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Predicting 3D lip shapes using facial surface EMG.

Merijn Eskes1,2, Maarten J A van Alphen1, Alfons J M Balm1,3

  • 1Dept of Head and Neck Oncology and Surgery, Netherlands Cancer Institute, Amsterdam, the Netherlands.

Plos One
|April 14, 2017
PubMed
Summary

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Facial surface electromyography (sEMG) can accurately predict 3D lip shapes. This finding enables the development of neural control models for biomechanical systems using sEMG signals and motion data.

Area of Science:

  • Biomedical Engineering
  • Neuroscience
  • Computer Vision

Background:

  • Facial electromyography (sEMG) measures muscle electrical activity.
  • Accurate 3D lip shape prediction is crucial for various applications, including speech synthesis and human-computer interaction.
  • Current methods may lack the precision needed for detailed lip motion analysis.

Purpose of the Study:

  • To determine if facial sEMG signals contain sufficient information to predict 3D lip shapes.
  • To establish the predictive accuracy of sEMG for lip kinematics.
  • To lay the groundwork for neural control models of biomechanical systems.

Main Methods:

  • Simultaneous recording of 3D lip shapes using stereo cameras and facial sEMG data.
  • Application of Principal Component Analysis (PCA) and a modified General Regression Neural Network (GRNN) to correlate sEMG signals with lip shapes.

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  • Evaluation of different sEMG features, window lengths, and configurations across five volunteers to optimize prediction.

    • Main Results:

    • Both PCA and modified GRNN methods achieved comparable accuracy in predicting 3D lip shapes.
    • Mean prediction accuracy was approximately 2.76 mm for PCA and 2.78 mm for GRNN.
    • Shorter window lengths improved prediction performance, while feature type and configuration had minimal impact.

    Conclusions:

    • Facial sEMG is a viable and accurate method for predicting 3D lip shapes.
    • The developed methods demonstrate the potential for real-time lip motion tracking and control.
    • This research paves the way for advanced neural control strategies in biomechanical modeling.