## 3 Materials and Methods
### Adapted BPSO Algorithm for Feature Selection in the Case of DLAs Sensors Data
In Table 4 is presented as a summary of the experiments for each monitored subject \(S\), as follows: the mean running time in milliseconds \(t_{m}\), the mean fitness value \(f_{m}\), the mean number of returned features \(NRF_{m}\), and the mean accuracy \(a_{m}\). For each monitored subject, five experiments were performed. The value of \(NRF_{m}\) is from \(\{5,6,7,8\}\). In Figure 3, the evolution of the fitness value in each iteration of the adapted BPSO algorithm for each subject is presented. The fitness value is improved after 30 iterations for each monitored subject. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Significance** & **Value** \\ \hline \(N_{iterations}\) & number of iterations & 30 \\ \(N_{particles}\) & number of particles & 20 \\ \(c_{1}\) & cognitive component value & 2 \\ \(c_{2}\) & social component value & 2 \\ \(V_{min}\) & minimum value of velocity & \(-0.5\) \\ \(V_{max}\) & maximum value of velocity & 0.5 \\ \(w_{min}\) & minimum value of inertia & 0.500 \\ \(w_{max}\) & maximum value of inertia & 0.901 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Configuration of the adapted binary particle swarm optimization (BPSO) algorithm parameters. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Monitored Subject/** & \multirow{2}{*}{\(t_{1}\)} & \multirow{2}{*}{\(t_{2}\)} & \multirow{2}{*}{\(t_{3}\)} & \multirow{2}{*}{\(t_{4}\)} \\
**Sensor Particle Weight** & & & & \\ \hline \(S_{1}\) & 0.478 & 0.567 & 0.448 & 0.507 \\ \(S_{2}\) & 0.544 & 0.490 & 0.466 & 0.500 \\ \(S_{3}\) & 0.570 & 0.535 & 0.418 & 0.477 \\ \(S_{4}\) & 0.498 & 0.559 & 0.470 & 0.473 \\ \(S_{5}\) & 0.565 & 0.492 & 0.465 & 0.478 \\ \(S_{6}\) & 0.482 & 0.529 & 0.465 & 0.524 \\ \(S_{7}\) & 0.472 & 0.502 & 0.528 & 0.498 \\ \(S_{8}\) & 0.564 & 0.520 & 0.435 & 0.481 \\ \(S_{9}\) & 0.537 & 0.554 & 0.429 & 0.480 \\ \(S_{10}\) & 0.509 & 0.505 & 0.472 & 0.514 \\ \(S_{11}\) & 0.508 & 0.494 & 0.461 & 0.537 \\ \(S_{12}\) & 0.536 & 0.520 & 0.474 & 0.470 \\ \(S_{13}\) & 0.594 & 0.510 & 0.441 & 0.455 \\ \(S_{14}\) & 0.555 & 0.480 & 0.475 & 0.490 \\ \(S_{15}\) & 0.497 & 0.525 & 0.489 & 0.489 \\ \(S_{16}\) & 0.573 & 0.490 & 0.450 & 0.487 \\ \(S_{17}\) & 0.555 & 0.527 & 0.451 & 0.467 \\ \(S_{18}\) & 0.571 & 0.484 & 0.453 & 0.492 \\ \(S_{19}\) & 0.532 & 0.524 & 0.472 & 0.472 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Sensor particle weights for the DaLiAc dataset for each monitored subject. Comparison of the Results Obtained Using the Adapted Version of BPSO for Feature Selection with the Results Obtained Using Other Methods

Both for the adapted versions of GA and DE for feature selection, the same objective function was applied as in the case of the adapted version of BPSO. Moreover, the number of returned features was restricted to six in the case of GA, DE, and BPSO, using the heuristic for features ranking introduced in this article, which returns the results presented in Appendix A. In the case of the adapted version of BPSO the configuration parameters described in the previous section were applied. The following configurations are applied in the case of the other methods:

1. FFS and BFE--The standard configurations from KNIME, a threshold for the number of features equal to six and a random drawing strategy;
2. RF--The standard configurations from sklearn, a number of estimators equal to 1000, a maximum number of features equal to six;
3. GA--20 chromosomes, 30 iterations, \(CR\) (crossover rate) = 0.5, \(MR\) (mutation rate) = 0.5;

Figure 3: Fitness value evolution in each iteration of the adapted BPSO algorithm for the best results. (4)

DE--20 agents, 30 iterations, \(CR\) (crossover probability) = 0.5, \(F\) (differential weight) = 1.0. Both for the adapted versions of GA and DE for feature selection, the same objective function was applied, as in the case of the adapted version of BPSO.