Background: The midfacial plane overlying the zygomaticus major (ZM) muscle undergoes unique compressive/tensile forces and displacements that vary with emotional expressions. In cases of facial paralysis, surgeons attempt to recapitulate these dynamics with muscle transplantation. New technologies attempting to simulate these movements require a holistic understanding of the biomechanical and neuromuscular characteristics of the midface and lower face. Methods: We recruited 33 healthy participants (12 men, 21 women) and measured physical movements, forces, and surface electromyography (sEMG) characteristics of the midface associated with smiling and puckering expressions. We assessed measures of central tendency and quantified characteristic neuromuscular features. We also trained machine learning classifiers to accurately predict muscular loading based on the sEMG features. Results: The midfacial ZM plane experiences a maximum contraction of 9.8% ± 3.1% during smiling while exerting a force of 0.93 ± 0.48 N. Conversely, it experiences a maximum extension of 9.2% ± 3.4% and a tension of 0.47 ± 0.23 N during puckering. Furthermore, we observed the emergence of characteristic neuromuscular features associated with varying degrees of biomechanical loading, which allowed us to successfully train machine classifiers to differentiate between facial expressions with up to 86% accuracy. Conclusions: We have successfully performed multimodal assessment of the physical and functional parameters (displacement, force, and sEMG patterns) associated with the midfacial ZM plane. Our results present a benchmark for the quantitative assessment of the midface and lower face, which could provide metrics for functional rehabilitation or even training data for technologies that may attempt to recapitulate facial animation.
Garg et al. (Wed,) studied this question.