# A Deep-Learning Approach for Identifying a Drunk Person Using Gait Recognition
## 5 Conclusions  
Recently, various problems caused by drinking, such as drunk driving accidents and employees working on industrial sites while drunk, are occurring, and the adverse effects of these problems on society are also gradually increasing. There is a need for a technology that can identify drunk individuals and prevent them, as necessary, from sitting in the driver's seat of a vehicle or entering an industrial site. It is difficult to implement this technique with only the traditional breathalyzer method. Therefore, we designed an algorithm to identify drunk individuals by analyzing walking videos and optimized the CNN model to improve its accuracy. In this study, GEIs were used to identify drunk gait by means of gait image data. The quality of the GEIs varied according to the type of background subtraction method and various experimental conditions. Therefore, we used the MOG2 algorithm, which showed the best background subtraction performance, and set specific experimental conditions to obtain the optimal GEI through repeated experiments. In order to minimize the uncertainty caused by factors such as noise that were expected to occur due to the characteristics of the image data, the illuminance condition was set to 3000 lux, a high-resolution camera was used, and the distance between the camera and the subject was kept constant. It was also found that the identification performance varied according to the structure of the CNN algorithm and the quantity of data. In particular, because CNN requires a large dataset, a problem occurred because this experiment was conducted with 20 subjects. Thus, the data were augmented using the IA technique. In addition, the accuracy was observed by changing variables such as the dropout rate, kernel size, and epoch. As a result, the best identification performance was achieved with an average accuracy of 74.94% under the conditions of epoch = 32, dropout rate = 0.3, and IA (\(\times\)10). This study is meaningful in that it determined the conditions of the algorithm with optimal performance for identifying drunk individuals. In addition, through analysis between parameters such as gait length, time, and speed, it was discovered that there are various variables in gait data that can identify drunk individuals. Although this study was limited to identifying drunk individuals using gait image analysis, this technology is expected to be used in other situations where abnormal gait appears in addition to drinking. It can also be applied in various fields, such as the diagnosis of diseases in which abnormal gait is the main symptom, including Parkinson's disease, cancer, and idiopathic normal pressure hydrocephalus. It may also be applied to the detection of abnormal gait due to headaches and dizziness caused by prolonged exposure to harmful substances in environments such as factories or laboratories. Conceptualization, Y.J.H. and J.U. ; methodology, S.P., B.B., K.K. and H.K. ; formal analysis, S.P., M.S.N., B.B., K.K. and H.K. ; writing--original draft preparation, S.P., B.B., K.K., H.K., M.S.N. and Y.J.H. ; writing--review and editing, J.U. and Y.J.H. ; supervision, J.U. and Y.J.H. ; project administration, J.U. and Y.J.H. ; funding acquisition, J.U. and Y.J.H. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Research Foundation of Korea, grant number NRF-2021R1A2C1009326. This work was partly supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant, funded by the Korean government (MSIT), No. RS-2022-00155911, Artificial Intelligence Convergence Innovation Human Resource Development (Kyung Hee University) and Korea Evaluation Institute of Industrial Technology (KEIT) grant funded by the Korea government (MOTIE). The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board of Kyung Hee University (IRB approval number: KHGIRB-21-287; IRB approval date: 16 July 2021).