  Impairments in gait occur after alcohol consumption, and, if detected in
real-time, could guide the delivery of "just-in-time" injury prevention
interventions. We aimed to identify the salient features of gait that could be
used for estimating blood alcohol content (BAC) level in a typical drinking
environment. We recruited 10 young adults with a history of heavy drinking to
test our research app. During four consecutive Fridays and Saturdays, every
hour from 8pm to 12am, they were prompted to use the app to report alcohol
consumption and complete a 5-step straight-line walking task, during which
3-axis acceleration and angular velocity data was sampled at a frequency of 100
Hz. BAC for each subject was calculated. From sensor signals, 24 features were
calculated using a sliding window technique, including energy, mean, and
standard deviation. Using an artificial neural network (ANN), we performed
regression analysis to define a model determining association between gait
features and BACs. 70\% of data was used as a training dataset, and the results
were tested and validated using the rest of samples. We evaluated different
training algorithms for the neural network and the result showed that a
Bayesian regularization neural network (BRNN) was the most efficient and
accurate. Analyses support the use of the tandem gait task paired with our
approach to reliably estimate BAC based on gait features. Results from this
work could be useful in designing effective prevention interventions to reduce
risky behaviors during periods of alcohol consumption.
