# 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

> **NIH NIH R01** · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · 2022 · $456,031

## Abstract

A wearable alcohol biosensor could represent a tremendous advance towards helping people make informed
decisions about their drinking and, ultimately, towards curbing alcohol-related morbidity and mortality.
Transdermal sensors, which measure alcohol consumption by assessing the alcohol content of insensible
perspiration, offer a uniquely non-invasive, passive, and low-cost method for the continuous assessment of
drinking likely to be attractive to a range of populations. But the relationship between transdermal alcohol
concentration (TAC) and blood alcohol concentration (BAC) is highly complex, varying across individuals and
contexts and involving some degree of lag time. Prior research, which has featured extremely small participant
samples and examined old-generation transdermal devices, has been poorly suited to modeling this
complexity. Thus, scientists are left with little sense for how to translate data produced by transdermal sensors
into estimates of BAC. Importantly, the past decade has seen remarkable technological and analytic
developments, offering the potential to tackle the challenge of TAC-BAC translation. In particular, in recent
years, machine learning approaches have been developed that are particularly well suited to modeling highly
complex and time-lagged relationships within larger datasets. Also during this time period, a new generation of
transdermal device has come under development, featuring sleek/compact designs, smartphone integration,
and capabilities for sampling TAC at approximately 90 times the rate of older-generation devices. These
sensors thus provide a rich source of data for machine learning models and also, for the first time, the potential
to produce transdermal BAC estimates in real time. The proposed research leverages machine learning, novel
transdermal technology, and large-scale multimodal human testing to translate transdermal sensor data into
estimates of BAC. Transdermal sensors will be examined in the context of multimodal research featuring a
large and diverse participant sample (N=240) examined both inside and outside the laboratory. The
ambulatory arm of the proposed project is aimed at capturing the TAC-BAC relationship across individuals in
varying real-world drinking contexts, examining regular drinkers wearing new-generation transdermal sensors
in everyday settings while providing prompted breathalyzer readings. This ambulatory research will be
complemented by a laboratory study arm, aimed at examining the TAC-BAC relationship among individuals
drinking in a controlled setting while alcohol dose and rate of consumption are systematically manipulated.
Machine learning algorithms, including deep neural network models, will be used to create estimates of BAC
from transdermal sensor data. These estimates will be examined in terms of their accuracy, temporal
specificity, and also context-dependence. Thus, results will carry significance for addiction science by
translating transdermal sensor data...

## Key facts

- **NIH application ID:** 10492011
- **Project number:** 5R01AA028488-02
- **Recipient organization:** UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN
- **Principal Investigator:** Catharine Fairbairn
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $456,031
- **Award type:** 5
- **Project period:** 2021-09-21 → 2026-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10492011

## Citation

> US National Institutes of Health, RePORTER application 10492011, 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 (5R01AA028488-02). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10492011. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*