Predicting 3D lip shapes using facial surface EMG.
Predicting 3D lip shapes using facial surface EMG.
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DOI：
10.1371/journal.pone.0175025
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发表时间：
2017
期刊：
影响因子：
3.7
通讯作者：
van der Heijden F
中科院分区：
文献类型：
--
作者：
Eskes M;van Alphen MJ;Balm AJ;Smeele LE;Brandsma D;van der Heijden F
本研究的目的是证明面部肌电图（sEMG）传达了足够的信息来预测三维唇形。高的表面肌电信号预测精度意味着我们可以通过同时记录表面肌电信号及其相关运动来训练激活生物力学模型的神经控制模型。通过立体摄像机设置，我们记录了3D唇形，同时对面部肌肉进行了肌电信号测量，应用主成分分析（PCA）和改进的广义回归神经网络（GRNN）将肌电信号测量与3D唇形联系起来。为了测试再现性，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肌电信号特征和窗口长度，以寻找面部肌电信号的最佳设置。两种方法的误差具有可比性。我们成功地预测了三维唇形，使用PCA方法的平均精度为2.76 mm，使用改进的GRNN方法的平均精度为2.78 mm。虽然较短的窗口长度提高了性能，但特征类型和配置几乎没有影响。
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.