# mmHSE: A Two-Stage Framework for Human Skeleton Estimation Using mmWave FMCW Radar Signals
## 5 Experimental Results and Analysis
### Ablation Experiment  
#### 5.4.1 The Influence of the Module  
To quantitatively evaluate the contribution of each component within the mmHSE framework, we perform a comprehensive ablation study, with results summarized in Table 3. The baseline configuration, which includes only the Stage 1 architecture without any auxiliary modules, yields an average localization error of MAE = 7.34 cm and RMSE = 9.86 cm. When the data augmentation strategies introduced in Section 4.1 are applied, the performance improves significantly, reducing the error to MAE = 5.86 cm and RMSE = 8.04 cm. This demonstrates the effectiveness of augmentation in enhancing generalization and mitigating overfitting.  
Further performance gains are observed with the integration of the multi-view fusion module. While fusion alone (without Stage 2) improves the results to MAE = 4.62 cm and  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Data Aug** & **Stage1** & **Fusion** & **Stage2** & **MAE (cm)** & **RMSE (cm)** \\ \hline  & ✓ & & & 7.34 & 9.86 \\ ✓ & ✓ & & & 5.86 & 8.04 \\ ✓ & ✓ & ✓ & & 4.62 & 7.19 \\ ✓ & ✓ & & ✓ & 3.84 & 5.83 \\ ✓ & ✓ & ✓ & ✓ & 2.78 & 4.35 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation study on the effects of different components.  
Figure 9: Estimation performance of the mmHSE system in cross-domain and cross-user scenarios. “CD” represents cross-domain, and “CU” represents cross-user. (**a**) represents the MAE error for cross-domain and cross-user, and (**b**) represents the RMSE error for cross-domain and cross-user.  
RMSE = 7.19 cm, a more pronounced enhancement is achieved by enabling Stage 2 alone, yielding MAE = 3.84 cm and RMSE = 5.83 cm. This indicates that the anatomically constrained refinement in Stage 2 plays a more pivotal role than early-stage multi-view fusion. Notably, the full configuration--incorporating data augmentation, Stage 1, fusion, and Stage 2--achieves the best overall performance, with MAE = 2.78 cm and RMSE = 4.35 cm. This represents a relative improvement of 62.1% in MAE and 55.9% in RMSE over the baseline, and a 27.6% and 25.4% improvement over the second-best variant (Stage 1 + Stage 2 without fusion), respectively. These results highlight a synergistic effect wherein the combination of all components yields multiplicative performance benefits, rather than merely additive gains.  
Figure 10 presents the qualitative comparison of predicted skeletal keypoints before and after refinement for two representative actions: stand and waving. Taking the stand action as an example and referencing the keypoint annotations in Figure 3d, we observe that the unrefined predictions (stage 1) contain clear anatomical inaccuracies. Specifically, the distance between the neck and shoulder center is excessively large, and the angle between the left ankle and left foot deviates substantially from natural human articulation, resulting in visibly unrealistic limb configurations.  
After refinement (Stage 2), the structural optimizer effectively corrects these discrepancies. The predicted keypoints are adjusted to better align with the ground truth positions, exhibiting more consistent bone lengths (e.g., between neck and shoulder center) and more plausible joint angles (e.g., between left ankle and left foot). As a result, the refined skeleton demonstrates improved continuity and anatomically valid postures. In the waving action, the refinement similarly enhances joint symmetry and produces more natural arm elevation and torso rotation. These visual improvements align with the quantitative  
Figure 10: Comparison of coarse estimation and optimized estimation for the 21 skeletal keypoints. The top row represents the action “stand”, and the bottom row represents the action “waving”.  
performance gains and underscore the importance of the Stage 2 refinement module in producing accurate and realistic human pose estimations from radar signals.