## 5 Discussion
### The Capability to Represent the Individual Motion of the Proposed Method  
In the motion tactics model proposed in Section 3, we explored the parameter space where the system remains stable and determined the gain \(K_{\text{base}}\) that allows the system to reach steady-state running in the shortest time. Figure 3a shows that the model with base gain achieves stable running in approximately 10 s, during which the stiffness and attitude angle are dynamically optimized. Beyond optimizing for stability and convergence speed, we also examined how individual movements can be represented within the constructed solution space. By adjusting the gain weights to align stride and cadence with experimental values, we investigated whether the model could account for variations in human running patterns. As shown in Table 4, we confirmed the existence of parameter sets that can represent individual motion. Figure 3b presents the simulation results when gain weights were adjusted to reproduce the motion. Although the convergence time is slower than that of the simulation using \(K_{\text{base}}\), stable running results are obtained. Table 5 further evaluates the accuracy of this personalized modeling, comparing simulated results to experimental data. Notably, for cadence in P.2 and P.4, the confidence intervals do not include zero; however, the values remain sufficiently close to zero, suggesting that the differences between simulation and experimental data are minimal. These findings showed that adjusting the gain weights enables the model to better approximate participant-specific variations in stride and cadence. This indicates that the proposed model, which generates running by combining motion tactics, can represent the performance to be evaluated. Although gain adjustments were performed through an exhaustive search in this study, we believe that this process can be automated using optimization frameworks, such as machine learning algorithms. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Mean**} & \multicolumn{2}{c}{**SD**} & \\ \cline{3-8}
**Participant** & **Metric** & **Sim** & **Exp** & **Sim** & **Exp** & **95\% CI** \\ \hline P.1 & Stride (m) & 0.796 & 0.797 & 1.40 \(\times 10^{-4}\) & 2.22 \(\times 10^{-2}\) & [\(-\)7.15 \(\times 10^{-3}\), 5.15 \(\times 10^{-3}\)] \\  & Cadence (steps/s) & 2.79 & 2.79 & 5.40 \(\times 10^{-4}\) & 2.05 \(\times 10^{-2}\) & [\(-\)8.59 \(\times 10^{-3}\), 2.79 \(\times 10^{-3}\)] \\  & Amplitude (mm) & 50.1 & 41.4 & 2.85 \(\times 10^{-2}\) & 1.05 & [8.43, 9.01] \\ P.2 & Stride (m) & 0.871 & 0.873 & 7.00 \(\times 10^{-4}\) & 2.31 \(\times 10^{-2}\) & [\(-\)4.81 \(\times 10^{-3}\), 1.93 \(\times 10^{-3}\)] \\  & Cadence (steps/s) & 2.55 & 2.55 & 3.79 \(\times 10^{-3}\) & 1.47 \(\times 10^{-2}\) & [ 7.42 \(\times 10^{-4}\), 6.26 \(\times 10^{-3}\)] \\  & Amplitude (mm) & 61.8 & 43.4 & 2.45 \(\times 10^{-1}\) & 1.26 & [1.82 \(\times 10^{1}\), 1.87 \(\times 10^{1}\)] \\ P.3 & Stride (m) & 0.819 & 0.817 & 8.90 \(\times 10^{-4}\) & 2.48 \(\times 10^{-2}\) & [\(-\)1.86 \(\times 10^{-3}\), 4.88 \(\times 10^{-3}\)] \\  & Cadence (steps/s) & 2.71 & 2.71 & 3.37 \(\times 10^{-3}\) & 2.12 \(\times 10^{-2}\) & [\(-\)4.25 \(\times 10^{-3}\), 2.25 \(\times 10^{-3}\)] \\  & Amplitude (mm) & 56.5 & 46.8 & 1.76 \(\times 10^{-1}\) & 1.01 & [9.54, 9.86] \\ P.4 & Stride (m) & 0.954 & 0.958 & 3.70 \(\times 10^{-4}\) & 3.05 \(\times 10^{-2}\) & [\(-\)8.05 \(\times 10^{-3}\), 1.03 \(\times 10^{-3}\)] \\  & Cadence (steps/s) & 2.33 & 2.32 & 2.00 \(\times 10^{-3}\) & 2.14 \(\times 10^{-2}\) & [ 2.58 \(\times 10^{-3}\), 9.22 \(\times 10^{-3}\)] \\  & Amplitude (mm) & 77.9 & 60.2 & 1.69 \(\times 10^{-1}\) & 1.93 & [1.74 \(\times 10^{1}\), 1.80 \(\times 10^{1}\)] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of Simulation and Experiment Results. The table presents the mean and standard deviation (SD) of stride length, cadence, and amplitude for simulation (Sim) and experimental (Exp) conditions.