Detection of Alcohol Inebriation from Eye Movements using Remote and Wearable Eye Trackers

Detecting alcohol inebriation using tracking of eye movements has potential applications in enhancing operational safety and enabling health-monitoring solutions. For wide practical adoption, eye movements need to be tracked without actively inconveniencing the user. We therefore study whether alcohol consumption can be detected using wearable and remote eye tracking. We collect eye-movement data of participants in sober and moderately inebriated states. We develop a machine learning approach that uses features derived from saccadic and fixational eye movements, pupil diameter, and eye closure. We find that while alcohol can be detected with moderate accuracy (ROCAUC > 0.65) in a closed-population setting, the attainable detection accuracy is just above random guessing (ROCAUC = 0.569) for unknown users. The attainable accuracy is higher for blood alcohol concentrations of 0.04%. Our findings highlight that strong individual differences in the response to alcohol render generalization across individuals challenging. Our code and data are available online; participants have given their informed consent and the study has been approved by the ethics committee of our university.