Drunk User Interfaces: Determining Blood Alcohol Level through Everyday Smartphone Tasks

Alex Mariakakis, Sayna Parsi, Shwetak Patel, Jacob O. Wobbrock

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The Drunk User Interfaces app (DUI) estimates a person's blood alcohol level by assessing the extent to which inebriation affects their motor coordination and cognition.

Abstract

Breathalyzers, the standard quantitative method for assessing inebriation, are primarily owned by law enforcement and used only after a potentially inebriated individual is caught driving. However, not everyone has access to such specialized hardware. We present drunk user interfaces: smartphone interfaces that measure the extent to which alcohol affects a person's motor coordination and cognition using performance metrics and sensor data. We examine five drunk user interfaces and combine them to form the DUI app. DUI uses machine learning models trained on performance metrics and sensor data to estimate a person's blood alcohol level (BAL). We evaluated DUI on 14 individuals in a week-long longitudinal study wherein each participant used DUI at various BALs. We found that with a global model that accounts for user-specific learning, DUI can estimate a person’s BAL with an absolute mean error of 0.005% ± 0.007% and a Pearson’s correlation of 0.96 with breathalyzer measurements.