## 4 Conclusions  
This paper introduced a contour-based method for Doppler envelope extraction that addresses key limitations of conventional percentile-based approaches in FMCW radar gait analysis. The conventional percentile-based Doppler envelope extraction method required manual selection of Doppler envelopes for the Spine, Leg, and Foot, which limited its practicality and consistency. In contrast, the proposed method automatically extracts the Doppler envelopes for these body parts, addressing the limitations of the conventional approach. To verify the accuracy and generalizability of the proposed method, experiments were conducted on five participants (three female and two male) with varying heights. Key gait parameters--such as walking speed, step length, and stride time--were compared with ground truth values obtained from a motion capture system. In most cases, the relative error remained within an acceptable range, typically below 10%. Notably, the proposed method consistently estimated the speeds of the Foot, Leg, and Spine regions with relative error levels comparable to the conventional method, demonstrating that it can serve as a viable alternative. Since the manual work required in percentile-based methods is eliminated, the proposed technique is expected to significantly improve the practicality of radar-based gait analysis. This contactless approach can be utilized not only in hospitals or rehabilitation centers but also in everyday environments such as homes. For example, if a radar is mounted on a wall in the residence of an elderly person living alone, the system can automatically extract Doppler envelopes without the need for expert supervision. This enables continuous daily monitoring of walking patterns, where any abnormal changes can trigger alerts--facilitating early detection of potential health issues or incidents. Moreover, the proposed algorithm exhibits low computational complexity, making it suitable for real-time applications. In our MATLAB R2023a implementation, the algorithm processed 30 s of radar data in approximately 1.74 s on a workstation equipped \begin{table}
\begin{tabular}{l l c c c} \hline \hline
**Region** & **Parameter** & \
\begin{tabular}{c} **Mean** \\ **(Proposed)** \\ \end{tabular} &
\begin{tabular}{c} **Mean** \\ **(Ground Truth)** \\ \end{tabular} & \(p\)**-Value** \\ \hline \multirow{5}{*}{Spine} & Speed & 1.414 & 1.340 & 0.398 \\  & StepLength & 0.646 & 0.650 & 0.818 \\  & StepTime & 0.538 & 0.564 & 0.441 \\  & StrideLength & 1.296 & 1.300 & 0.938 \\  & StrideTime & 1.068 & 1.110 & 0.613 \\ \hline \multirow{5}{*}{Leg} & Speed & 2.392 & 2.374 & 0.487 \\  & StepLength & 0.700 & 0.650 & 0.241 \\  & StepTime & 0.588 & 0.564 & 0.446 \\  & StrideLength & 1.378 & 1.300 & 0.320 \\  & StrideTime & 1.150 & 1.118 & 0.563 \\ \hline \multirow{5}{*}{Foot} & Speed & 3.150 & 3.310 & 0.124 \\  & StepLength & 0.638 & 0.648 & 0.374 \\ \cline{1-1}  & StepTime & 0.536 & 0.562 & 0.328 \\ \cline{1-1}  & StrideLength & 1.300 & 1.302 & 0.911 \\ \cline{1-1}  & StrideTime & 1.080 & 1.126 & 0.448 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Paired \(t\)-test results comparing the gait parameter values obtained by the proposed method and the ground truth for Spine, Leg, Foot. with a 32-core CPU and 128 GB of RAM. We believe the computational cost is acceptable, and further improvements could be achieved through algorithmic optimization or GPU acceleration. Nevertheless, this study has several limitations. The sample size is modest, and the validation was conducted only on participants with normal gait patterns. This limits the generalizability of the results to pathological gait conditions. Additionally, while statistical tests showed no significant differences, a few parameters exhibited larger deviations that may impact clinical interpretation--particularly in detecting small changes over time in longitudinal gait monitoring.