# Merging-Squeeze-Excitation Feature Fusion for Human Activity Recognition Using Wearable Sensors
## 6 Experimental Results and Discussion
### Computational Complexity Comparison  
Tables 17-19 show the numbers of trainable parameters of the baseline architectures, the proposed MSE feature fusion, and the extensions of the MSE feature fusion on classifying the PAMAP2, DaLiAc, and DSAD datasets, respectively. We do not specify the numbers of trainable parameters for individual merging methods since they are the same. The following results are obtained:  
* The numbers of trainable parameters of the proposed MSE feature fusion are higher than those of the single-branch architecture since the proposed MSE feature fusion consists of several branches using CNN models as feature extractors. On the other hand, the proposed MSE feature fusion requires lower numbers of trainable parameters than the multi-branch architecture does since the MSE feature fusion reduces the number of features which will enter the classification process by using the addition, maximum, minimum, and average merging instead of the concatenation merging.
* The numbers of trainable parameters of the extensions (of the MSE feature fusion) are slightly higher than those of the proposed MSE feature fusion since the modification parts in the extensions require few trainable parameters.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Merging**} & \multicolumn{4}{c}{**Number of MSE Feature Fusion Blocks (\(D\))**} \\ \cline{2-5}  & \(D\) = 1 & \(D\) = 2 & \(D\) = 3 & \(D\) = 4 & \(D\) = 5 \\ \hline Addition & 97.45 & 97.53 & 97.05 & 97.52 & 97.28 \\ Maximum & 97.68 & 97.19 & 97.60 & 97.32 & 97.52 \\ Minimum & 97.75 & 97.31 & 97.18 & 97.28 & 97.45 \\ Average & **98.04 * & 97.61 & 97.50 & 97.58 & 97.49 \\ \hline \hline \end{tabular}
\end{table}
Table 16: Accuracy scores (%) of the deep MSE feature fusion on classifying the DSAD dataset where AlexNet is applied as the feature extractor.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Merging**} & \multicolumn{4}{c}{**Number of MSE Feature Fusion Blocks (\(D\))**} \\ \cline{2-5}  & \(D\) = 1 & \(D\) = 2 & \(D\) = 3 & \(D\) = 4 & \(D\) = 5 \\ \hline Addition & 98.91 & 98.54 & 98.84 & 98.77 & 98.79 \\ Maximum & 99.06 & 98.73 & 98.94 & 99.08 & 99.06 \\ Minimum & **99.17 * & 99.03 & 99.03 & 99.03 & 98.99 \\ Average & 99.06 & 98.79 & 98.72 & 99.03 & 99.10 \\ \hline \hline \end{tabular}
\end{table}
Table 14: Accuracy scores (%) of the deep MSE feature fusion on classifying the PAMAP2 dataset where AlexNet is applied as the feature extractor.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Merging**} & \multicolumn{4}{c}{**Number of MSE Feature Fusion Blocks (\(D\))**} \\ \cline{2-5}  & \(D\) = 1 & \(D\) = 2 & \(D\) = 3 & \(D\) = 4 & \(D\) = 5 \\ \hline Addition & 97.63 & 97.69 & 97.32 & 96.90 & 98.03 \\ Maximum & 98.04 & 97.90 & 97.83 & 98.21 & 98.17 \\ Minimum & 98.18 & 98.04 & 98.03 & 97.74 & 98.06 \\ Average & **98.32 * & 98.06 & 97.49 & 97.41 & 98.08 \\ \hline \hline \end{tabular}
\end{table}
Table 15: Accuracy scores (%) of the deep MSE feature fusion on classifying the DaLiAc dataset where AlexNet is applied as the feature extractor.