## 3 Materials and Methods
### Adapted BPSO Algorithm for Feature Selection in the Case of DLAs Sensors Data
\begin{tabular}{l l} AAL & ambient assisted living \\ BFE & backward features elimination \\ BPSO & binary particle swarm optimization \\ C & chest \\ CNN & convolutional neural network \\ CR & crossover rate or crossover probability \\ DaLiAc & Daily Life Activities \\ DE & differential evolution \\ DLA & daily living activity \\ ERT & extremely randomized trees \\ F & differential weight \\ FFS & forward feature selection \\ GA & genetic algorithm \\ k-NN & k-nearest neighbor \\ KNIME & Konstanz Information Miner \\ HPSO-LS & hybrid particle swarm optimization with local search \\ HPSO-SSM & hybrid particle swarm optimization with a spiral-shaped mechanism \\ \end{tabular} 

## Appendix A

The ranking of the features for each monitored subject \(S\) of the DaLiAc dataset in the adapted version of BPSO introduced in this article is presented next:

1. \(S_{1}\)-- \(CA_{1},CA_{3},WA_{3},CA_{2},WA_{1},WA_{2},HA_{3},HA_{1},HA_{2},AA_{3},AA_{2},AA_{1}, CG_{3},HG_{3},WG_{2},AG_{1},\)\(AG_{2},\)\(CG_{2},\)\(HG_{2},\)\(HG_{1},\)\(CG_{1},WG_{3},WG_{1},\)\(AG_{3};\)
2. \(S_{2}\)-- \(CA_{1},CA_{3},HA_{3},CA_{2},HA_{1},WA_{3},HA_{2},WA_{1},WA_{2},AA_{2},AA_{3},AA_{1}, CG_{3},HG_{3},HG_{1},CG_{1},\)\(CG_{2},\)\(HG_{2},WG_{2},\)\(AG_{1},WG_{3},\)\(AG_{2},\)\(AG_{3},WG_{1};\)
3. \(S_{3}\)-- \(CA_{1},H_{3},H_{2},CA_{2},HA_{1},CA_{3},WA_{1},WA_{3},WA_{2},AA_{3},AA_{2},AA_{1}, CG_{3},HG_{3},HG_{1},HG_{2},\)\(CG_{2},WG_{2},\)\(CG_{1},WG_{3},\)\(AG_{1},\)\(AG_{2},\)\(AG_{3},WG_{1};\)
4. \(S_{4}\)-- \(CA_{1},H_{2},CA_{3},HA_{1},HA_{3},CA_{2},WA_{1},WA_{3},WA_{2},AA_{3},AA_{2},AA_{1}, CG_{3},HG_{3},HG_{1},\)\(AG_{1},\)\(AG_{3},\)\(CG_{2},\)\(HG_{2},WG_{2},WG_{3},\)\(AG_{2},\)\(CG_{1},WG_{3},\)\(AG_{2},\)\(CG_{1},WG_{3},\)\(AG_{2},WG_{1};\)
5. \(S_{5}\)-- \(CA_{1},CA_{3},CA_{2},WA_{3},HA_{3},HA_{2},WA_{1},HA_{1},WA_{2},AA_{3},AA_{2},AA_{1}, CG_{3},HG_{3},CG_{2},WG_{2},\)\(AG_{1},HG_{2},HG_{1},CG_{1},WG_{3},\)\(AG_{2},\)\(AG_{3},WG_{1};\)
