# Merging-Squeeze-Excitation Feature Fusion for Human Activity Recognition Using Wearable Sensors
## 6 Experimental Results and Discussion
### Performance Comparison to Other HAR Approaches  
Table 20 shows the accuracy scores of other HAR approaches which were evaluated by using the PAMAP2, DaLiAc, and DSAD datasets. These accuracy scores were presented in their publications. Note that the evaluation setups and pre-processing may be different from ours. We compare them to the highest accuracy scores achieved by the original MSE feature fusion (Section 6.3.1). The proposed MSE feature fusion offers higher accuracy scores than those obtained from the other approaches.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Architecture** & **LeNet5** & **AlexNet** & **VGG16** \\ \hline \multirow{2}{*}{Baselines} & Single-Branch Model & 148,171 & 1,472,684 & 5,460,108 \\  & Multi-branch Model & 413,710 & 4,315,372 & 16,339,852 \\ \hline \multirow{5}{*}{Extensions} & Proposed MSE Feature Fusion & 179,155 & 3,841,100 & 15,489,612 \\  & Local Skip Connections & 179,155 & 3,841,100 & 15,489,612 \\  & Global Channel Attention & 182,890 & 3,857,772 & 15,555,724 \\  & Deep MSE (\(D\) = 2) & - & 3,891,116 & - \\  & Deep MSE (\(D\) = 3) & - & 3,941,132 & - \\  & Deep MSE (\(D\) = 4) & - & 3,991,148 & - \\  & Deep MSE (\(D\) = 5) & - & 4,041,164 & - \\ \hline \hline \end{tabular}
\end{table}
Table 17: PAMAP2 dataset: The numbers of trainable parameters of the baseline architectures and the proposed MSE feature fusion architectures on classifying the PAMAP2 dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Architecture** & **LeNet5** & **AlexNet** & **VGG16** \\ \hline \multirow{2}{*}{Baseline} & Single-Branch Model & 148,171 & 1,461,037 & 5,458,829 \\  & Multi-branch Model & 547,477 & 5,725,069 & 21,778,445 \\ \hline \multirow{5}{*}{Extensions} & Local Skip Connections & 193,777 & 5,005,325 & 20,470,029 \\  & Global Skip Connection & 193,777 & 5,005,325 & 20,470,029 \\  & Global Channel Attention & 197,512 & 5,021,997 & 20,536,141 \\  & Deep MSE (\(D\) = 2) & - & 5,072,013 & - \\  & Deep MSE (\(D\) = 3) & - & 5,138,701 & - \\  & Deep MSE (\(D\) = 4) & - & 5,205,389 & - \\  & Deep MSE (\(D\) = 5) & - & 5,272,077 & - \\ \hline \hline \end{tabular}
\end{table}
Table 18: DaLiAc dataset: The numbers of trainable parameters of the baseline architectures and the proposed MSE feature fusion architectures on classifying the DaLiAc dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Architecture** & **LeNet5** & **AlexNet** & **VGG16** \\ \hline \multirow{2}{*}{Baseline} & Single-Branch Model & 154,951 & 1,489,363 & 5,469,011 \\  & Multi-branch Model & 687,359 & 7,174,739 & 27,228,499 \\ \hline \multirow{5}{*}{Proposed MSE Feature Fusion} & 214,514 & 6,209,523 & 25,461,907 \\ \cline{1-1}  & Local Skip Connections & 214,514 & 6,209,523 & 25,461,907 \\ \cline{1-1}  & Global Skip Connection & 214,514 & 6,209,523 & 25,461,907 \\ \cline{1-1}  & Global Channel Attention & 218,249 & 6,226,195 & 25,528,019 \\ \cline{1-1}  & Deep MSE (\(D\) = 2) & - & 6,292,883 & - \\ \cline{1-1}  & Deep MSE (\(D\) = 3) & - & 6,376,243 & - \\ \cline{1-1}  & Deep MSE (\(D\) = 4) & - & 6,459,603 & - \\ \cline{1-1}  & Deep MSE (\(D\) = 5) & - & 6,542,963 & - \\ \hline \hline \end{tabular}
\end{table}
Table 19: DSAD dataset: The numbers of trainable parameters of the baseline architectures and the proposed MSE feature fusion architectures on classifying the DSAD dataset.