## 3 Materials and Methods
### Adapted BPSO Algorithm for Feature Selection in the Case of DLAs Sensors Data
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline
**Subject/Features** & & & & & & & & & & & \\
**Selection** & & & & & & & & & & & \\ \hline \(S_{1}\) & 90.9\% & 93.7\% & 87.1\% & 89.2\% & 91.7\% & 92.8\% & 91.5\% & 92.1\% & 91.5\% & 90.1\% \\ \(S_{2}\) & 94.1\% & 97.1\% & 88.8\% & 90.6\% & 97.5\% & 97.5\% & 97.0\% & 95.8\% & 96.8\% & 94.2\% \\ \(S_{3}\) & 91.9\% & 94.0\% & 86.7\% & 90.3\% & 93.0\% & 93.4\% & 94.0\% & 93.4\% & 93.4\% & 92.7\% \\ \(S_{4}\) & 90.5\% & 96.2\% & 87.5\% & 89.5\% & 95.9\% & 95.9\% & 95.4\% & 95.7\% & 93.5\% & 91.2\% \\ \(S_{5}\) & 92.1\% & 95.4\% & 82.3\% & 90.7\% & 95.3\% & 95.3\% & 94.9\% & 94.7\% & 88.8\% & 93.6\% \\ \(S_{6}\) & 95.5\% & 94.1\% & 88.3\% & 93.3\% & 95.1\% & 96.8\% & 95.4\% & 96.6\% & 93.2\% & 94.4\% \\ \(S_{7}\) & 94.6\% & 95.0\% & 87.1\% & 93.0\% & 96.1\% & 95.7\% & 94.5\% & 92.5\% & 91.9\% & 91.2\% \\ \(S_{8}\) & 92.1\% & 93.9\% & 85.5\% & 88.3\% & 96.9\% & 96.9\% & 95.8\% & 95.1\% & 93.8\% & 88.7\% \\ \(S_{9}\) & 92.3\% & 93.4\% & 83.6\% & 88.2\% & 94.4\% & 94.3\% & 94.0\% & 94.7\% & 89.6\% & 94.0\% \\ \(S_{10}\) & 94.5\% & 94.1\% & 87.0\% & 88.4\% & 95.0\% & 95.0\% & 94.5\% & 92.4\% & 93.7\% & 90.0\% \\ \(S_{11}\) & 91.7\% & 91.4\% & 84.7\% & 88.6\% & 92.1\% & 91.9\% & 92.7\% & 90.4\% & 88.9\% & 89.3\% \\ \(S_{12}\) & 94.3\% & 94.1\% & 85.1\% & 90.5\% & 94.6\% & 95.6\% & 94.5\% & 94.0\% & 93.3\% & 94.5\% \\ \(S_{13}\) & 92.6\% & 94.1\% & 86.0\% & 89.8\% & 93.2\% & 92.8\% & 91.9\% & 94.0\% & 91.4\% & 91.4\% \\ \(S_{14}\) & 91.4\% & 95.6\% & 86.3\% & 91.3\% & 95.4\% & 95.9\% & 95.1\% & 95.0\% & 90.9\% & 94.1\% \\ \(S_{15}\) & 91.6\% & 93.2\% & 88.8\% & 91.7\% & 93.0\% & 93.3\% & 91.2\% & 92.5\% & 93.3\% & 90.0\% \\ \(S_{16}\) & 91.5\% & 91.6\% & 81.3\% & 90.1\% & 94.2\% & 94.2\% & 91.5\% & 92.8\% & 91.2\% & 89.3\% \\ \(S_{17}\) & 95.1\% & 95.3\% & 89.2\% & 94.5\% & 95.8\% & 95.9\% & 94.4\% & 94.5\% & 92.8\% & 95.0\% \\ \(S_{18}\) & 89.0\% & 93.3\% & 83.6\% & 88.1\% & 93.5\% & 93.5\% & 91.1\% & 92.6\% & 88.7\% & 93.9\% \\ \(S_{19}\) & 92.1\% & 96.0\% & 88.4\% & 92.2\% & 96.2\% & 97.0\% & 96.2\% & 93.4\% & 92.9\% & 91.2\% \\ \hline
**Average** & 92.5\% & 94.2\% & 86.1\% & 90.4\% & 94.6\% & 94.9\% & 93.9\% & 93.8\% & 92.0\% & 92.0\% \\ \hline \hline \end{tabular}

* (1) The best results are underlined with one line, (2) the second best results are underlined with two lines, (3) the third best results are underlined with three lines, (4) the worst results are underlined with one dash line.

\end{table} 
Table 6: The random forest accuracy classification results for each feature selection approach for the DaLiAc dataset for each monitored subject 1.

and the worst results were obtained in the case of the activities bicycling (50 watt) (\(DLA_{11}\)) and bicycling (100 watt) (\(DLA_{12}\)). Comparison of the Performance of the BPSO Based Approach with the Performance of Literature Approaches

In this subsection, the results obtained using the machine learning methodology based on BPSO are compared with some of the best literature results. Table 7 presents a comparison of the results obtained, using the adapted version of the BPSO algorithm with the results obtained in the literature. As can be seen in the table, the approach proposed in this article returns results comparable to the ones from the literature. The method presented in [33] returns much better results; however, in our approach we consider a maximum of six features and, in that approach, all features are used, therefore the two approaches are not directly comparable.