6. \(S_{6}\)-- \(HA_{3},CA_{1},WA_{3},HA_{2},CA_{3},WA_{1},CA_{2},HA_{1},WA_{2},AA_{3},AA_{2},AA_{1}, CG_{3},HG_{3},WG_{2},HG_{2},\)\(HG_{1},CG_{2},CG_{1},WG_{3},\)\(AG_{1},\)\(AG_{2},WG_{1},AG_{3};\)
7. \(S_{7}\)-- \(CA_{1},CA_{3},CA_{2},WA_{1},WA_{3},HA_{1},HA_{3},HA_{2},WA_{2},AA_{3},AA_{2},AA_{1}, CG_{3},CG_{1},HG_{3},CG_{2},\)\(WG_{2},\)\(HG_{1},WG_{3},\)\(AG_{1},\)\(AG_{2},\)\(AG_{3},\)\(HG_{2},WG_{1};\)
8. \(S_{8}\)-- \(CA_{1},CA_{3},CA_{2},HA_{2},WA_{3},HA_{1},WA_{1},HA_{3},WA_{2},AA_{3},AA_{2},AA_{1}, CG_{3},AG_{1},HG_{3},CG_{2},\)\(WG_{2},\)\(HG_{1},WG_{3},\)\(AG_{2},\)\(HG_{2},\)\(CG_{1},\)\(AG_{3},WG_{1};\)
9. \(S_{9}\)-- \(HA_{3},WA_{1},HA_{1},WA_{3},CA_{1},HA_{2},CA_{3},CA_{2},WA_{2},AA_{2},AA_{3},AA_{1}, CG_{3},HG_{3},HG_{2},HG_{1},\)\(WG_{2},\)\(CG_{2},\)\(AG_{1},WG_{3},\)\(CG_{1},\)\(AG_{3},WG_{1},\)\(AG_{2};\)
10. \(S_{10}\)-- \(CA_{1},\)\(HA_{3},\)\(CA_{3},CA_{2},\)\(HA_{2},\)\(HA_{1},WA_{1},WA_{3},WA_{2},\)\(AA_{3},\)\(AA_{2},\)\(AA_{1},\)\(CG_{3},\)\(HG_{3},\)\(AG_{1},\)\(AG_{2},\)\(CG_{2},\)\(CG_{1},\)\(HG_{1},WG_{3},\)\(AG_{3},WG_{2},\)\(HG_{2},WG_{1};\)
11. \(S_{11}\)-- \(HA_{3},\)\(CA_{1},\)\(HA_{2},\)\(CA_{3},\)\(HA_{1},\)\(CA_{2},WA_{3},WA_{2},WA_{1},\)\(AA_{3},\)\(AA_{2},\)\(AA_{1},\)\(CG_{3},\)\(HG_{3},\)\(HG_{1},\)\(CG_{1},\)\(AG_{1},\)\(AG_{2},\)\(HG_{2},WG_{2},WG_{3},\)\(CG_{2},\)\(AG_{3},WG_{1};\)
12. \(S_{12}\)-- \(CA_{3},CA_{1},CA_{2},\)\(HA_{3},WA_{3},WA_{1},\)\(HA_{1},HA_{2},WA_{2},\)\(AA_{3},\)\(AA_{2},AA_{1},CG_{3},CG_{1},CG_{2},WG_{2},\)\(HG_{3},WG_{3},\)\(AG_{1},\)\(HG_{1},\)\(AG_{2},\)\(AG_{3},\)\(AG_{2},WG_{1};\)
13. \(S_{13}\)-- \(CA_{1},\)\(CA_{2},\)\(CA_{3},\)\(HA_{1},\)\(HA_{3},\)\(HA_{2},\)\(WA_{3},WA_{1},\)\(WA_{2},\)\(AA_{3},\)\(AA_{2},\)\(AA_{1},\)\(CG_{3},\)\(AG_{1},\)\(HG_{1},\)\(HG_{3},\)\(CG_{1},\)\(CG_{2},WG_{2},\)\(AG_{2},WG_{3},\)\(AG_{3},\)\(HG_{2},WG_{1};\)
14. \(S_{14}\)-- \(HA_{3},\)\(CA_{1},\)\(HA_{2},\)\(HA_{1},WA_{3},\)\(CA_{3},WA_{1},\)\(CA_{2},\)\(WA_{2},\)\(AA_{3},\)\(AA_{2},\)\(AA_{1},\)\(HG_{3},\)\(CG_{3},\)\(HG_{1},\)\(WG_{2},\)\(HG_{2},WG_{3},\)\(AG_{1},\)\(CG_{2},\)\(AG_{2},\)\(CG_{1},\)\(AG_{3},WG_{1};\)
15.