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Impairments in gait occur after alcohol consumption, and, if detected inreal-time, could guide the delivery of "just-in-time" injury preventioninterventions. We aimed to identify the salient features of gait that could beused for estimating blood alcohol content (BAC) level in a typical drinkingenvironment. We recruited 10 young adults with a history of heavy drinking totest our research app. During four consecutive Fridays and Saturdays, everyhour from 8pm to 12am, they were prompted to use the app to report alcoholconsumption and complete a 5-step straight-line walking task, during which3-axis acceleration and angular velocity data was sampled at a frequency of 100Hz. BAC for each subject was calculated. From sensor signals, 24 features werecalculated using a sliding window technique, including energy, mean, andstandard deviation. Using an artificial neural network (ANN), we performedregression analysis to define a model determining association between gaitfeatures and BACs. 70\% of data was used as a training dataset, and the resultswere tested and validated using the rest of samples. We evaluated differenttraining algorithms for the neural network and the result showed that aBayesian regularization neural network (BRNN) was the most efficient andaccurate. Analyses support the use of the tandem gait task paired with ourapproach to reliably estimate BAC based on gait features. Results from thiswork could be useful in designing effective prevention interventions to reducerisky behaviors during periods of alcohol consumption.

An Artificial Neural Network for Gait Analysis to Estimate Blood Alcohol Content Level