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Simulation of facial expressions using person-specific sEMG signals controlling a biomechanical face model
Authors:Merijn Eskes  Alfons J. M. Balm  Maarten J. A. van Alphen  Ludi E. Smeele  Ian Stavness  Ferdinand van der Heijden
Affiliation:1.Department of Head and Neck Oncology and Surgery,Netherlands Cancer Institute,Amsterdam,The Netherlands;2.MIRA Institute of Biomedical Engineering and Technical Medicine,University of Twente,Enschede,The Netherlands;3.Department of Oral and Maxillofacial Surgery,Academic Medical Center,Amsterdam,The Netherlands;4.Department of Computer Science,University of Saskatchewan,Saskatoon,Canada;5.Amsterdam,The Netherlands
Abstract:

Purpose

Functional inoperability in advanced oral cancer is difficult to assess preoperatively. To assess functions of lips and tongue, biomechanical models are required. Apart from adjusting generic models to individual anatomy, muscle activation patterns (MAPs) driving patient-specific functional movements are necessary to predict remaining functional outcome. We aim to evaluate how volunteer-specific MAPs derived from surface electromyographic (sEMG) signals control a biomechanical face model.

Methods

Muscle activity of seven facial muscles in six volunteers was measured bilaterally with sEMG. A triple camera set-up recorded 3D lip movement. The generic face model in ArtiSynth was adapted to our needs. We controlled the model using the volunteer-specific MAPs. Three activation strategies were tested: activating all muscles ((hbox {act}_mathrm{all})), selecting the three muscles showing highest muscle activity bilaterally ((hbox {act}_3))—this was calculated by taking the mean of left and right muscles and then selecting the three with highest variance—and activating the muscles considered most relevant per instruction ((hbox {act}_mathrm{rel})), bilaterally. The model’s lip movement was compared to the actual lip movement performed by the volunteers, using 3D correlation coefficients ((rho )).

Results

The correlation coefficient between simulations and measurements with (hbox {act}_mathrm{rel}) resulted in a median (rho ) of 0.77. (hbox {act}_3) had a median (rho ) of 0.78, whereas with (hbox {act}_mathrm{all}) the median (rho ) decreased to 0.45.

Conclusion

We demonstrated that MAPs derived from noninvasive sEMG measurements can control movement of the lips in a generic finite element face model with a median (rho ) of 0.78. Ultimately, this is important to show the patient-specific residual movement using the patient’s own MAPs. When the required treatment tools and personalisation techniques for geometry and anatomy become available, this may enable surgeons to test the functional results of wedge excisions for lip cancer in a virtual environment and to weigh surgery versus organ-sparing radiotherapy or photodynamic therapy.
Keywords:
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