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This paper presents development of a three dimensional finite element model for simulations of a conformable piezoelectric sensor utilizing COMSOL Multiphysics. The sensor has a multi-layer structure composed of four circular piezoelectric elements arranged in an array structure laminated on a soft substrate and is capable of providing a strain mapping of soft tissue surfaces for spatiotemporal biokinematic assessment of the facial skin. Here, we provide the finite element method (FEM) for the sensor to predict its electromechanical behavior. This paper studies the effect of the design parameters such as dimensions of the piezoelectric sensor and the substrate on voltage sensitivity and sensor compliance. The FEM model is established to understand the underlying physics and guide the mechanical characterization of the system. The developed model is experimentally verified through two series of tests. The first set of tests involve comprehensive in vitro mechanical testing to provide accurate measurements of strain during compression, stretching, and bending. The second set of tests present in vivo experiments on healthy and amyotrophic lateral sclerosis subjects. The experimentally verified FEM model provides a detailed insight into analyzing the response of the sensor which establishes new design rules for next generations of conformable piezoelectric sensors. 

Devices that facilitate nonverbal communication typically require high computational loads or have rigid and bulky form factors that are unsuitable for use on the face or on other curvilinear body surfaces. Here, we report the design and pilot testing of an integrated system for decoding facial strains and for predicting facial kinematics. The system consists of mass-manufacturable, conformable piezoelectric thin films for strain mapping; multiphysics modelling for analysing the nonlinear mechanical interactions between the conformable device and the epidermis; and three-dimensional digital image correlation for reconstructing soft-tissue surfaces under dynamic deformations as well as for informing device design and placement. In healthy individuals and in patients with amyotrophic lateral sclerosis, we show that the piezoelectric thin films, coupled with algorithms for the real-time detection and classification of distinct skin-deformation signatures, enable the reliable decoding of facial movements. The integrated system could be adapted for use in clinical settings as a nonverbal communication technology or for use in the monitoring of neuromuscular conditions.