SEMG-assisted inverse modelling of 3D lip movement: A feasibility study towards person-specific modelling

M Eskes, Alfonsus Jacobus Maria Balm, Maarten J.A. Van Alphen, Ludi E. Smeele, Ian Stavness, Ferdinand Van Der Heijden

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Abstract

We propose a surface-electromyographic (sEMG) assisted inverse-modelling (IM) approach for a biomechanical model of the face to obtain realistic person-specific muscle activations (MA) by tracking movements as well as innervation trajectories. We obtained sEMG data of facial muscles and 3D positions of lip markers in six volunteers and, using a generic finite element (FE) face model in ArtiSynth, performed inverse static optimisation with and without sEMG tracking on both simulation data and experimental data. IM with simulated data and experimental data without sEMG data showed good correlations of tracked positions (0.93 and 0.67) and poor correlations of MA (0.27 and 0.20). When utilising the sEMG-assisted IM approach, MA correlations increased drastically (0.83 and 0.59) without sacrificing performance in position correlations (0.92 and 0.70). RMS errors show similar trends with an error of 0.15 in MA and of 1.10 mm in position. Therefore, we conclude that we were able to demonstrate the feasibility of an sEMG-assisted inverse modelling algorithm for the perioral region. This approach may help to solve the ambiguity problem in inverse modelling and may be useful, for instance, in future applications for preoperatively predicting treatment-related function loss.
Original languageEnglish
Article number17729
JournalScientific reports
Volume7
Issue number1
DOIs
Publication statusPublished - 18 Dec 2017

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Feasibility Studies
Lip
Muscles
Facial Muscles
Volunteers
Therapeutics

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title = "SEMG-assisted inverse modelling of 3D lip movement: A feasibility study towards person-specific modelling",
abstract = "We propose a surface-electromyographic (sEMG) assisted inverse-modelling (IM) approach for a biomechanical model of the face to obtain realistic person-specific muscle activations (MA) by tracking movements as well as innervation trajectories. We obtained sEMG data of facial muscles and 3D positions of lip markers in six volunteers and, using a generic finite element (FE) face model in ArtiSynth, performed inverse static optimisation with and without sEMG tracking on both simulation data and experimental data. IM with simulated data and experimental data without sEMG data showed good correlations of tracked positions (0.93 and 0.67) and poor correlations of MA (0.27 and 0.20). When utilising the sEMG-assisted IM approach, MA correlations increased drastically (0.83 and 0.59) without sacrificing performance in position correlations (0.92 and 0.70). RMS errors show similar trends with an error of 0.15 in MA and of 1.10 mm in position. Therefore, we conclude that we were able to demonstrate the feasibility of an sEMG-assisted inverse modelling algorithm for the perioral region. This approach may help to solve the ambiguity problem in inverse modelling and may be useful, for instance, in future applications for preoperatively predicting treatment-related function loss.",
author = "M Eskes and Balm, {Alfonsus Jacobus Maria} and {Van Alphen}, {Maarten J.A.} and Smeele, {Ludi E.} and Ian Stavness and {Van Der Heijden}, Ferdinand",
year = "2017",
month = "12",
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doi = "10.1038/s41598-017-17790-4",
language = "English",
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journal = "Scientific reports",
issn = "2045-2322",
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SEMG-assisted inverse modelling of 3D lip movement: A feasibility study towards person-specific modelling. / Eskes, M ; Balm, Alfonsus Jacobus Maria; Van Alphen, Maarten J.A.; Smeele, Ludi E.; Stavness, Ian; Van Der Heijden, Ferdinand.

In: Scientific reports, Vol. 7, No. 1, 17729, 18.12.2017.

Research output: Contribution to journalArticleAcademicpeer-review

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AU - Eskes, M

AU - Balm, Alfonsus Jacobus Maria

AU - Van Alphen, Maarten J.A.

AU - Smeele, Ludi E.

AU - Stavness, Ian

AU - Van Der Heijden, Ferdinand

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AB - We propose a surface-electromyographic (sEMG) assisted inverse-modelling (IM) approach for a biomechanical model of the face to obtain realistic person-specific muscle activations (MA) by tracking movements as well as innervation trajectories. We obtained sEMG data of facial muscles and 3D positions of lip markers in six volunteers and, using a generic finite element (FE) face model in ArtiSynth, performed inverse static optimisation with and without sEMG tracking on both simulation data and experimental data. IM with simulated data and experimental data without sEMG data showed good correlations of tracked positions (0.93 and 0.67) and poor correlations of MA (0.27 and 0.20). When utilising the sEMG-assisted IM approach, MA correlations increased drastically (0.83 and 0.59) without sacrificing performance in position correlations (0.92 and 0.70). RMS errors show similar trends with an error of 0.15 in MA and of 1.10 mm in position. Therefore, we conclude that we were able to demonstrate the feasibility of an sEMG-assisted inverse modelling algorithm for the perioral region. This approach may help to solve the ambiguity problem in inverse modelling and may be useful, for instance, in future applications for preoperatively predicting treatment-related function loss.

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