
collection PubMed
description PURPOSE: Functional inoperability in advanced oral cancer is difficult to assess preoperatively. To assess functions of lips and tongue, biomechanical models are required. Apart from adjusting generic models to individual anatomy, muscle activation patterns (MAPs) driving patient-specific functional movements are necessary to predict remaining functional outcome. We aim to evaluate how volunteer-specific MAPs derived from surface electromyographic (sEMG) signals control a biomechanical face model. METHODS: Muscle activity of seven facial muscles in six volunteers was measured bilaterally with sEMG. A triple camera set-up recorded 3D lip movement. The generic face model in ArtiSynth was adapted to our needs. We controlled the model using the volunteer-specific MAPs. Three activation strategies were tested: activating all muscles [Formula: see text] , selecting the three muscles showing highest muscle activity bilaterally [Formula: see text] —this was calculated by taking the mean of left and right muscles and then selecting the three with highest variance—and activating the muscles considered most relevant per instruction [Formula: see text] , bilaterally. The model’s lip movement was compared to the actual lip movement performed by the volunteers, using 3D correlation coefficients [Formula: see text] . RESULTS: The correlation coefficient between simulations and measurements with [Formula: see text] resulted in a median [Formula: see text] of 0.77. [Formula: see text] had a median [Formula: see text] of 0.78, whereas with [Formula: see text] the median [Formula: see text] decreased to 0.45. CONCLUSION: We demonstrated that MAPs derived from noninvasive sEMG measurements can control movement of the lips in a generic finite element face model with a median [Formula: see text] of 0.78. Ultimately, this is important to show the patient-specific residual movement using the patient’s own MAPs. When the required treatment tools and personalisation techniques for geometry and anatomy become available, this may enable surgeons to test the functional results of wedge excisions for lip cancer in a virtual environment and to weigh surgery versus organ-sparing radiotherapy or photodynamic therapy. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11548-017-1659-5) contains supplementary material, which is available to authorized users.
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spelling pubmed-57543952018-01-22 Simulation of facial expressions using person-specific sEMG signals controlling a biomechanical face model Eskes, Merijn Balm, Alfons J. M. van Alphen, Maarten J. A. Smeele, Ludi E. Stavness, Ian van der Heijden, Ferdinand Int J Comput Assist Radiol Surg Original Article PURPOSE: Functional inoperability in advanced oral cancer is difficult to assess preoperatively. To assess functions of lips and tongue, biomechanical models are required. Apart from adjusting generic models to individual anatomy, muscle activation patterns (MAPs) driving patient-specific functional movements are necessary to predict remaining functional outcome. We aim to evaluate how volunteer-specific MAPs derived from surface electromyographic (sEMG) signals control a biomechanical face model. METHODS: Muscle activity of seven facial muscles in six volunteers was measured bilaterally with sEMG. A triple camera set-up recorded 3D lip movement. The generic face model in ArtiSynth was adapted to our needs. We controlled the model using the volunteer-specific MAPs. Three activation strategies were tested: activating all muscles [Formula: see text] , selecting the three muscles showing highest muscle activity bilaterally [Formula: see text] —this was calculated by taking the mean of left and right muscles and then selecting the three with highest variance—and activating the muscles considered most relevant per instruction [Formula: see text] , bilaterally. The model’s lip movement was compared to the actual lip movement performed by the volunteers, using 3D correlation coefficients [Formula: see text] . RESULTS: The correlation coefficient between simulations and measurements with [Formula: see text] resulted in a median [Formula: see text] of 0.77. [Formula: see text] had a median [Formula: see text] of 0.78, whereas with [Formula: see text] the median [Formula: see text] decreased to 0.45. CONCLUSION: We demonstrated that MAPs derived from noninvasive sEMG measurements can control movement of the lips in a generic finite element face model with a median [Formula: see text] of 0.78. Ultimately, this is important to show the patient-specific residual movement using the patient’s own MAPs. When the required treatment tools and personalisation techniques for geometry and anatomy become available, this may enable surgeons to test the functional results of wedge excisions for lip cancer in a virtual environment and to weigh surgery versus organ-sparing radiotherapy or photodynamic therapy. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11548-017-1659-5) contains supplementary material, which is available to authorized users. Springer International Publishing 2017-08-31 2018 /pmc/articles/PMC5754395/ /pubmed/28861702 http://dx.doi.org/10.1007/s11548-017-1659-5 Text en © The Author(s) 2017 Open AccessThis 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.
spellingShingle Original Article
Eskes, Merijn
Balm, Alfons J. M.
van Alphen, Maarten J. A.
Smeele, Ludi E.
Stavness, Ian
van der Heijden, Ferdinand
Simulation of facial expressions using person-specific sEMG signals controlling a biomechanical face model
title Simulation of facial expressions using person-specific sEMG signals controlling a biomechanical face model
title_full Simulation of facial expressions using person-specific sEMG signals controlling a biomechanical face model
title_fullStr Simulation of facial expressions using person-specific sEMG signals controlling a biomechanical face model
title_full_unstemmed Simulation of facial expressions using person-specific sEMG signals controlling a biomechanical face model
title_short Simulation of facial expressions using person-specific sEMG signals controlling a biomechanical face model
title_sort simulation of facial expressions using person-specific semg signals controlling a biomechanical face model
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5754395/
https://www.ncbi.nlm.nih.gov/pubmed/28861702
http://dx.doi.org/10.1007/s11548-017-1659-5
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