## 7 Detailed Analysis of Parameters and Nodes
### Processing Node  
As with branch nodes, we examined the properties of a limited number of derivatives of processing nodes. For this experiment, we added five processing nodes, N25-N29, to the existing processing nodes as shown in Table 13. Below is the definition.  
\[s(t)=s(t)-\alpha s(t-2)+\beta s(t-1)+\gamma s(t)+\beta s(t+1)+\alpha s(t+2) \tag{20}\]  
\[s(t)=\frac{1}{5}\{s(t-2)+s(t-1)+s(t)+s(t+1)+s(t+2))\} \tag{21}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ranking** & **N21** & **N22** & **N23** & **N24** & **Accuracy [\%]** \\ \hline
1 & & ✓ & & ✓ & 50.00 \\
2 & & ✓ & & & 48.83 \\
3 & & & ✓ & & 48.70 \\
4 & ✓ & & & & 48.61 \\
5 & ✓ & ✓ & & & 48.49 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Top five recognition accuracies of the added branch nodes.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Node** & **Left** & **Center** & **RIGHT** \\ \hline N21 & \(a(t)>a(t-2)\) & \(a(t)=a(t-2)\) & \(a(t)<a(t-2)\) \\ N22 & \(a(t)>a(t+2)\) & \(a(t)=a(t+2)\) & \(a(t)<a(t+2)\) \\ N23 & \(d_{2}(a)>0\) & \(d_{2}(a)=0\) & \(d_{2}(a)<0\) \\ N24 & \(d_{3}(a)>0\) & \(d_{3}(a)=0\) & \(d_{3}(a)<0\) \\ \hline \hline \end{tabular}
\end{table}
Table 9: Optimization experimentally obtained results for \(\mathcal{O}_{\mathbb{C}}\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ranking** & **N21** & **N22** & **N23** & **N24** & **Accuracy [\%]** \\ \hline
1 & & ✓ & & ✓ & 50.00 \\
2 & & ✓ & & & 48.83 \\
3 & & & ✓ & & 48.70 \\
4 & ✓ & & & & 48.61 \\
5 & ✓ & ✓ & & & 48.49 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Number of branch nodes used.  
\[s(t)=\alpha s(t-2)+\beta s(t-1)+\gamma s(t)+\beta s(t+1)+\alpha s(t+2) \tag{22}\] \[s(t)=s(t)-\alpha s(t-2)+\beta s(t-1)+\gamma s(t)+\beta s(t+1)+ \alpha s(t+2)\] (23) \[s(t)=\alpha s(t-2)+\beta s(t-1)+\gamma s(t)+\beta s(t+1)+ \alpha s(t+2). \tag{24}\]  
Table 14 shows the top five recognition accuracies of the added processing nodes for Dataset 6. The maximum recognition accuracy of 50.41% was obtained in the additional case of N25 and N26. The recognition accuracy was 4.91% higher than that before the addition.  
Table 15 presents the number of used processing nodes. As a utilization tendency, N7, N8, and N9 process slight signal changes used only one time. In contrast, N15, N18, and N19 process signal changes applied for neighbor sensor signals, used three times. Other nodes were used twice.  
Table 16 shows the top five recognition accuracies after changing N7, N8, and N29 to the new processing nodes, as denoted in Table 14. The maximum recognition accuracy of 50.36% was obtained in the case which exchanged N7 and N28, N8 and N26, and N9 and N29. The recognition accuracy was 4.81% higher than that before the exchange.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Node** & **Formula Index** & \(\alpha\) & \(\beta\) & \(\gamma\) \\ \hline N25 & (20) & \(-\)0.10 & 0.20 & 0.40 \\ N26 & (21) & & & \\ N27 & (22) & 0.10 & 0.20 & 0.40 \\ N28 & (23) & 0.05 & 0.15 & 0.60 \\ N29 & (24) & 0.05 & 0.15 & 0.60 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Additional processing nodes of five types.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ranking** & **N25** & **N26** & **N27** & **N28** & **N29** & **Accuracy [\%]** \\ \hline
1 & ✓ & ✓ & – & – & – & 50.41 \\
2 & ✓ & – & – & – & 49.26 \\
3 & ✓ & ✓ & – & ✓ & – & 48.96 \\
4 & – & ✓ & – & ✓ & ✓ & 48.90 \\
5 & – & ✓ & – & – & ✓ & 48.54 \\ \hline \hline \end{tabular}
\end{table}
Table 14: Top five recognition accuracies of the added processing nodes.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No. of Used Nodes** & **Index of Nodes** \\ \hline
1 & N7, N8, N9 \\
2 & N6, N10, N11, N12, N13, N14, N16, N17, N20 \\
3 & N15, N18, N19 \\ \hline \hline \end{tabular}
\end{table}
Table 15: Number of processing nodes used.