## 3 Materials and Methods
### Adapted BPSO Algorithm for Feature Selection in the Case of DLAs Sensors Data
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Approach** & **Result** & **Method** \\ \hline Our approach & 93.8\% accuracy & The features are selected using an adapted version of BPSO and the data is classified using RF \\ \hline \multirow{3}{*}{([PERSON] et al. [32])} & \multirow{3}{*}{93.4\% accuracy} & A method that applies the score-drift feature selection, based on an algorithm for feature extraction, selection, and classification \\ \hline \multirow{3}{*}{([PERSON] et al. [16])} & \multirow{3}{*}{89.6\% accuracy} & A hierarchical multi-sensor based classification system \\ \hline \multirow{3}{*}{([PERSON] et al. [33])} & \multirow{3}{*}{96.4\% accuracy} & A method that is based on Iss2 Image-UCNet6 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of the results obtained using the adapted version of the BPSO algorithm with the results obtained in the literature using the DaLiAc dataset as the experimental support. Figure 5: The DLAs precision results for the DaLiAc dataset using adapted BPSO and RF for each monitored subject. ## 6 Conclusions

In this article, we presented a novel approach for feature selection in the case of data that is generated by sensors that monitor various types of DLAs. The method was tested and validated on the DaLiAc dataset. The experimental results show that the adapted version of BPSO presented in this article is comparable to other classical algorithms such as FFS, BFE, and RF, in terms of running time and classification performance. The running time of the adapted version of BPSO is better than the running time of BFE and the developed objective function also returns promising results in combination with other bio-inspired algorithms, such as GA and DE. Moreover, for some monitored subjects the approach based on BPSO returns results that are much better than the ones obtained using classical algorithms for feature selection. Even if the proposed method based on BPSO returns results, which are not as good as the ones obtained using classical methods for some monitored subjects, further improvements of the objective function might lead to better results. For example, the method presented in [33] performs significantly better,,; however, it requires complex computational resources and image processing transformations. As future research work the following research directions are proposed: (1) The testing and the validation of the proposed methodology using other validation types, such as the leave-one-subject validation type or the leave-two-subjects validation type, (2) the proposal of another heuristic for the ranking of the features returned by the adapted version of BPSO and (3) the comparison of the performance of the adapted version of BPSO with the performance of similar approaches based on other types of bio-inspired algorithms. Conceptualization, D.M., I.A. and T.C. ; methodology, D.M. and T.C. ; software, D.M. and I.A. ; validation, D.M., I.A. and T.C. ; formal analysis, I.S. ; resources, I.A., T.C. and I.S. ; writing-original draft preparation, D.M. ; writing-review and editing, D.M., I.A. and T.C. ; supervision, I.S. ; project administration, I.A. and T.C. ; funding acquisition, I.A., T.C. and I.S. All authors have read and agreed to the published version of the manuscript. This research was funded by the Romanian National Authority for Scientific Research and Innovation, CCCDI-UEFISCDI and of the AAL Programme with co-funding from the European Union's Horizon 2020 research and innovation programme grant number AAL59/2018 ReMIND within PNCDI III. The APC was funded by the Technical University of Cluj-Napoca through the grants for scientific research support programme. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: