Wearable skin sensor to track blood alcohol levels
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 project builds wearable sensors and smart computer algorithms to estimate blood alcohol levels from alcohol in the skin for people who drink alcohol.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Illinois at Urbana-Champaign NIH-funded |
| Lab location | 1 site (Champaign, United States) |
| Project ID | NIH-11103133 on NIH RePORTER |
What this research studies
You would wear a small, new-generation transdermal device that passively measures alcohol in your sweat while researchers collect standard breath or blood alcohol readings at the same time. The team will enroll a large, diverse group of people and capture motion and other sensor data to understand how signals change across everyday activities. They will use machine learning to translate the device's skin-alcohol signals into accurate estimates of blood alcohol concentration, accounting for individual differences and time delays. The goal is to validate the technology and software so it can provide reliable, continuous monitoring outside the lab.
Who could benefit from this research
Good fit: Adults who drink alcohol regularly, including those with heavy drinking or alcohol use disorder who are willing to wear a device and attend testing sessions, would be ideal candidates.
Not a fit: People who do not drink, are unable to wear a transdermal device (for example due to skin allergies), or cannot travel to the study site are unlikely to benefit from participating.
Why it matters
Potential benefit: If successful, this could give people a comfortable, continuous way to monitor drinking and support safer choices, treatment, or relapse prevention.
How similar studies have performed: Earlier small studies with older transdermal devices showed promise but were limited, and applying modern devices plus large-scale machine learning is a newer approach.
Where this research is happening
Champaign, United States
- University of Illinois at Urbana-Champaign — Champaign, United States (Active)
Researchers
- Principal investigator: Fairbairn, Catharine — University of Illinois at Urbana-Champaign
- Study coordinator: Fairbairn, Catharine
About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page below.