# Merging-Squeeze-Excitation Feature Fusion for Human Activity Recognition Using Wearable Sensors
## 6 Experimental Results and Discussion
### Proposed Merging-Squeeze-Excitation Feature Fusion
#### 6.3.2 MSE Feature Fusion with Skip Connections  
Tables 10-12 show the accuracy scores of the MSE feature fusion with skip connections proposed in Section 5.1 on classifying the PAMAP2, DaLiAc, and DSAD datasets, respectively. Each table presents the accuracy scores according to the merging methods, the DL models, and skip-connection methods. We have the following results:  
* On classifying the PAMAP2 dataset (Table 10), the MSE feature fusion with local skip connections achieves the highest accuracy score of 99.18% when using the minimum merging and AlexNet, while the MSE feature fusion with a global skip connection offers the highest accuracy score of 99.24% when using the average merging and AlexNet. Both architectures outperform the original MSE feature fusion (whose highest accuracy score is 99.17%).
* On classifying the DaLiAc dataset (Table 11), the MSE feature fusion with local skip connections achieves the highest accuracy score of 98.59% when using the minimum merging and AlexNet, while the MSE feature fusion with a global skip connection offers the highest accuracy score of 98.42% when using the minimum merging and AlexNet. Both architectures outperform the original MSE feature fusion (whose highest accuracy score is 98.32%).
* On classifying the DSAD dataset (Table 12), the MSE feature fusion with local skip connections achieves the highest accuracy score of 98.02% when using the average merging and AlexNet while the MSE feature fusion with a global skip connection offers the highest accuracy score of 97.97% when using the average merging and AlexNet. Both architectures offer lower accuracy scores than that of the original MSE feature fusion (whose highest accuracy score is 98.04%).
* Since the results are not conclusive, we cannot indicate whether the MSE feature fusion with skip connections is better than the original MSE feature fusion nor which skip connection method is the best.  
\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 & 98.68 & 98.75 & 98.16 & 98.49 & 98.77 & 98.23 \\ Maximum & 98.99 & 99.20 & 98.66 & 98.89 & 98.99 & 98.85 \\ Minimum & 99.05 & **99.18*** & 98.70 & 98.87 & 99.13 & 98.77 \\ Average & 98.91 & 99.15 & 98.70 & 98.72 & **99.24*** & 98.94 \\ \hline \hline \end{tabular}
\end{table}
Table 10: PAMAP2 dataset: Accuracy scores (%) of the MSE feature fusion with skip connections on classifying the PAMAP2 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 c c c} \hline \hline \multirow{2}{*}{**Merging**} & \multicolumn{3}{c}{**PAMAP2**} & \multicolumn{3}{c}{**DaLiAc**} & \multicolumn{3}{c}{**DSAD**} \\ \cline{2-10}  & **LeNet5** & **AlexNet** & **VGG16** & **LeNet5** & **AlexNet** & **VGG16** & **LeNet5** & **AlexNet** & **VGG16** \\ \hline Addition & 98.59 & 98.91 & 97.99 & 96.82 & 97.63 & 96.22 & 95.53 & 97.45 & 94.64 \\ Maximum & 98.92 & 99.06 & 98.82 & 98.03 & 98.04 & 96.92 & 97.42 & 97.68 & 96.00 \\ Minimum & 98.84 & **99.17*** & 98.72 & 97.59 & 98.18 & 97.72 & 97.34 & 97.75 & 97.50 \\ Average & 98.91 & 99.06 & 98.79 & 97.86 & **98.32*** & 97.67 & 97.00 & **98.04*** & 97.92 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Accuracy scores (%) of the MSE feature fusion 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.