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Journal Article


Park S, Bae B, Kang K, Kim H, Nam MS, Um J, Heo YJ. Appl. Sci. (Basel) 2023; 13(3): e1390.


(Copyright © 2023, MDPI: Multidisciplinary Digital Publishing Institute)






Various accidents caused by alcohol consumption have recently increased in prevalence and have become a huge social problem. There have been efforts to identify drunk individuals using mobile devices; however, it is difficult to apply this method to a large number of people. A promising approach that does not involve wearing any sensors or subject cooperation is a markerless, vision-based method that only requires a camera to classify a drunk gait. Herein, we first propose a markerless, vision-based method to determine whether a human is drunk or not based on his or her gait pattern. We employed a convolutional neural network to analyze gait patterns with image augmentation depending on gait energy images. Gait images captured through a camera allow a complex neural network to detect the human body shape accurately. It is necessary for removing the background behind human shape in the gait image because it disrupts the detection algorithm. A process of conversion into gait energy images and augmenting image data is then applied to the dataset of the gait images. A total of 20 participants participated in the experiment. They were required to walk along a line both with and without wearing the Drunk Busters Goggles, which were intended to collect sober and drunk gait images. Validation accuracy for the recognition of a drunk state in 20 persons was approximately 74.94% under optimal conditions. If the present approach fulfills its promise, we can prevent safety accidents due to alcohol, thus decreasing its burden on industries and society.

Language: en


convolutional neural network; drunk identification; gait energy image; image augmentation


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