## 6 Discussion  
Because the system determines whether an authentication process starts based on measured proximity levels, the performance improvement of proximity detection increases \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model Type** & \(T_{TP}\) & \(T_{TC}\) & \(T_{Scan}\) & \(T_{Pre}\) & \(T_{Cla}\) & \(T_{Ens}\) \\ \hline Raw\_MLP & 0 ms & 16,511 ms & 4000 ms & 0 ms & 38 ms & 0 ms \\ \hline Ensemble\_MLP & 21,337 ms & 16,925 ms & 4000 ms & 83 ms & 40 ms & 0.29 ms \\ \hline Raw\_1D-CNN & 0 ms & 18,507 ms & 4000 ms & 0 ms & 40 ms & 0 ms \\ \hline Ensemble\_1D-CNN & 21,337 ms & 18,832 ms & 4000 ms & 83 ms & 40 ms & 0.29 ms \\ \hline Raw\_SVM & 0 ms & 356 ms & 4000 ms & 0 ms & 0.2 ms & 0 ms \\ \hline Ensemble\_SVM & 21,337 ns & 427 ms & 4000 ms & 83 ms & 1.3 ms & 0.06 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Execution time for proximity detection when the environment is a steel door. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model Type** & \(T_{TP}\) & \(T_{TC}\) & \(T_{Scan}\) & \(T_{Pre}\) & \(T_{Cla}\) & \(T_{Ens}\) \\ \hline Raw\_MLP & 0 ms & 16,497 ms & 4000 ms & 0 ms & 38 ms & 0 ms \\ \hline Ensemble\_MLP & 20,891 ms & 16,898 ms & 4000 ms & 82 ms & 40 ms & 0.29 ms \\ \hline Raw\_1D-CNN & 0 ms & 18,425 ms & 4000 ms & 0 ms & 39 ms & 0 ms \\ \hline Ensemble\_1D-CNN & 20,891 ms & 18,749 ms & 4000 ms & 82 ms & 39 ms & 0.29 ms \\ \hline Raw\_SVM & 0 ms & 657 ms & 4000 ms & 0 ms & 0.21 ms & 0 ms \\ \hline Ensemble\_SVM & 20,891 ms & 751 ms & 4000 ms & 82 ms & 1.2 ms & 0.06 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Execution time for proximity detection when the environment is a wood door. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model Type** & \(T_{TP}\) & \(T_{TC}\) & \(T_{Scan}\) & \(T_{Pre}\) & \(T_{Cla}\) & \(T_{Ens}\) \\ \hline Raw\_MLP & 0 ms & 16,579 ms & 4000 ms & 0 ms & 38 ms & 0 ms \\ \hline Ensemble\_MLP & 21,357 ms & 17,026 ms & 4000 ms & 83 ms & 40 ms & 0.29 ms \\ \hline Raw\_1D-CNN & 0 ms & 18,568 ms & 4000 ms & 0 ms & 39 ms & 0 ms \\ \hline Ensemble\_1D-CNN & 21,357 ms & 18,929 ms & 4000 ms & 83 ms & 40 ms & 0.29 ms \\ \hline Raw\_SVM & 0 ms & 459 ms & 4000 ms & 0 ms & 0.19 ms & 0 ms \\ \hline Ensemble\_SVM & 21,357 ms & 522 ms & 4000 ms & 83 ms & 1 ms & 0.06 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Execution time for proximity detection when the environment is a glass door. the system's stability. Results of proximity detection experiments show that using an ensemble and various pre-processing results in reduced error rates compared to only using raw data. We find that MLP and 1D-CNN are better than SVM in the system with pre-processing and ensemble. Additionally, execution times for using raw data and using the ensemble are shown. So, a trade-off between accuracy and time is found. Although the stability of the system is enhanced by increasing the accuracy of the proximity detection, potential challenges remain. Proximity detection may decrease the accuracy when the user moves while the mobile device scans the BLE signal. Because human movement affects the RSSI value, the proximity level measured using RSSI may be measured incorrectly. To reduce this negative effect on the user's movement, the system recommends that the authentication process should be carried out after the user stops at the location where the user promised to meet the matching person. Also, proximity detection can be used to determine that the user is within a specific distance. For example, let us assume the system requires that a matched person be within 2.0 m of the user. Then, 1.0 m is the same as 2.0 m and 2.5 m is the same as 3.0 m. This can make the system become error-tolerant. The system can be used for a traditional mobile-mobile interaction system that supports a user-matching system. In the case of the delivery service, a delivery person matches with a buyer. When the delivery person arrives at the buyer's house, the delivery person and the buyer use the system for mutual recognition in a contactless situation.