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Phone sensors could be useful in assessing changes in gait that occur withalcohol consumption. This study determined (1) feasibility of collectinggait-related data during drinking occasions in the natural environment, and (2)how gait-related features measured by phone sensors relate to estimated bloodalcohol concentration (eBAC). Ten young adult heavy drinkers were prompted tocomplete a 5-step gait task every hour from 8pm to 12am over four consecutiveweekends. We collected 3-xis accelerometer, gyroscope, and magnetometer datafrom phone sensors, and computed 24 gait-related features using a slidingwindow technique. eBAC levels were calculated at each time point based onEcological Momentary Assessment (EMA) of alcohol use. We used an artificialneural network model to analyze associations between sensor features and eBACsin training (70% of the data) and validation and test (30% of the data)datasets. We analyzed 128 data points where both eBAC and gait-related sensordata was captured, either when not drinking (n=60), while eBAC was ascending(n=55) or eBAC was descending (n=13). 21 data points were captured at timeswhen the eBAC was greater than the legal limit (0.08 mg/dl). Using a Bayesianregularized neural network, gait-related phone sensor features showed a highcorrelation with eBAC (Pearson's r > 0.9), and >95% of estimated eBAC wouldfall between -0.012 and +0.012 of actual eBAC. It is feasible to collectgait-related data from smartphone sensors during drinking occasions in thenatural environment. Sensor-based features can be used to infer gait changesassociated with elevated blood alcohol content.

Using Phone Sensors and an Artificial Neural Network to Detect Gait Changes During Drinking Episodes in the Natural Environment