Abstract
Recently, Bayesian estimation coupled with finite element modeling has been demonstrated as a viable tool for estimating vocal fold material properties from kinematic information obtained via high-speed video recordings. In this article, the sensitivity of the parameter estimations to the employed fluid model is explored by considering Bernoulli and one-dimensional viscous fluid flow models. Simulation results indicate that prescribing an ad hoc separation location for the Bernoulli flow model can lead to large estimate biases, whereas including the separation location as an estimated parameter leads to results comparable to that of the viscous fluid flow model.
Highlights
The past decade has seen significant advances in the development of subject-specific vocal fold (VF) models.1–8 The development of subject-specific lumped element models was originally demonstrated by Dollinger et al.1 using the Nelder-Mead method to determine model parameters that best reproduced chosen Fourier coefficients obtained from measured VF trajectory waveforms
We explore the influence of fluid flow model selection on the accuracy and uncertainty of twodimensional finite element model parameter estimates based upon observations of silicone VF model kinematics for a small range of subglottal pressures
The glottal area waveforms extracted from the high speed video recordings of the silicone VFs at each experimental subglottal pressure were used as the data for purposes of fitting the model
Summary
The past decade has seen significant advances in the development of subject-specific vocal fold (VF) models. The development of subject-specific lumped element models was originally demonstrated by Dollinger et al. using the Nelder-Mead method to determine model parameters that best reproduced chosen Fourier coefficients obtained from measured VF trajectory waveforms. Bayesian estimation has been employed to estimate time-varying VF model parameters from synthetic VF kinematic data, subglottal pressure and muscle activation parameters from clinical data, and recently finite element material properties from high-speed video observations of vibrating silicone VF models.. Bayesian estimation has been employed to estimate time-varying VF model parameters from synthetic VF kinematic data, subglottal pressure and muscle activation parameters from clinical data, and recently finite element material properties from high-speed video observations of vibrating silicone VF models.7 The success of such an estimation procedure is predicated on a variety of factors, including the quality of the observation data, the ability of the selected model(s) to capture the relevant physics, and the degrees of freedom of the estimation (i.e., the number of parameters to be estimated)..
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