## 4 Evaluation and Analysis
### Comparative Analysis  
This section presents the results obtained by our approach in comparison with state-of-the-art approaches [3; 8; 15]. For a fair comparison, we also use the WISDM dataset [31]. This dataset was collected using a smartphone accelerometer from 36 participants. The participants were asked to carry a smartphone in their front pant's leg pocket while performing activities as we collect in this work. The participants were asked to perform walking, walking upstairs, jogging, walking downstairs, sitting, and standing activities. This dataset is also used by various recent studies to improve activity recognition [3; 5; 6; 7; 8; 15]. The axis analysis results in Table 3 and Figure 10 show that our approach achieves a promising recognition rate using the y-z axis. We also use the (y-z) axis data of the WISDM dataset to compare the results.  
The Tables 4 and 5 and Figure 11 demonstrate the recognition rates on each activity using proposed approach and state-of-the-art studies. In Table 4, for standing activity, we achieve 1% and 4% better recognition rate than [8] and [3] respectively. For sitting activity, we achieve same recognition rate as [8]. However, we achieve 1% less recognition rate than the study [3] on sitting activity. In case of downstairs activity, we achieve 17% and 47% better recognition rate than [8] and [3]. For walking activity, we achieve 10% and 5% better recognition rate than [8] and [3]. In case of upstairs activity, we achieve 27% and 23% better recognition rate than [8] and [3] respectively. However, we achieve 1% and 2% less recognition rate than the study [15] and [3] on jogging activity. By using (y-z) axis of WISDM dataset, we achieve 93% recognition rate which is 12% and 13% better recognition rate than [8] and [3] respectively.  
The Table 5 shows the comparison results using Leave-One-Subject-Out (LOSO) cross-validation. It shows that for standing activity, we achieve the same recognition rate as [15]. For sitting activity, we  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **(x-y) axis** & **(x-z) axis** & **(y-z) axis** & **(x-y-z) axis** \\ \hline Standing & 90.1 & 88.2 & **96.4** & 93.7 \\ Sitting & 90.2 & 86.8 & **94.7** & 92.0 \\ Downstairs & 70.7 & 68.5 & **87.1** & 71.5 \\ Walking & 89.5 & 90.0 & **95.0** & 92.5 \\ Upstairs & 79.6 & 65.5 & **89.8** & 79.3 \\ Jogging & 92.2 & 94.1 & **96.5** & 94.0 \\ Overall & 85.4 & 82.2 & **93.3** & 87.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Recognition rate comparison of different combination of accelerometer axis with respect to each activity using MLP.  
Figure 10: Recognition rate comparison of different combination of accelerometer axis with respect to each activity using MLP.  
achieve an 8% better recognition rate than [15]. In the case of downstairs activity, we achieve a 2% better recognition rate than [15]. However, for walking activity, we achieve a 3% less recognition rate than [15]. In the case of upstairs activity, we achieve a 10% better recognition rate than [15]. However, we achieve a 2% less recognition rate than the study [15] on jogging activity. By using (y-z) axis of WISDM dataset, we achieve 91% recognition rate which is 3% better than [15].