# A Passive RF Testbed for Human Posture Classification in FM Radio Bands
## 3 Feature Engineering and Classification
### Classification Process Overview  
The solutions proposed in this work consider that the classification algorithms take advantage of an initial stage of calibration, where the user is asked to pose according to each posture class, and labeled data are acquired for learning purposes. In the methodology, the datasets are split into calibration and test. The calibration data are used to extract knowledge that is further used to classify the test data. The calibration data are used to compute \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Day** & \begin{tabular}{c} **Bias-Tee** \\ **Gain** \\ \end{tabular} & \
\begin{tabular}{c} **Acquisition** \\ **Period** \\ \end{tabular} &
\begin{tabular}{c} **Total** \\ **Samples** \\ \end{tabular} & **File Size** \\ \hline mainLab\_Class012\_12 & 01/07 & 18 dB & 180 s & 45 MS & 360 MB \\ \hline mainLab\_Class012\_13 & 01/07 & 20 dB & 180 s & 45 MS & 360 MB \\ \hline mainLab\_Class012\_14 & 01/07 & 35 dB & 180 s & 45 MS & 360 MB \\ \hline mainLab\_Class012\_15 & 01/07 & 35 dB & 180 s & 45 MS & 360 MB \\ \hline mainLab\_Class012\_16 & 13/07 & 18 dB & 90 s & 22.5 MS & 180 MB \\ \hline mainLab\_Class012\_17 & 13/07 & 55 dB & 90 s & 22.5 MS & 180 MB \\ \hline mainLab\_Class012\_18 & 13/07 & 18 dB & 90 s & 22.5 MS & 180 MB \\ \hline mainLab\_Class012\_19 & 13/07 & 55 dB & 90 s & 22.5 MS & 180 MB \\ \hline mainLab\_Class012\_20 & 18/07 & 55 dB & 180 s & 45 MS & 360 MB \\ \hline mainLab\_Class012\_21 & 19/07 & 55 dB & 180 s & 45 MS & 360 MB \\ \hline mainLab\_Class012\_22 & 19/07 & 35 dB & 180 s & 45 MS & 360 MB \\ \hline mainLab\_Class012\_23 & 25/07 & 35 dB & 180 s & 45 MS & 360 MB \\ \hline mainLab\_Class012\_24 & 25/07 & 35 dB & 90 s & 22.5 MS & 180 MB \\ \hline mainLab\_Class012\_25 & 26/07 & 35 dB & 180 s & 45 MS & 360 MB \\ \hline mainLab\_Class012\_26 & 26/07 & 55 dB & 180 s & 45 MS & 360 MB \\ \hline mainLab\_Class012\_27 & 28/07 & 35 dB & 180 s & 45 MS & 360 MB \\ \hline mainLab\_Class012\_28 & 28/07 & 55 dB & 90 s & 22.5 MS & 180 MB \\ \hline mainLab\_Class012\_29 & 29/07 & 55 dB & 180 s & 45 MS & 360 MB \\ \hline mainLab\_Class012\_30 & 29/07 & 35 dB & 90 s & 22.5 MS & 180 MB \\ \hline \hline \end{tabular}
\end{table} 
Table 7: _Cont._ \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Acquisition** & **Class 0** & **Class 1** & **Class 2** &
\begin{tabular}{c} **Class** \\ **Duration** \\ \end{tabular} & **Class Samples** \\ \hline
90 s & [2, 28] s & [32, 58] s & [62, 88] s & 26 s & 26 \(\times\) sample rate = 6.5 MS \\ \hline
180 s & [2, 58] s & [62, 118] s & [122, 178] s & 56 s & 56 \(\times\) sample rate = 14 MS \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Time division of each class for the datasets lasting 90 s and 180 s.  
a set of features (presented in Section 3.2) and to identify a subset of features that better discriminate the calibration data. Note that different datasets, with different calibration data, can produce different feature selections. We also propose labeling the calibration data in order to follow a supervised learning approach in the classification process. An overview of the classification process is presented in the block diagram depicted in Figure 6, which summarizes the approach followed in this work. The top right dataset block is constructed by merging the calibration data (green color) from each class into the calibration portion and merging the test data (blue color) from each class into the test portion while maintaining the order of each class. As depicted, the calibration data are used to define which features should be computed and, in this way, the combination of the selected features is dynamically chosen. Then, the information regarding the selection of the features and the labeled data from the calibration portion are sent to the classification algorithm to predict the data in the test portion. The datasets lasting 90 s and 180 s are split into calibration and test portions. The calibration portion has a total duration of 18 s, which represents 6 s of data (or 1.5 MS) for each class.