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 | |
| Publication status | Published - 1 Apr 2017 |
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Predicting 3D lip shapes using facial surface EMG. / Eskes, Merijn; Van Alphen, Maarten J.A.; Balm, Alfons J.M.; Smeele, Ludi E.; Brandsma, Dieta; Van Der Heijden, Ferdinand.
In: PLoS ONE, Vol. 12, No. 4, e0175025, 01.04.2017.Research output: Contribution to journal › Article › Academic › peer-review
TY - JOUR
T1 - Predicting 3D lip shapes using facial surface EMG
AU - Eskes, Merijn
AU - Van Alphen, Maarten J.A.
AU - Balm, Alfons J.M.
AU - Smeele, Ludi E.
AU - Brandsma, Dieta
AU - Van Der Heijden, Ferdinand
PY - 2017/4/1
Y1 - 2017/4/1
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.
UR - http://www.scopus.com/inward/record.url?scp=85017416621&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0175025
DO - 10.1371/journal.pone.0175025
M3 - Article
VL - 12
JO - PLoS ONE
JF - PLoS ONE
SN - 1932-6203
IS - 4
M1 - e0175025
ER -