Objective: Sensing the effects of alcohol consumption in real time could offer numerous opportunities to reduce related harms. This study sought to explore accuracy of gait-related features measured by smartphone accelerometer sensors on detecting alcohol intoxication (breath alcohol concentration [BrAC] > .08%).
Method: In a controlled laboratory study, participants (N = 17; 12 male) were asked to walk 10 steps in a straight line, turn, and walk 10 steps back before drinking and each hour, for up to 7 hours after drinking a weight-based dose of alcohol to reach a BrAC of .20%. Smartphones were placed on the lumbar region and 3-axis accelerometer data was recorded at a rate of 100 Hz. Accelerometer data were segmented into task segments (i.e., walk forward, walk backward). Features were generated for each overlapping 1-second windows, and the data set was split into training and testing data sets. Logistic regression models were used to estimate accuracy for classifying BrAC ≤ .08% from BrAC > .08% for each subject.
Results: Across participants, BrAC > .08% was predicted with a mean accuracy of 92.5% using logistic regression, an improvement from a naive model accuracy of 88.2% (mean sensitivity = .89; specificity = .92; positive predictive value = .77; and negative predictive value = .97). The two most informative accelerometer features were mean signal amplitude and variance of the signal in the x-axis (i.e., gait sway).
Conclusions: We found preliminary evidence supporting use of gait-related features measured by smartphone accelerometer sensors to detect alcohol intoxication. Future research should determine whether these findings replicate in situ.