## 5 Experimental Results and Discussions
### Accuracy Comparison
PCA(k2) represents the case when the number of principal component is 2, while PCA(k4) represents the case with 4 principal components. Figure 6: Comparison of confusion matrices in each experiment scenario. In the case of the Corner scenario, 0 m indicates the case where there is no person in the corridor. PCA(k2) represent the case where the number of principal components is 2, while PCA(k4) represents the case with 4 principal components. In the confusion matrices in Figure 6, we observe that while existing schemes result in very low classification accuracy in some environments and for some classes (i.e., the number of people), the proposed method achieves high classification accuracy across all environments and classes, with very small differences in classification performance between classes. For example, in the case of the _Median_ method, the probability of correctly identifying two people when there are actually two people in the TTW environment is 0.74, and the probability of correctly identifying zero people when there are actually zero people in the Queuing environment is 0.32. In contrast, when the proposed method is applied, the probability of correctly predicting two people when there are actually two people in the TTW environment is increased to 0.91, and the probability of correctly identifying zero people when there are actually zero people in the Queuing environment is greatly improved to 0.99. In Figure 5, we show the box plots for the overall IOD accuracy obtained by each method. We observe that except in the TTW scenario, our method outperforms the _PCA_ methods in terms of the IOD sensing accuracy. In Figure 6, we also compare the _PCA_ method to our method in terms of the confusion matrix. Let us denote \(c_{T}\) as the true class (i.e., the number of people). When we inspect the TTW scenario, we observe that even though the overall accuracy obtained by _PCA_ method with two principle components (\(k=2\)) is higher than the accuracy acquired by our method, our method outperforms the _PCA_ method with \(k=2\) for all \(c_{T}\)s except \(c_{T}=4\). When compared to the _PCA_ method with four principle components (\(k=4\)), our method achieves higher accuracy in all the scenarios and all the \(c_{T}\)s.  
We also observe that the smaller the number of principal components used becomes, the more that information that might be useful for classification is removed from the data, which makes it difficult to distinguish between classes. For example, in the Queuing scenario, the accuracy of _PCA_ with \(k=2\) is 0.68 when \(c_{T}=2\), while the accuracy is 0.90 when _PCA_ with \(k=4\) is used. Conversely, as the number of principal components increases, the amount of deleted information decreases, but the possibility of including unnecessary information increases, which also makes it difficult to distinguish between classes. For example, in the Queuing scenario, _PCA_ with \(k=4\) produces an accuracy of 0.86 when \(c_{T}=3\), while it becomes 0.93 when _PCA_ with \(k=2\) is used. Therefore, it is difficult to find the optimal number of principal components for each scenario and for each class within each scenario. However, in both cases, our method achieves the highest accuracy. When \(c_{T}=2\) in the Queuing scenario, the accuracy obtained by our method is 0.93, and it is 0.95 when \(c_{T}=4\).