## 3 Experiment
### Experimental Results  
Based on the RSSI signal data, the proposed technique is applied to represent the BB, and the final BB is obtained through the process of merging and expanding the BB. The result is then divided into cells, as shown in (a) of Figure 3. The initial cell is sized to encompass all BBs placed within the floor, and the distribution of BBs across the cells demonstrates that the BBs and cells form the foundation for generating movement paths. This is (b) in Figure 3. As a result, we can observe that the BBs positioned within the entire cell represent the gathered dataset as BBs, and they are organized and divided in a manner consistent with the actual indoor structure of the floor. When the placement of these BBs is plotted against the arrangement of the original data points, we can observe that they are correctly distributed within the cell region. This proves the appropriateness of the cell division, as the clustering of the replaced BBs is correctly represented within the cells. Therefore, our cell division technique is the basis for computing the movement path. In Figure 4a shows that the division of the cells made sense when merged with the original data points. Figure 3: BB and cell division. (**a**) The result of merging and expanding the BB. (**b**) The result of merging the BB from (**a**) with the structure of the cells divided by the cell division algorithm. Figure 2: Distributions of initial data points. Based on the BB's location within the cell calculated in (b) of Figure 3 and the neighboring cells where the BB exists, the cell adjacency calculation algorithm determines the adjacency. The movement path was then computed based on cells whose adjacency was confirmed through the path generation algorithm. The result is depicted in (b) of Figure 4, and we confirmed that the calculated paths match the actual paths taken in the real building. Training was carried out by utilizing the computed movement path dataset and the specified hyperparameters. The results are shown in Table 6, which are the experimental results of the proposed method based on the 2019 dataset [32]. The experimental results express the accuracy of the computed paths. The training data and test data were trained with the LSTM model, and the accuracy of the training results is shown as the mean, deviation, maximum, and minimum values. We can see that the difference between the maximum and minimum accuracy is 0.5% for the test data, and the deviation is low, so the accuracy remains constant. An experiment was conducted to determine the optimal path length for the proposed technology. When experiments were conducted on various paths using hyperparameter search, the performance of the top five results was selected and evaluated. Table 7 displays the results of an experiment comparing the performance when applying different numbers of paths (accumulated past data). The number of paths used in the experiment was confirmed to be 1 case without a path, 3, 5, and 7 using paths. In experiments using 1 to 5 paths, the accuracy of location judgment gradually improved, while with 7 paths, the accuracy decreased. This confirmed that the optimal number of paths for location judgment is 5. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Average** & **Deviation** & **Max** & **Min** \\ \hline LSTM (path = 5) & 93.968 & 0.327 & 94.48 & 93.64 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Experimental results (unit: Accuracy %). Figure 4: Cell division and movement path computation: (**a**) Distribution the initial data with the cell structure divided by the cell algorithm. (**b**) Paths generated by the movement path generation algorithm. We conducted an experiment to determine the optimal path length for the proposed technology. When we performed experiments on various paths using hyperparameter search, we selected and evaluated the performance of the top five results. Table 7 shows the results of an experiment comparing the performance when applying different numbers of paths (accumulated past data).