# mmHSE: A Two-Stage Framework for Human Skeleton Estimation Using mmWave FMCW Radar Signals
## 5 Experimental Results and Analysis
### Comparative Experiment  
To validate the feasibility of performing HSE based on mmWave FMCW radar RA maps, we conduct comparative experiments using four representative deep learning models: ResNet-18 (2D convolutional backbone) [25], LSTM (temporal sequence modeling) [26], CNN-LSTM (spatial-temporal hybrid model) [27], and MobileViT (a lightweight vision transformer) [28]. The results are presented in Figure 7, with performance evaluated in terms of MAE and RMSE across the horizontal, vertical, and depth axes. Among the baselines, ResNet-18 exhibits the highest localization errors, yielding an average MAE of 5.86 cm and RMSE of 7.98 cm, with particularly poor performance in the depth direction (MAE = 7.18 cm; RMSE = 9.27 cm). In contrast, CNN-LSTM and MobileViT demonstrate significantly improved accuracy. CNN-LSTM achieves an MAE of 4.36 cm and RMSE of 6.38 cm, while MobileViT obtains an MAE of 4.83 cm and RMSE of 6.62 cm, highlighting the effectiveness of temporal modeling and global attention mechanisms for radar-based pose estimation. Furthermore, as shown in Figure 7, the proposed mmHSE framework achieves substantial performance gains over all baseline models. Specifically, mmHSE attains the lowest average MAE of 2.78 cm and RMSE of 4.35 cm, with consistently superior results across all spatial dimensions (e.g., horizontal MAE = 2.24 cm; depth RMSE = 5.51 cm). These find  
Figure 7: Verifying the feasibility of using range–angle maps for human skeleton estimation. (**a**) represents the MAE error, and (**b**) represents the RMSE error. ings validate the effectiveness of our dual-radar cooperative architecture and anatomically constrained refinement module. By incorporating human skeletal priors and exploiting cross-radar spatial complementarity, mmHSE significantly enhances the structural accuracy and robustness of 3D skeletal keypoint localization from sparse radar reflections. To further assess the effectiveness of the proposed mmHSE framework, we conduct comparative evaluations against four representative radar-based 3D human pose estimation methods: RF-Pose3D [21], RPM [22], mmPose [20], and HuPR [23]. As summarized in Table 2, mmHSE consistently achieves the lowest MAE across all spatial dimensions--horizontal, vertical, and depth--outperforming all baselines by a considerable margin. Compared to RF-Pose3D and mmPose--the two next-best performing methods with average MAE of 4.37 cm and 4.47 cm, respectively--mmHSE achieves a relative error reduction of 36.4% and 37.8%. The improvement is even more pronounced when compared to HuPR, with a 59.2% reduction in average MAE. Additionally, mmHSE consistently outperforms all methods on individual spatial axes: the horizontal, vertical, and depth errors are 2.24 cm, 2.75 cm, and 3.36 cm, respectively, whereas other methods exhibit imbalanced or substantially higher errors--for example, mmPose yields a horizontal MAE of 7.5 cm, and RF-Pose3D struggles in the depth dimension. These improvements are primarily attributed to mmHSE's dual-radar architecture and anatomically constrained refinement module, which together enhance both spatial resolution and pose consistency. While direct comparisons across methods may be influenced by differences in experimental protocols and preprocessing, the consistent performance gains observed under a unified evaluation pipeline demonstrate the superiority of mmHSE for radar-based skeletal keypoint estimation. To systematically evaluate the spatial reliability and anatomical consistency of the proposed mmHSE model, we further analyze the distribution of localization errors across all 21 skeletal keypoints, as illustrated in Figure 8. Both MAE and RMSE are computed along the horizontal, vertical, and depth dimensions. The results show that errors in the horizontal and vertical directions remain relatively stable across keypoints, exhibiting minor fluctuations without significant outliers.