Emulating facial biomechanics using multivariate partial least squares surrogate models

Abstract

SUMMARYA detailed biomechanical model of the human face driven by a network of muscles is a useful tool in relating the muscle activities to facial deformations. However, lengthy computational times often hinder its applications in practical settings. The objective of this study is to replace precise but computationally demanding biomechanical model by a much faster multivariate meta-model (surrogate model), such that a significant speedup (to real-time interactive speed) can be achieved. Using a multilevel fractional factorial design, the parameter space of the biomechanical system was probed from a set of sample points chosen to satisfy maximal rank optimality and volume filling. The input-output relationship at these sampled points was then statistically emulated using linear and nonlinear, cross-validated, partial least squares regression models. It was demonstrated that these surrogate models can mimic facial biomechanics efficiently and reliably in real-time.

Effects of fetal head shape variation on the second stage of labour

“…The covariance of T and U is maximised and linked by the regression function f(T), while the residual H for the inner mapping is minimised. A detailed description on PLSR can be found elsewhere (Bennett and Embrechts, 2003;Geladi and Kowalski, 1986;Wu et al, 2014). In this study, the fetal heads were assumed to have negligible deformations in the childbirth simulations, thus only the FE descriptions of the fetal skull outer surfaces that came into direct contact with the pelvic floor were included as input X.…”

Bioinspired Technologies to Connect Musculoskeletal Mechanobiology to the Person for Training and Rehabilitation

“…Generally, FEM models are computationally demanding and not solvable in real-time. Thus, FEM models must be reduced to surrogates by a process known as “Kriging” (Matheron, 1963 ), whereby the continuum model is first solved offline for all possible, or physiologic, configurations (Wu et al, 2014 ; Eskinazi and Fregly, 2016 ), and simulation results are then be stored for future real-time use. However, it is computationally expensive to establish robust surrogates of musculoskeletal tissue continuum models, given the large data throughput imposed by performing many multi-scale simulations (Erdemir et al, 2015 ).…”

Analyzing complex mathematical model behavior by partial least squares regression‐based multivariate metamodeling

“…Faster computation of a complex model . (a) Slow mathematical model Outputs = M ( Inputs ): A finite element model of facial expressions, representing biomechanical simulations of facial expressions (from left to right) joy, sadness, snarl and the kissing gesture, as controlled by 18 input parameters.…”

“…Average computation time: 2 h. (b) Fast classical metamodel Outputs ≈ C ( Inputs ) based on simulations according to the design in Figure . Average computation time for different versions of PLSR: <0.1 s. ( Reprinted with permission from Ref . Copyright 2014 Wiley)…”