# Merging-Squeeze-Excitation Feature Fusion for Human Activity Recognition Using Wearable Sensors
## 6 Experimental Results and Discussion
### Proposed Merging-Squeeze-Excitation Feature Fusion
#### 6.3.4 Deep MSE Feature Fusion  
Tables 14-16 show the accuracy scores of the deep MSE feature fusion (in Section 5.3) using AlexNet as the feature extractor on classifying the PAMAP2, DaLiAc, and DSAD datasets, respectively. We consider only AlexNet since it outperforms the other DL models as shown in the previous subsections. Each table presents the accuracy scores according  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Merging**} & \multicolumn{3}{c}{**PAMAP2**} & \multicolumn{3}{c}{**DaLiAc**} & \multicolumn{3}{c}{**DSAD**} \\ \cline{2-9}  & **LeNet5** & **AlexNet** & **VGG16** & **LeNet5** & **AlexNet** & **VGG16** & **LeNet5** & **AlexNet** & **VGG16** \\ \hline Addition & 98.33 & 98.32 & 96.88 & 96.65 & 97.10 & 95.95 & 95.09 & 97.38 & 94.65 \\ Maximum & 98.94 & 99.06 & 98.04 & 97.73 & 97.55 & 96.85 & 97.54 & 97.81 & 97.12 \\ Minimum & 99.01 & **99.17 *** & 98.73 & 97.47 & **98.08 *** & 97.69 & 97.24 & 97.27 & 97.55 \\ Average & 98.82 & 98.99 & 98.73 & 97.60 & 97.78 & 97.28 & 97.08 & **98.03 *** & 97.97 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Accuracy scores (%) of the MSE feature fusion with global channel attention on classifying the PAMAP2, DaLiAc, and DSAD datasets where LeNet5, AlexNet, and VGG16 are applied as feature extractors. The asterisk (*) indicates the highest accuracy score of each dataset.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Merging**} & \multicolumn{3}{c}{**Local Skip Connections**} & \multicolumn{3}{c}{**Global Skip Connection**} \\ \cline{2-7}  & **LeNet5** & **AlexNet** & **VGG16** & **LeNet5** & **AlexNet** & **VGG16** \\ \hline Addition & 96.67 & 97.71 & 93.30 & 96.08 & 97.12 & 94.59 \\ Maximum & 98.30 & 97.78 & 97.13 & 98.19 & 97.97 & 97.00 \\ Minimum & 98.13 & **98.59 *** & 97.55 & 98.12 & **98.42 *** & 97.60 \\ Average & 98.12 & 97.92 & 97.68 & 98.05 & 98.06 & 98.00 \\ \hline \hline \end{tabular}
\end{table}
Table 11: DaLiAc dataset: Accuracy scores (%) of the MSE feature fusion with skip connections on classifying the DaLiAc dataset where LeNet5, AlexNet, and VGG16 are applied as feature extractors. The asterisk (*) indicates the highest accuracy score of each type of skip connections.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Merging**} & \multicolumn{3}{c}{**Local Skip Connections**} & \multicolumn{3}{c}{**Global Skip Connection**} \\ \cline{2-7}  & **LeNet5** & **AlexNet** & **VGG16** & **LeNet5** & **AlexNet** & **VGG16** \\ \hline Addition & 94.98 & 96.61 & 95.96 & 95.16 & 97.16 & 94.57 \\ Maximum & 97.65 & 97.40 & 97.00 & 97.57 & 97.89 & 97.27 \\ Minimum & 97.42 & 97.72 & 97.60 & 97.42 & 97.27 & 97.48 \\ Average & 97.18 & **98.02 *** & 97.83 & 97.12 & **97.97 *** & 97.97 \\ \hline \hline \end{tabular}
\end{table}
Table 12: DSAD dataset: Accuracy scores (%) of the SE feature fusion with skip connections on classifying the DSAD dataset where LeNet5, AlexNet, and VGG16 are applied as feature extractors. The asterisk (*) indicates the highest accuracy score of each type of skip connections.  
to the merging methods and the number of MSE feature fusion blocks (\(D\)). We see that the deep MSE feature fusion with \(D=1\) offers the highest accuracy scores for all three datasets (i.e, 99.17% for the PAMAP2 dataset, 98.32% for the DaLiAc dataset, and 98.04% for the DSAD dataset). In fact, with \(D=1\), the deep MSE feature fusion is equivalent to the original MSE feature fusion. This indicates that, for the investigated datasets, increasing the number of MSE feature fusion blocks does not provide any further useful information to the output feature maps \(\mathbf{\hat{Y}}^{leep,(D)}\) which are used in the classification process.