# A Passive RF Testbed for Human Posture Classification in FM Radio Bands
## 4 Performance Evaluation  
In this section, we assess the classification techniques described in Section 3.3 with the objective of detecting and classifying the three different human postures from the computed features.  
The 180 s datasets were used in the classification and, consequently, in the performance evaluation results. Given that each dataset has nine different calibration portions (and the corresponding test portions), the accuracy of each class is defined as the mean accuracy considering all the calibration portions.  
The expression \(\frac{\text{mean accuracy of Class }0+\text{mean accuracy of Class }1+\text{mean accuracy of Class }2}{3}\) was computed to determine the accuracy of each classification method. KNN achieved the lowest accuracy, 61.030%, using the KNN classifier with K = 1 neighbors and using the cosine distance metric. On the other hand, SVM achieved the highest accuracy, 87.896%, with an RBF kernel. Considering each class individually, the mean accuracy was 87.434% for Class 0, 82.212% for Class 1, and 94.040% for Class 2. Taking into consideration the presented results, an SVM classifier with an RBF kernel was chosen for the classification tests.  
The confusion matrix obtained with the SVM classifier with the RBF kernel was computed considering the data from all the test portions of all 180 s datasets and is represented in Table 15. The columns represent the class, and the rows represent the predicted class. As such, the diagonal matrix entries represent the cases where each class is correctly predicted. Computing the accuracy metric using the diagonal matrix entries will result in \(\frac{8656+8139+9310}{(8656+1158+86)\times 3}\times 100=87.896\%\), which matches the value exposed above within the previous performance evaluation step. Note that the denominator operation corresponds to the total number of predictions.  
Taking into consideration the confusion matrix, Table 16 presents the accuracy of the classification testbed. Notice that Class 2 achieves the highest accuracy values, followed by Class 0, and ending with Class 1. Taking into consideration the three classes, the system achieves a mean precision of 87.933%, a mean recall of 87.895%, a mean specificity of 93.948%, and a mean F1-score of 87.908%  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **True** & \\ \hline \multirow{4}{*}{**SVM**} & \multicolumn{2}{c}{Class 0} & Class 1 & Class 2 \\ \cline{2-4}  & Class 0 & 8656 & 1393 & 86 \\ \cline{2-4}  & Class 1 & 1158 & 8139 & 504 \\ \cline{2-4}  & Class 2 & 86 & 368 & 9310 \\ \hline \hline \end{tabular}
\end{table}
Table 15: Confusion matrix computed considering the data from all test portions of all 180 s datasets.  
As it was mentioned before, two out of three features are dynamically selected for each dataset. To evaluate the effectiveness of the selection of the features, we assess the frequency of each combination of the selected features for all 180 s datasets, as presented in Table 17. Although there is a combination of selected features with a significantly higher probability (Feature6 and Feature2), the proposed solution adapts to the different conditions imposed by the calibration data.