  Driving under the influence of alcohol is a widespread phenomenon in the US
where it is considered a major cause of fatal accidents. In this research we
present a novel approach and concept for detecting intoxication from motion
differences obtained by the sensors of wearable devices. We formalize the
problem of drunkenness detection as a supervised machine learning task, both as
a binary classification problem (drunk or sober) and a regression problem (the
breath alcohol content level).
  In order to test our approach, we collected data from 30 different subjects
(patrons at three bars) using Google Glass and the LG G-watch, Microsoft Band,
and Samsung Galaxy S4. We validated our results against an admissible
breathalyzer used by the police.
  A system based on this concept, successfully detected intoxication and
achieved the following results: 0.95 AUC and 0.05 FPR, given a fixed TPR of
1.0. Applications based on our system can be used to analyze the free gait of
drinkers when they walk from the car to the bar and vice-versa, in order to
alert people, or even a connected car and prevent people from driving under the
influence of alcohol.
