Title: Using Phone Sensors and an Artificial Neural Network to Detect Gait Changes During Drinking Episodes in the Natural Environment,
Abstract: Phone sensors could be useful in assessing changes in gait that occur with
alcohol consumption. This study determined (1) feasibility of collecting
gait-related data during drinking occasions in the natural environment, and (2)
how gait-related features measured by phone sensors relate to estimated blood
alcohol concentration (eBAC). Ten young adult heavy drinkers were prompted to
complete a 5-step gait task every hour from 8pm to 12am over four consecutive
weekends. We collected 3-xis accelerometer, gyroscope, and magnetometer data
from phone sensors, and computed 24 gait-related features using a sliding
window technique. eBAC levels were calculated at each time point based on
Ecological Momentary Assessment (EMA) of alcohol use. We used an artificial
neural network model to analyze associations between sensor features and eBACs
in 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 sensor
data 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 times
when the eBAC was greater than the legal limit (0.08 mg/dl). Using a Bayesian
regularized neural network, gait-related phone sensor features showed a high
correlation with eBAC (Pearson's r > 0.9), and >95% of estimated eBAC would
fall between -0.012 and +0.012 of actual eBAC. It is feasible to collect
gait-related data from smartphone sensors during drinking occasions in the
natural environment. Sensor-based features can be used to infer gait changes
associated with elevated blood alcohol content.