Towards a Wearable Alcohol Biosensor: Examining the Accuracy of BAC Estimates from New-Generation Transdermal Technology using Large-Scale Human Testing and Machine Learning Algorithms

This NIH-funded project focuses on machine learning approaches for translating transdermal alcohol content (i.e., alcohol measured from a person’s skin) into blood alcohol content (“BAC”). Modern transdermal sensors are small, easy to use, and measure transdermal alcohol content frequently, but lag behind typical measures of BAC (especially breathalyzers) in terms of accuracy. This project addresses the accuracy problem by creating machine learning methods that can predict the current BAC more accurately than the individual transdermal readings do. The project trains and applies these models in both laboratory and in-the-wild contexts with the goal of providing accurate and reliable inebriation measurements that can be used as part of treatment programs for people with alcohol use disorder.