# Data Mining and Augmented Reality: An Application to the Fashion Industry
## 4 Results
### Augmented Reality Support  
As anticipated in the proposed framework, to speed up the quality control process and differently support experts and trainees, AR has been used. More in detail, both the user types will see, first, the updated sample size for the control order selected. The number of articles from the order requiring quality control, indeed, is defined according to the strategical redefinition of the sampling rules through the ARM. Secondly, the areas to be controlled are shown on the screen one by one and ordered according to the defect occurrence. All controllers can also access the control set description by clicking on the related icon on the screen (i.e., the list icon in Figures 4 and 5). Figure 4: Expert view per Control area, Control set (grey areas display the overlayed suggestions). Figure 5: Trainee view per control area, control set, and defect (grey areas display the overlayed suggestions). As shown in Figure 5, each defect is followed by two images as examples of compliance and non-compliance, respectively, in order to get the real product to be checked visually closer to the required quality standard through AR. According to the company's requirement, the device used is a commercial tablet with dual cameras, and the data acquisition is made by scanning the barcode with the control lot reference. As it refers to a specific article processed by a supplier, all the information needed to mine the association rules is retrieved from the database, such as the product category and main material. The AR application is based on a web app developed with Xamarin and linked to a LAMP (Linux, Apache, MySQL and PHP) server. Data are acquired from and exported to tables structured as Tables 1 and 2 and stored in a MySQL database. The device has been installed on a holder, and a marker-based approach has been followed to virtually pin the augmented textual information on the right side, leaving the left side free for the object visualization, as shown in Figures 4 and 5. The proposed approach has been carried out following a qualitative and a quantitative approach. From a quantitative perspective, the inspection process has been observed, and its duration has been measured. Specifically, the time employed by expert operators has been measured both by carrying out the traditional process and using the AR device in order to observe whether different mean values could be observed. More in detail, the time required to inspect compliant products is collected separately from the duration to inspect non-compliant products. The same comparison is also carried out for non-expert operators. The observations are compared through a t-test at a 95% confidence level in order to assess the differences in the groups' means. The normalized durations of the quality control procedures carried out without the support of the device are reported in Table 7, while Table 8 details the ones obtained using the AR device. \begin{table}
\begin{tabular}{c c c c} \hline  & \multicolumn{3}{c}{**No Device**} \\ \hline  & **Expert** & \multicolumn{3}{c}{**Non-Expert**} \\ \hline
**Time for** & **Time for** & **Time for** & **Time for** \\
**OK Products** & **KO Products** & **OK Products** & **KO Products** \\ \hline  
\hline \end{tabular}
\end{table} 
Table 7: Normalized duration of the quality control procedure made by expert and non-expert operators without AR devices, differentiating by compliant (OK) and non-compliant (KO) products. The comparison between the performances of expert operators did not present a statistically significant difference in the groups' means: that is, expert operators almost employ the same time in inspecting compliant products both using the device or not (\(p=0.740\)); the same result is obtained in the observation of non-compliant products (\(p=0.084\)). In both cases, the null hypothesis cannot be rejected. Hence, the mean values of the groups compared are almost the same.