The utility of newly developed wearable biosensors for passively, non-invasively, 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 is 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 is 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 requires some advanced offline training on a laptop or on the cloud, it produces the estimated blood or breath alcohol concentration recursively online in real time and requires 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.
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