Eskes Merijn, van Alphen Maarten J A, Balm Alfons J M, Smeele Ludi E, Brandsma Dieta, van der Heijden Ferdinand
Dept of Head and Neck Oncology and Surgery, Netherlands Cancer Institute, Amsterdam, the Netherlands.
MIRA Institute of Biomedical Engineering and Technical Medicine, University of Twente, Enschede, the Netherlands.
PLoS One. 2017 Apr 13;12(4):e0175025. doi: 10.1371/journal.pone.0175025. eCollection 2017.
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.
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.
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.
本研究旨在证明面部表面肌电图（sEMG）能够传达足够信息以预测三维嘴唇形状。较高的sEMG预测准确性意味着我们可以通过同时记录sEMG信号及其相关运动来训练一个神经控制模型，用于激活生物力学模型。
利用立体相机设置，我们记录了三维嘴唇形状，并同时对面部肌肉进行sEMG测量，应用主成分分析（PCA）和改进的广义回归神经网络（GRNN）将sEMG测量结果与三维嘴唇形状联系起来。为了测试可重复性，我们对五名志愿者进行了实验，评估了单极和双极配置下的几种sEMG特征和窗口长度，以寻找面部sEMG的最佳设置。
两种方法的误差相当。使用PCA方法时，我们成功预测三维嘴唇形状的平均准确率为2.76毫米，使用改进的GRNN时为2.78毫米。虽然较短的窗口长度可提高性能，但特征类型和配置影响不大。
