## 4 Experimental Results
### Experiment III: Comparison with Well-Known Classifiers  
In this experiment, we analyzed the performance of the proposed system while using two other well-known classifiers namely, K-nearest neighbors (KNN) and AdaBoost. After that, we compared the results with the original multilayer perceptron (MLP)-based system in terms of precision, recall, and F1-score. We repeated the process for all three datasets used in this study. Table 3 represents the comparison results using MobiAct_v2.0, while Table 4 shows the comparison using Real-World HAR, and finally, Table 5 summarizes the comparison results that we obtained using Real-Life HAR. Figure 13: Confusion matrix for Real-World HAR. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Activity Classes** & \multicolumn{2}{c}{**K-Nearest Neighbors (KNN)**} & \multicolumn{2}{c}{**AdaBoost**} & \multicolumn{4}{c}{**Multilayer Perceptron**} \\ \hline
**Activities** & **Precision** & **Recall** & **F1-Score** & **Precision** & **Recall** & **F1-Score** & **Precision** & **Recall** & **F1-Score** \\ \hline STDN & 0.58 & 0.93 & 0.72 & 0.38 & 0.67 & 0.49 & 0.87 & 0.87 & 0.87 \\ STUP & 0.58 & 0.93 & 0.72 & 0.16 & 0.20 & 0.18 & 0.86 & 0.80 & 0.83 \\ JUM & 1.00 & 1.00 & 1.00 & 0.57 & 0.53 & 0.55 & 1.00 & 1.00 & 1.00 \\ LV & 1.00 & 0.93 & 0.97 & 0.50 & 0.47 & 0.48 & 1.00 & 1.00 & 1.00 \\ RUN & 0.81 & 0.87 & 0.84 & 1.00 & 0.60 & 0.75 & 0.88 & 1.00 & 0.94 \\ SIT & 1.00 & 0.47 & 0.64 & 0.32 & 0.40 & 0.35 & 1.00 & 0.93 & 0.97 \\ STD & 0.67 & 0.53 & 0.59 & 0.16 & 0.20 & 0.18 & 0.72 & 0.87 & 0.79 \\ WAL & 0.62 & 0.33 & 0.43 & 0.00 & 0.00 & 0.00 & 0.92 & 0.73 & 0.81 \\ Mean & 0.78 & 0.75 & 0.74 & 0.39 & 0.38 & 0.37 & 0.91 & 0.90 & 0.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison of multilayer perceptron classifier with KNN and AdaBoost classifiers on Real-World HAR in terms of precision, recall, and F1-score. Figure 14: Confusion matrix for Real-Life HAR. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Activity Classes** & \multicolumn{2}{c}{**K-Nearest Neighbors (KNN)**} & \multicolumn{2}{c}{**AdaBoost**} & \multicolumn{4}{c}{**Multilayer Perceptron**} \\ \hline
**Activities** & **Precision** & **Recall** & **F1-Score** & **Precision** & **Recall** & **F1-Score** & **Precision** & **Recall** & **F1-Score** \\ \hline WAL & 0.95 & 0.90 & 0.92 & 0.52 & 0.52 & 0.52 & 0.95 & 0.97 & 0.96 \\ STU & 0.82 & 0.83 & 0.83 & 0.33 & 0.69 & 0.45 & 0.89 & 0.88 & 0.88 \\ STN & 0.82 & 0.89 & 0.85 & 0.34 & 0.29 & 0.32 & 0.87 & 0.89 & 0.88 \\ SCH & 0.92 & 0.96 & 0.94 & 0.19 & 0.99 & 0.32 & 0.98 & 0.98 & 0.98 \\ CHU & 0.97 & 0.98 & 0.98 & 0.00 & 0.00 & 0.00 & 0.99 & 0.95 & 0.97 \\ CSI & 0.95 & 0.96 & 0.96 & 0.00 & 0.00 & 0.00 & 0.97 & 0.97 & 0.97 \\ CSO & 1.00 & 0.98 & 0.99 & 0.52 & 0.07 & 0.13 & 0.98 & 0.99 & 0.99 \\ FOL & 0.42 & 0.52 & 0.46 & 0.00 & 0.00 & 0.00 & 0.69 & 0.68 & 0.68 \\ BSC & 0.85 & 0.66 & 0.75 & 0.00 & 0.00 & 0.00 & 0.84 & 0.86 & 0.85 \\ FKL & 0.49 & 0.48 & 0.49 & 0.00 & 0.00 & 0.00 & 0.70 & 0.70 & 0.70 \\ SDL & 0.80 & 0.74 & 0.77 & 0.00 & 0.00 & 0.00 & 0.84 & 0.85 & 0.85 \\ JOG & 0.89 & 0.91 & 0.90 & 0.00 & 0.00 & 0.00 & 0.94 & 0.95 & 0.95 \\ JUM & 0.91 & 0.81 & 0.86 & 0.50 & 0.05 & 0.09 & 0.94 & 0.94 & 0.94 \\ STD & 0.96 & 0.83 & 0.89 & 0.60 & 0.05 & 0.09 & 0.95 & 0.90 & 0.92 \\ SIT & 1.00 & 0.63 & 0.77 & 0.25 & 0.95 & 0.39 & 1.00 & 0.74 & 0.85 \\ Mean & 0.85 & 0.81 & 0.82 & 0.25 & 0.24 & 0.15 & 0.90 & 0.88 & 0.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison of multilayer perceptron classifier with KNN and AdaBoost classifiers on MobiAct_v2.0 in terms of precision, recall, and F1-score. The last row of Table 3 shows the mean precision, recall, and F1-score for each of the used classifiers. Performance statistics clearly state that the AdaBoost classifier was the worst performer among the three, and KNN was the second-best, while MLP performed the classification in the best way.