# mmHSE: A Two-Stage Framework for Human Skeleton Estimation Using mmWave FMCW Radar Signals
## 6 Conclusions  
This paper introduces mmHSE, a novel framework for 3D human skeleton estimation that leverages cooperative sensing from dual mmWave FMCW radars. The system integrates a two-stage estimation network, spatiotemporal fusion, and kinematic constraint refinement to deliver accurate and anatomically consistent pose predictions. Comprehensive experiments demonstrate the effectiveness of each module, the contribution of temporal context, and the robustness of mmHSE under cross-domain and cross-user evaluations. The results show that mmHSE not only achieves competitive accuracy but also generalizes well to challenging real-world radar-based pose estimation scenarios.  
To further enhance the applicability and generalization of mmHSE, we outline several future directions:  
* Improved Depth Estimation and Real-Time Deployment: While existing mmWave-based systems achieve reasonable accuracy in the xy-plane, they often suffer from  
Figure 11: The impact of historical frames on the mmHSE system. (**a**) represents the experimental MAE error of historical frames, and (**b**) represents the experimental RMSE error of historical frames.  
limited precision in the z-axis. Future work will focus on improving depth estimation by leveraging larger and more diverse datasets, as well as more powerful neural network architectures. Additionally, we can explore some high-resolution research methods, as higher image quality may lead to improved estimation performance. Moreover, to support deployment on resource-constrained edge devices, we plan to incorporate model compression techniques such as pruning, quantization, and hardware-aware optimization, enabling efficient real-time skeletal tracking in practical applications.
* Deployment Across Diverse Scenes and Action Categories: To extend the utility of mmHSE to more realistic scenarios, we aim to adapt the system for complex and dynamic environments, including cluttered indoor settings and unstructured outdoor spaces. In addition, we will expand the action space to include diverse body movements such as sitting, crouching, falling, and dancing. These enhancements are expected to facilitate broader adoption in applications such as smart healthcare, behavior understanding, and human-robot collaboration.
* Multimodal Integration with Vision-Language Models and Heterogeneous Data: We plan to explore the integration of mmWave FMCW radar signals with large-scale vision-language models to enrich semantic reasoning and contextual awareness. Furthermore, we will curate a large-scale benchmark composed of both public and self-collected datasets to support training under cross-domain and cross-user conditions. This direction aims to promote robust multimodal learning and transferable representations for generalized human pose estimation in the wild.  
Conceptualization, J.T. and Y.Z.; methodology, J.T.; software, J.T.; formal analysis, J.T.; investigation, J.T. and J.L.; data curation, J.L.; writing--original draft preparation, J.T.; writing--review and editing, Y.Z. and J.L.; visualization, J.T.; project administration, Y.Z.; funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research is supported partly by the Guangzhou GJYC Fund No. 2024D0 IJ0010, partly by Shenzhen Science and Technology Program Fund No. KQTD20230301092840010, as well as partly by the SCUT Fund No. K3200890.  
Not applicable.  
The data used in this paper are collected through our own experiments and are not yet publicly available. However, data may be obtained from the authors upon reasonable request.  
The authors declare no conflicts of interest. The affiliation with Shenzhen Jelicomm Co., Ltd. is for institutional identification purposes only and does not imply any financial or personal interests that could influence the research.