# A Deep-Learning Approach for Identifying a Drunk Person Using Gait Recognition
## 3 Results
### Identification  
We trained a CNN model using GEI image data to identify drinkers. The diagram of the CNN architecture used in this study is shown in Figure 5. The data consist of 100 images per subject, divided into 10 images for testing and 90 images for training. For each, the drunk data and sober data were set to have the same number. To overcome the disadvantage of a large training dataset being required for a CNN to learn, the data were multiplied through IA. We used scaling, rotation, and flip in IA. In the case of scaling, the range of image enlargement or reduction was set to 0.5 to 1.0 times. The maximum enlargement ratio was \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Drunk/Soker** & **Stride Time** & **Stride Length** & **Stride Velocity** \\ \hline Average & 1.13 \(\pm\) 0.09 & 0.873 \(\pm\) 0.06 & 0.780 \(\pm\) 0.08 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results of drunk/sober. Figure 4: **(a)** The difference in stride time between sober gait and drunk gait. **(b)** The difference in stride length between sober gait and drunk gait. **(c)** The difference in stride velocity between sober gait and drunk gait. **(d)** The average difference between parameters of sober gait and drunk gait. The difference was statistically significant. (* \(p\) < 0.05) (** \(p\) < 0.1). Figure 5: CNN architecture diagram to recognize the drunk gait pattern from GEI image data. set to 1.0 times so as not to exceed the original, because if a part of the body shown in the GEI image were cut out, accurate identification would become difficult. For rotation, 0-10\({}^{\circ}\) rotation to the right and left, as well as 90\({}^{\circ}\) and 270\({}^{\circ}\) rotation options, were used. For flip, two options were used: up and down and left and right. In this way, the existing 100 images were multiplied 10 times, so the test data increased to 100 images and the training data to 900 images. Therefore, a total of 1000 images were used for identification through the CNN algorithm. We optimized the parameters of the CNN model to improve the accuracy of identification of drunk individuals. First, when running CNN, it takes a significant amount of time to train in validation and perform the test because of the large number of datasets. Therefore, epochs were fixed to 200 in order to reduce the computational cost and the time required to run CNN. Then, we increased the dropout rate from 0.3 to 0.9 in steps of +0.2 and checked the accuracy change. As a result, we found that the accuracy was highest when the dropout rate = 0.3, and decreased as the dropout rate increased; such results corresponded to the well-known characteristic of artificial neural networks. We also checked the identification accuracy according to the kernel size change, but the sensitivity to the kernel size change was negligible. Therefore, according to these results, the default values of the CNN algorithm parameters were set as epochs = 200, dropout rate = 0.3, and kernel size = 3. The CNN algorithm designed in this way was run with the data from each of the 20 subjects. The resulting loss and accuracy indicating the identification performance are shown in Figure 6.  
The identification accuracy for each subject varied from 63.50 to 92.75%. The model showed relatively high identification performance with an average accuracy of 74.93%, and the standard deviation was only 2.81%. Such accuracy was similar to the accuracy with the method using smartphones [9,12]. In the case of loss, the average value was 0.76 and the standard deviation was 0.11. In addition, we conducted the heatmap analysis to evaluate the correlation between accuracy and parameters. Stride velocity showed the highest correlation with accuracy (Figure 7). The results of the training and validation processes are the following. First, we performed an analysis on 20 people and found that the average accuracy of all passengers was about 74%. The graph from the training result shows that the loss and accuracy trends gradually change and converge, as shown below.