Real-time recursive estimation of, and uncertainty quantification for, breath alcohol concentration via LQ tracking control-based inverse filtering of transdermal alcohol biosensor signals

Abstract

The utility of newly developed wearable biosensors for passively, noninvasively, and continuously measuring transdermal alcohol levels in the body in real time has been limited by the fact that raw transdermal alcohol data does not consistently correlate (quantitatively or temporally) with interpretable metrics of breath and blood across individuals, devices, and the environment. A novel method using a population model in the form of a random abstract hybrid system of ordinary and partial differential equations and linear quadratic tracking control in Hilbert space was developed to estimate blood or breath alcohol concentration from the biosensor-produced transdermal alcohol level signal. Using human subject data in the form of 270 drinking episodes, the method was shown to produce estimates of blood or breath alcohol concentration that are highly correlated and, thus, good predictors of breath analyzer measurements. Moreover, although the method required some advanced offline training on a laptop or on the cloud, it produced the estimated blood or breath alcohol concentration recursively online in real time, and required only computations that could be carried out on either the biosensor's built-in processor or on a portable mobile device such as a phone or tablet.1. Introduction. The two standard methods for monitoring alcohol use in naturalistic settings, the self-report and the breath analyzer, are plagued by a number of disadvantages. The self report is often hindered by a lack of knowledge of alcohol content, quantity consumed, timing, variability in stomach content, metabolism, and body composition. This means that the amount of alcohol consumed does not