All Times EDT
Keywords: Optimization, Nonparametric, Maximum Likelihood, Machine Learning, Alcohol
Alcohol biosensor devices have the prospect to positively impact medicine and law enforcement by giving a noninvasive method to acquire continuous alcohol readings. We developed a nonparametric estimation algorithm that estimates the joint mixing distribution of the parameters of a heat equation model via a maximum likelihood method. This model is assumed to estimate the diffusion of alcohol through transdermal layers while taking into account measurement error. These parameters are assumed to be random due to natural irregularity in an individual’s body conditions and the variability of population data. This is superior to parametric estimation methods since it can capture unusual fluctuations of a subject’s condition as well as environmental factors. This will help to ascertain a precise relation between blood alcohol concentration and transdermal alcohol concentration.