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

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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 languageEnglish
    Article numbere0175025
    JournalPLoS ONE
    Volume12
    Issue number4
    DOIs
    Publication statusPublished - 1 Apr 2017

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    Eskes, M., Van Alphen, M. J. A., Balm, A. J. M., Smeele, L. E., Brandsma, D., & Van Der Heijden, F. (2017). Predicting 3D lip shapes using facial surface EMG. PLoS ONE, 12(4), [e0175025]. https://doi.org/10.1371/journal.pone.0175025