Main Presenter:
Canan Dagdeviren, PhD
Assistant Professor of Media Arts and Sciences
LG Career Development Professor of Media Arts and Sciences

Title:
Conformable Facial Code Extrapolation Sensor (cFaCES)    

Description:
Devices that facilitate nonverbal communication typically require high computational loads or have rigid and bulky form factors 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 the decoding of facial strains and for predicting facial kinematics. The system consists of mass-manufacturable, conformable piezoelectric thin films for strain mapping, multiphysics modelling for the analysis of the nonlinear mechanical interactions between the conformable device and the epidermis, and three-dimensional digital image correlation for the reconstruction of soft-tissue surfaces under dynamic deformations and for informing device design and placement. In healthy subjects and in subjects 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.