# A Passive RF Testbed for Human Posture Classification in FM Radio Bands
## 3 Feature Engineering and Classification
### Feature Selection
The mean represents the average performance of a particular feature considering all datasets. The median, on the other hand, indicates the overall performance while ignoring the effect of outliers. The dissimilarity is computed as \(\frac{\text{Mean-Median}}{\text{Mean}}\times 100\) and represents the relative difference between the mean and the median value of a particular feature. The normalized variance is computed as \(\frac{\text{Variance-Min}}{\text{Max-Min}}\times 100\), where Max is the maximum variance value from all 15 features and Min is the minimum variance value from all 15 features. The normalized variance indicates how distant the F-statistic values are from the mean value. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Acquisition Period** & **Class Samples** & **Number of Feature Outputs** \\ \hline
20 s & 1.5 MS & 1.5 M/25 k = 60 \\ \hline
30 s & 1.5 MS & 1.5 M/25 k = 60 \\ \hline
90 s & 6.5 MS & 6.5 M/25 k = 260 \\ \hline
180 s & 14 MS & 14 M/25 k = 560 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Description of the number of feature outputs for all datasets. \begin{table}
\begin{tabular}{l l} \hline \hline
**Feature Label** & **Formula** \\ \hline Feature7 & skew (\(|\mathbf{r}|\)) \\ \hline Feature8 & skew (\(|\mathbf{s}|\)) \\ \hline Feature9 & mean (\(|\mathbf{r}|\)) \\ \hline Feature10 & mean (\(|\mathbf{s}|\)) \\ \hline Feature11 & mean (\(|\mathbf{r}\)-\(\mathbf{s}|\)) \\ \hline Feature12 & MAD (\(|\mathbf{r}|\)) \\ \hline Feature13 & MAD (\(|\mathbf{s}|\)) \\ \hline Feature14 & std (\(\Re\epsilon(\mathbf{r})\)) \\ \hline Feature15 & std (\(\Re\epsilon(\mathbf{s})\)) \\ \hline \hline \end{tabular}
\end{table} 
Table 10: _Cont._Each feature represented in Tables 12 and 13 is color-labeled, and each color identifies which element was used to compute the feature. The blue color represents a feature computed from data acquired from the reference antenna, the yellow color represents a feature computed from data acquired from the surveillance antenna, and the orange color represents a feature that was computed using the data that resulted from the difference between the complex values acquired by the reference antenna (reference signal) and the complex values acquired by the surveillance antenna (surveillance signal). Regarding the reference signal, Feature1 and Feature5 are the two features containing the lowest mean and median values. Comparing them, Feature5 exhibits lower mean and median values and higher dissimilarity and normalized variance values. For this reason, Feature1 is preferred. The features Feature9 and Feature14 are very similar in terms of their performance, both having a higher mean and median value than Feature1. Nonetheless, their dissimilarity and normalized variance values are significantly higher than Feature1. Given that a robust and consistent system is desired, Feature1 is preferred as a discriminant of the data obtained from the reference antenna. Regarding the data obtained from the surveillance antenna, although Feature10 and Feature15 are the features that contain the highest mean value of all features, they also have the highest normalized variance values. Thus, Feature2 is preferred to describe the data obtained from the surveillance antenna. Lastly, considering the features computed from the difference between the reference and surveillance data, Feature11 achieves the highest dissimilarity and normalized variance values of the three candidate features, and hence, it is discarded. The features Feature3 and Feature6 are very similar: Feature6 achieves a higher dissimilarity value and Feature3 has a higher normalized variance. Feature6 is chosen as the preferred one because we consider the variance as a more important metric in terms of measuring the overall performance of the system. The two features to be used in the real-time classification process, out of Feature1, Feature2, and Feature6, are dynamically selected from the ones achieving the best ANOVA F-statistic.