Deconvolving the input to random abstract parabolic systems: a population model-based approach to estimating blood/breath alcohol concentration from transdermal alcohol biosensor data

DOI10.1088/1361-6420/aae791zbMath1462.65135arXiv1807.05088OpenAlexW2884203359WikidataQ92357339 ScholiaQ92357339MaRDI QIDQ4646410

Susan E. Luczak, Catharine E. Fairbairn, Dahyeon Kang, Konrad Bresin, Melike Sirlanci, I. G. Rosen

Publication date: 14 January 2019

Published in: Inverse Problems (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1807.05088

zbMATH Keywords

system identificationdistributed parameter systemsdeconvolutionpopulation modeltransdermal alcohol biosensorrandom abstract parabolic systemslinear semigroups of operators

Mathematics Subject Classification ID

System identification (93B30) Biochemistry, molecular biology (92C40) PDEs with randomness, stochastic partial differential equations (35R60) Numerical methods for inverse problems for initial value and initial-boundary value problems involving PDEs (65M32)

Related Items (7)

Blood and breath alcohol concentration from transdermal alcohol biosensor data: estimation and uncertainty quantification via forward and inverse filtering for a covariate-dependent, physics-informed, hidden Markov model* ⋮ A population model‐based linear‐quadratic Gaussian compensator for the control of intravenously infused alcohol studies and withdrawal symptom prophylaxis using transdermal sensing ⋮ Estimation of parameter distributions for reaction-diffusion equations with competition using aggregate spatiotemporal data ⋮ Estimating the distribution of random parameters in a diffusion equation forward model for a transdermal alcohol biosensor ⋮ Nonparametric estimation of blood alcohol concentration from transdermal alcohol measurements using alcohol biosensor devices ⋮ Deconvolving breath alcohol concentration from biosensor measured transdermal alcohol level under uncertainty: a Bayesian approach ⋮ Tracking and blind deconvolution of blood alcohol concentration from transdermal alcohol biosensor data: a population model-based LQG approach in Hilbert space

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