Development of an accelerometer‐based wearable sensor approach for alcohol consumption detection
Overall, the results indicated that consumer-grade smartwatches can be utilized to detect and measure alcohol use behavior using machine learning and distributional algorithms. This work provides the methodological foundation for future research to analyze the behavioral pharmacology of alcohol use and develop accessible just-in-time clinical interventions.
Authors
- Tongzhen Xie (ORCID: https://orcid.org/0000-0002-0968-0079)
- Adriana K. Cushnie (ORCID: https://orcid.org/0000-0002-3026-9997)
- Jeff Boissoneault (ORCID: https://orcid.org/0000-0002-2268-6491)
- Huda Ahmed (ORCID: https://orcid.org/0009-0008-5684-8795)
- Rachel Schorn
- Madison Sinclair
- Nicholas J. Bush (ORCID: https://orcid.org/0000-0001-8502-3006)
Institutions
- University of Minnesota (US)
Publication Details
- Journal
- Alcohol Clinical and Experimental Research
- Published
- 2024-10-14
- DOI
- https://doi.org/10.1111/acer.15465
- Citations
- 2
- Primary Topic
- Sleep and Work-Related Fatigue
- Type
- article
- Field-Weighted Citation Impact
- 1.02
Funders
- National Institute on Alcohol Abuse and Alcoholism