# A Passive RF Testbed for Human Posture Classification in FM Radio Bands
## 2 Experimental Testbed
### Datasets  
Initially, we acquired smaller datasets (lasting 20 and 30 s). Table 5 depicts a description of 13 different datasets. For each table row, only one of the two files generated by the antennas is described, since both files have equal sizes and formats. The first four datasets only target two of the three classes. Then, the next nine datasets already take into consideration all classes. We highlight that the last three datasets were also collected in the secondary lab, and its name holds the \"_2\" suffix, not to be confused with the datasets obtained in the same lab and with the same order of classes. Considering these smaller datasets, our objective was to understand if there was any visual difference between each class and if the order in which each class was collected by the system was relevant and influenced the results. The first datasets were also used to characterize the quality of different features computed from the data, and their performance was visually analyzed. To process and analyze each dataset, we read back the complex values stored in the generated binary files describing the complex samples acquired from the two antennas. The names of the datasets follow the structure depicted in Figure 5, where one of the datasets described above is used as an example. \begin{table}
\begin{tabular}{l 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\_Class01 & 18/05 & 20 dB & 20 s & 5 MS & 40 MB \\ \hline mainLab\_Class10 & 18/05 & 20 dB & 20 s & 5 MS & 40 MB \\ \hline mainLab\_Class02 & 18/05 & 20 dB & 20 s & 5 MS & 40 MB \\ \hline mainLab\_Class12 & 18/05 & 20 dB & 20 s & 5 MS & 40 MB \\ \hline mainLab\_Class012 & 18/05 & 20 dB & 30 s & 7.5 MS & 60 MB \\ \hline mainLab\_Class102 & 18/05 & 20 dB & 30 s & 7.5 MS & 60 MB \\ \hline mainLab\_Class210 & 18/05 & 20 dB & 30 s & 7.5 MS & 60 MB \\ \hline secondaryLab\_Class102 & 20/05 & 16 dB & 30 s & 7.5 MS & 60 MB \\ \hline secondaryLab\_Class201 & 20/05 & 16 dB & 30 s & 7.5 MS & 60 MB \\ \hline secondaryLab\_Class201 & 20/05 & 16 dB & 30 s & 7.5 MS & 60 MB \\ \hline secondaryLab\_Class210 & 20/05 & 16 dB & 30 s & 7.5 MS & 60 MB \\ \hline secondaryLab\_Class102\_2 & 20/05 & 16 dB & 30 s & 7.5 MS & 60 MB \\ \hline secondaryLab\_Class201\_2 & 20/05 & 16 dB & 30 s & 7.5 MS & 60 MB \\ \hline secondaryLab\_Class201\_2 & 20/05 & 16 dB & 30 s & 7.5 MS & 60 MB \\ \hline secondaryLab\_Class201\_2 & 20/05 & 16 dB & 30 s & 7.5 MS & 60 MB \\ \hline secondaryLab\_Class210\_2 & 20/05 & 16 dB & 30 s & 7.5 MS & 60 MB \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Description of the 20 and 30 s datasets (main and secondary labs). Given that we did not consider transitions between the sampling process of each human posture, the data acquired between different postures need to be removed. Having a continuous flow of data being collected, we consider that the posture transitions do not last longer than 2 s and, consequently, we removed 2 s of signal at the beginning and at the end of each posture's period acquisition. Table 6 provides information about the time division of each class for each dataset. Note that in this table, it is assumed that Class 0 is the first posture to be sampled, followed by Class 1, and finishing with Class 2. After evaluating the smaller datasets, we acquired larger datasets lasting 90 and 180 s to have more data to work with and, consequently, have more representative data to analyze and make conclusions. Each dataset may be used to assess the real-time acquisition and evaluate the classification of human postures in an offline manner. Table 7 depicts a description of 30 different datasets. We collected the datasets on different days in order to take into consideration variations in the sampled signal. We also tested different bias-tee gains to analyze their impact.