Predicting 3D lip shapes using facial surface EMG

Merijn Eskes; Maarten J.A. van Alphen; Alfons J.M. Balm; Ludi E. Smeele; Dieta Brandsma; Ferdinand van der Heijden

doi:10.1371/journal.pone.0175025

Predicting 3D lip shapes using facial surface EMG

Merijn Eskes, Maarten J.A. van Alphen, Alfons J.M. Balm, Ludi E. Smeele, Dieta Brandsma, Ferdinand van der Heijden

Research output: Contribution to journal › Article › Academic › peer-review

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Abstract

Aim: The aim of this study is to prove that facial surface electromyography (sEMG) conveys sufficient information to predict 3D lip shapes. High sEMG predictive accuracy implies we could train a neural control model for activation of biomechanical models by simultaneously recording sEMG signals and their associated motions.

Materials and methods: With a stereo camera set-up, we recorded 3D lip shapes and simultaneously performed sEMG measurements of the facial muscles, applying principal component analysis (PCA) and a modified general regression neural network (GRNN) to link the sEMG measurements to 3D lip shapes. To test reproducibility, we conducted our experiment on five volunteers, evaluating several sEMG features and window lengths in unipolar and bipolar configurations in search of the optimal settings for facial sEMG.

Conclusions: The errors of the two methods were comparable. We managed to predict 3D lip shapes with a mean accuracy of 2.76 mm when using the PCA method and 2.78 mm when using modified GRNN. Whereas performance improved with shorter window lengths, feature type and configuration had little influence.

Original language	English
Article number	e0175025
Journal	PLoS ONE
Volume	12
Issue number	4
DOIs	https://doi.org/10.1371/journal.pone.0175025
Publication status	Published - 1 Apr 2017

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10.1371/journal.pone.0175025Licence: CC BY

Eskes2017predictingFinal published version, 1.62 MBLicence: CC BY

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title = "Predicting 3D lip shapes using facial surface EMG",

abstract = "Aim: The aim of this study is to prove that facial surface electromyography (sEMG) conveys sufficient information to predict 3D lip shapes. High sEMG predictive accuracy implies we could train a neural control model for activation of biomechanical models by simultaneously recording sEMG signals and their associated motions.Materials and methods: With a stereo camera set-up, we recorded 3D lip shapes and simultaneously performed sEMG measurements of the facial muscles, applying principal component analysis (PCA) and a modified general regression neural network (GRNN) to link the sEMG measurements to 3D lip shapes. To test reproducibility, we conducted our experiment on five volunteers, evaluating several sEMG features and window lengths in unipolar and bipolar configurations in search of the optimal settings for facial sEMG. Conclusions: The errors of the two methods were comparable. We managed to predict 3D lip shapes with a mean accuracy of 2.76 mm when using the PCA method and 2.78 mm when using modified GRNN. Whereas performance improved with shorter window lengths, feature type and configuration had little influence.",

author = "Merijn Eskes and {van Alphen}, {Maarten J.A.} and Balm, {Alfons J.M.} and Smeele, {Ludi E.} and Dieta Brandsma and {van der Heijden}, Ferdinand",

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AU - Balm, Alfons J.M.

AU - Smeele, Ludi E.

AU - Brandsma, Dieta

AU - van der Heijden, Ferdinand

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N2 - Aim: The aim of this study is to prove that facial surface electromyography (sEMG) conveys sufficient information to predict 3D lip shapes. High sEMG predictive accuracy implies we could train a neural control model for activation of biomechanical models by simultaneously recording sEMG signals and their associated motions.Materials and methods: With a stereo camera set-up, we recorded 3D lip shapes and simultaneously performed sEMG measurements of the facial muscles, applying principal component analysis (PCA) and a modified general regression neural network (GRNN) to link the sEMG measurements to 3D lip shapes. To test reproducibility, we conducted our experiment on five volunteers, evaluating several sEMG features and window lengths in unipolar and bipolar configurations in search of the optimal settings for facial sEMG. Conclusions: The errors of the two methods were comparable. We managed to predict 3D lip shapes with a mean accuracy of 2.76 mm when using the PCA method and 2.78 mm when using modified GRNN. Whereas performance improved with shorter window lengths, feature type and configuration had little influence.

AB - Aim: The aim of this study is to prove that facial surface electromyography (sEMG) conveys sufficient information to predict 3D lip shapes. High sEMG predictive accuracy implies we could train a neural control model for activation of biomechanical models by simultaneously recording sEMG signals and their associated motions.Materials and methods: With a stereo camera set-up, we recorded 3D lip shapes and simultaneously performed sEMG measurements of the facial muscles, applying principal component analysis (PCA) and a modified general regression neural network (GRNN) to link the sEMG measurements to 3D lip shapes. To test reproducibility, we conducted our experiment on five volunteers, evaluating several sEMG features and window lengths in unipolar and bipolar configurations in search of the optimal settings for facial sEMG. Conclusions: The errors of the two methods were comparable. We managed to predict 3D lip shapes with a mean accuracy of 2.76 mm when using the PCA method and 2.78 mm when using modified GRNN. Whereas performance improved with shorter window lengths, feature type and configuration had little influence.

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Predicting 3D lip shapes using facial surface EMG

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