# Passive Indoor People Counting by Bluetooth Signal Deformation Analysis with Deep Learning
## 5 Experimental Results
The three models were also tested in the regression setup on the Fine-Tuning Test dataset, with fine-tuning and with five increasing per-class cardinalities (100, 200, 350, 500, 700 elements, respectively). In other words, the Test dataset was subsampled in such a way as to obtain five different configurations. In Configuration 1, each class contained 100 elements; in Configuration 2, each class contained 200 elements; etc. As shown in Table 4, increasing the per-class cardinality beyond 350 did not produce significant performance improvements, and according to the ACPR with \(Th=0.5\) and MAE, the best performer is again the hybrid CNN+LSTM model. Figure 6(a),b show the ACPR confusion matrix (\(Th=0.5\)) for the hybrid CNN+LSTM model, not fine-tuned (a) and fine-tuned (b) with 350 samples per class, evidencing that the performance of the model is nearly uniform across all classes. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**Regression Models Without Fine-Tuning**} \\ \hline
**Model** & **ACPR, \(Th=0.5\)** & **ACPR, \(Th=1\)** & **MAE** \\ \hline DenseNN & 39.74\% & 55.70\% & 0.84 \\ CNN & 73.90\% & 81.21\% & 0.32 \\ LSTM & 79.83\% & 85.26\% & 0.27 \\ CNN+LSTM & **81.25\%** & **85.65\%** & **0.26** \\ Transformer & 47.92\% & 61.16\% & 0.66 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance of the regression models applied to the Pre-Test dataset (without fine-tuning). Bold text indicates the best accuracy values. Figure 7: (a) ACPR confusion matrix (\(Th=0.5\)) for the regression model CNN+LSTM, not fine-tuned. (b) ACPR confusion matrix (\(Th=0.5\)) for the regression model CNN+LSTM, fine-tuned with 350 samples per class. Probably the most interesting feature of regression models is the possibility of predicting, at least in theory, values that were never seen in the fine-tuning step, that is, to interpolate or extrapolate the predicted values. We also tested this characteristic by removing from the Fine-Tuning Training dataset all the samples belonging to classes 0, 2, 3, 4, 6, 7, 8, 10, thus fine-tuning the models only on samples belonging to classes 1, 5, 9. In this case, we compared only the best performers from the previous test, that is, the CNN, LSTM, and CNN+LSTM models. Table 5 reports the ACPR and MAE performance of the models, with the hybrid CNN+LSTM model still clearly winning over the other two. We consider the results shown above as an important improvement over the state of the art because, to the best of our knowledge, the literature does not report on similar partial fine-tuning. Moreover, the possibility to fine-tune the model on a small subset of the \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Regression Models with Fine-Tuning**} \\ \hline
**Model** & **Samples per Class** & **ACPR \(Th=0.5\)** & **ACPR \(Th=1\)** & **MAE** \\ \hline DenseNN & 100 & 40.84\% & 64.85\% & 0.82 \\  & 200 & 41.04\% & 69.10\% & 0.77 \\  & 350 & 41.17\% & 69.90\% & 0.76 \\  & 500 & 42.03\% & 70.01\% & 0.75 \\  & 700 & 42.88\% & 71.01\% & 0.73 \\ \hline CNN & 100 & 64.24\% & 84.85\% & 0.49 \\  & 200 & 74.24\% & 89.70\% & 0.32 \\  & 350 & 96.60\% & 98.10\% & 0.10 \\  & 500 & 96.63\% & 98.02\% & 0.11 \\  & 700 & 96.62\% & 98.30\% & 0.08 \\ \hline LSTM & 100 & 75.15\% & 93.33\% & 0.30 \\  & 200 & 86.97\% & 95.15\% & 0.15 \\  & 350 & 90.83\% & 93.75\% & 0.1 \\  & 500 & 92.12\% & 98.27\% & 0.09 \\  & 700 & 92.13\% & 98.35\% & 0.10 \\ \hline CNN+LSTM & 100 & 76.36\% & 95.15\% & 0.26 \\  & 200 & 86.67\% & 96.67\% & 0.15 \\  & **350** & **96.83\%** & **99.30\%** & **0.04** \\  & 500 & 96.63\% & 99.28\% & 0.05 \\  & 700 & 96.62\% & 99.74\% & 0.04 \\ \hline Transformer & 100 & 45.15\% & 65.85\% & 0.77 \\  & 200 & 49.77\% & 70.01\% & 0.73 \\  & 350 & 50.17\% & 72.84\% & 0.69 \\  & 500 & 51.15\% & 72.78\% & 0.68 \\  & 700 & 51.09\% & 72.33\% & 0.70 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance of the regression models applied to the Fine-Tuning Test dataset (with fine-tuning).