# A Deep-Learning Approach for Identifying a Drunk Person Using Gait Recognition
## 1 Introduction  
According to the World Health Organization (WHO), Korea's alcohol consumption in 2015 was found to be the third largest in East Asia, after Thailand and India [1]. As a result, accidents have occurred in various places, such as drunk driving accidents or accidents caused by alcohol poisoning at industrial sites. In addition, according to the U.S. Department of Labor, 65% of workplace accidents are related to alcohol or drugs [2]. Due to the seriousness of these problems, not only Korea, but the whole world, is paying attention to this situation. Therefore, if it is possible to identify drunk individuals in advance, many alcohol-related accidents may be prevented, thereby minimizing the burden on individuals, industries, and even society due to accidents. The traditional method of identifying alcohol consumption is the measurement of blood alcohol level using a respiratory system or by drawing blood. However, all of these are direct contact methods, so there is a risk of infection, and they require the subject's cooperation. This is especially true in the case of drawing blood, as it is an invasive method that involves pain. To overcome this problem, a non-invasive method of measuring thealcohol level in sweat, which is highly correlated with blood alcohol level, through a sensor attached to the skin has recently been proposed [3; 4]. However, the method using sweat is available in the only conditions of hot and humid air, exercises, or iontophoresis that accompanies skin irritations. On the other hand, gait analysis is more suitable as a method of identification of a drinker because it can be measured in a no-contact, non-invasive manner without the subject's effort or cooperation. Traditionally, commercial devices such as motion capture systems, Force Plate, and GAITRite(r) have been used for gait measurement and analysis [5; 6; 7]. This system has high reliability and accuracy, but the experimental environment is limited and expensive, and there is the disadvantage that it requires much time for analysis and installation. As an alternative to avoid these disadvantages, two main approaches have been proposed: a sensor-based method using wearable sensors and a vision-based method using images [8]. First, the sensor-based method uses data extracted from sensors in a smartphone [9; 10; 11; 12; 13], a smartwatch [14], and a shoe [15]. Since a smartphone has various motion sensors and high portability, there have been efforts to detect drunk gait abnormalities. When accelerometer and gyroscope data in a smartphone were used, higher accuracy was achieved compared to accuracy when accelerometer data was used alone [9; 10; 11; 12]. Yet, the smartphone's location can cause noise and decrease accuracy [16]. A smartwatch can be a better option due to its various sensors, high portability, and lack of variation in wearing location. When accelerometer, heart rate, and temperature data were collected and analyzed, the accuracy was 98.8% [14]. Although such a multi-modal sensing approach using portable devices showed high accuracy in detecting drunk gait, the requirement of the subject's cooperation in wearing the device is a remaining challenge. In addition, a change in gait due to discomfort caused by wearing devices and errors due to differences in wearing positions or contact may occur. Therefore, this will be difficult to apply to a case such as a factory, in which many subjects need to be monitored. The markerless vision-based method, on the other hand, is a method of analyzing a gait after taking a frame from an image captured by a video camera [8]. This method has the advantage of being able to acquire gait information in a no-contact manner from a distance without the cooperation of the recognition target [17; 18; 19; 20; 21; 22; 23; 24; 25]. Thus, even if a body sensor or wearable device is not attached to the target person, we can measure gait in real-time anytime, anywhere, wherever the camera is installed, making it suitable for judging alcohol consumption regardless of location.