This study introduces the in vivo application of a Bayesian framework to estimate subglottal pressure, laryngeal muscle activation, and vocal fold contact pressure from calibrated transnasal high-speed videoendoscopy and oral airflow data. A subject-specific, lumped-element vocal fold model is estimated using an extended Kalman filter and two observation models involving glottal area and glottal airflow. Model-based inferences using data from a vocally healthy male individual are compared with empirical estimates of subglottal pressure and reference values for muscle activation and contact pressure in the literature, thus providing baseline error metrics for future clinical investigations.

Bayesian estimation has been previously demonstrated as a viable method for developing subject-specific vocal fold models from observations of the glottal area waveform. These prior efforts, however, have been restricted to lumped-element fitting models and synthetic observation data. The indirect relationship between the lumped-element parameters and physical tissue properties renders extracting the latter from the former difficult. Herein we propose a finite element fitting model, which treats the vocal folds as a viscoelastic deformable body comprised of three layers. Using the glottal area waveforms generated by self-oscillating silicone vocal folds we directly estimate the elastic moduli, density, and other material properties of the silicone folds using a Bayesian importance sampling approach. Estimated material properties agree with the “ground truth” experimental values to within $3\%$ for most parameters. By considering cases with varying subglottal pressure and medial compression we demonstrate that the finite element model coupled with Bayesian estimation is sufficiently sensitive to distinguish between experimental configurations. Additional information not available experimentally, namely, contact pressures, are extracted from the developed finite element models. The contact pressures are found to increase with medial compression and subglottal pressure, in agreement with expectation.

Time-resolved laser-induced incandescence (TiRe-LII) data can be used to infer spatially and temporally resolved volume fractions and primary particle size distributions of soot-laden aerosols, but these estimates are corrupted by measurement noise as well as uncertainties in the spectroscopic and heat transfer submodels used to interpret the data. Estimates of the temperature, concentration, and size distribution of soot primary particles within a sample aerosol are typically made by nonlinear regression of modeled spectral incandescence decay, or effective temperature decay, to experimental data. In this work, we employ nonstationary Bayesian estimation techniques to infer aerosol properties from simulated and experimental LII signals, specifically the extended Kalman filter and Schmidt–Kalman filter. These techniques exploit the time-varying nature of both the measurements and the models, and they reveal how uncertainty in the estimates computed from TiRe-LII data evolves over time. Both techniques perform better when compared with standard deterministic estimates; however, we demonstrate that the Schmidt–Kalman filter produces more realistic uncertainty estimates.

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.