## 3 Experiments and Result
### Result
#### 3.2.2 Gait Parameters
method often reduces variability and outliers; meanwhile, the summary statistics in Table 4 capture the overall accuracy and stability of both methods. For example, while the mean error for Spine Stride Time in the proposed method exceeds 10%, the boxplot reveals that this is primarily due to one or two outlying cases, with most estimates clustered below that level. This comparison reinforces the observation that the proposed method achieves consistent and reliable performance across most gait parameters, even in the presence of individual variations. Compared to conventional radar-based gait estimation methods, which often rely on fixed percentile thresholds [16; 17; 18; 19], the proposed contour-based method offers a more adaptive and automated approach to envelope extraction. This adaptability reduces the dependency on human expertise and enhances robustness across individuals with different gait characteristics. To quantitatively evaluate the performance of the proposed Doppler envelope extraction method, paired \(t\)-tests were conducted between the gait parameter values obtained using the proposed method and the ground truth. Table 5 presents the results of a paired \(t\)-test comparing the average values of gait parameters estimated by the proposed method against the ground truth. Across all parameters and body regions (Spine, Leg, Foot), the p-values were greater than 0.05, indicating no statistically significant difference between the proposed method and the ground truth. This result suggests that the proposed method provides gait parameter estimates that are statistically equivalent to those obtained using the motion capture system. However, some gait parameters showed relatively large numerical differences despite being statistically insignificant. For instance, the Foot speed showed a difference of 0.16 m/s (Proposed: 3.150 m/s, GT: 3.310 m/s; \(p=0.124\)), and the Step Length of the Leg differed by 0.05 m (Proposed: 0.700 m, GT: 0.650 m; \(p=0.241\)). Although these differences were not statistically significant, they may reflect slightly higher estimation variability in parameters involving more dynamic movement, such as the Foot. These results demonstrate that the proposed method closely approximates the gold-standard values provided by the motion capture system. The consistency across various gait parameters supports its potential as a reliable and non-invasive tool for radar-based gait \begin{table}
\begin{tabular}{l l c c} \hline \hline \multirow{2}{*}{**Region**} & \multirow{2}{*}{**Parameter**} & \multicolumn{2}{c}{**Conventional**} & **Proposed** \\  & & **(Mean \(\pm\) Std)** & **(Mean \(\pm\) Std)** \\ \hline \multirow{4}{*}{Spine} & Speed & 14.94 \(\pm\) 3.95 & 9.49 \(\pm\) 10.11 \\  & StepLength & 2.66 \(\pm\) 3.31 & 3.65 \(\pm\) 3.64 \\ \cline{1-1}  & StepTime & 7.64 \(\pm\) 7.16 & 7.67 \(\pm\) 8.30 \\ \cline{1-1}  & StrideLength & 5.24 \(\pm\) 5.04 & 5.11 \(\pm\) 5.46 \\ \cline{1-1}  & StrideTime & 8.67 \(\pm\) 8.59 & 10.07 \(\pm\) 9.34 \\ \hline \multirow{4}{*}{Leg} & Speed & 6.33 \(\pm\) 5.44 & 1.28 \(\pm\) 1.96 \\  & StepLength & 5.46 \(\pm\) 5.06 & 10.51 \(\pm\) 8.54 \\ \cline{1-1}  & StepTime & 9.39 \(\pm\) 7.65 & 10.14 \(\pm\) 5.60 \\ \cline{1-1}  & StrideLength & 3.95 \(\pm\) 3.63 & 8.13 \(\pm\) 8.77 \\ \cline{1-1}  & StrideTime & 7.57 \(\pm\) 9.58 & 8.10 \(\pm\) 5.37 \\ \hline \multirow{4}{*}{Foot} & Speed & 8.23 \(\pm\) 3.76 & 5.10 \(\pm\) 4.52 \\ \cline{1-1}  & StepLength & 7.58 \(\pm\) 3.40 & 3.43 \(\pm\) 0.86 \\ \cline{1-1}  & StepTime & 10.78 \(\pm\) 7.37 & 7.07 \(\pm\) 5.58 \\ \cline{1-1}  & StrideLength & 6.16 \(\pm\) 2.86 & 2.29 \(\pm\) 1.35 \\ \cline{1-1}  & StrideTime & 11.25 \(\pm\) 7.95 & 6.80 \(\pm\) 7.44 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Mean and standard deviation of the relative error (%) for each gait parameter and body region. analysis, though further validation with a larger sample size could help reduce variability and improve generalizability.