# mmHSE: A Two-Stage Framework for Human Skeleton Estimation Using mmWave FMCW Radar Signals
###### Abstract  
We present mmHSE, a two-stage framework for human skeleton estimation using dual millimeter-Wave (mmWave) Frequency-Modulated Continuous-Wave (FMCW) radar signals. To enable data-driven model design and evaluation, we collect and process over 30,000 range-angle maps from 12 users across three representative indoor environments using a dual-node radar acquisition platform. Leveraging the collected data, we develop a two-stage neural architecture for human skeleton estimation. The first stage employs a dual-branch network with depthwise separable convolutions and self-attention to extract multi-scale spatiotemporal features from dual-view radar inputs. A cross-modal attention fusion module is then used to generate initial estimates of 21 skeletal keypoints. The second stage refines these estimates using a skeletal topology module based on graph convolutional networks, which captures spatial dependencies among joints to enhance localization accuracy. Experiments show that mmHSE achieves a Mean Absolute Error (MAE) of 2.78 cm. In cross-domain evaluations, the MAE remains at 3.14 cm, demonstrating the method's generalization ability and robustness for non-intrusive human pose estimation from mmWave FMCW radar signals. human skeleton estimation; mmWave FMCW radar; range-angle maps; cross-domain 2020 IEEEexample: accepted: 72016/928-9309-4/19  
systems combined with computer vision algorithms, offer high localization accuracy under favorable conditions [8]. Yet, their performance is highly sensitive to environmental lighting [9]. More critically, the acquisition and processing of visual biometric data raise increasing concerns over user privacy. To address the limitations of traditional HSE methods, recent work has explored alternative sensing modalities based on radio frequency signals [10]. Early studies primarily employed commercial Wi-Fi devices operating in the 2.4/5 GHz bands [11], achieving coarse-grained action recognition by analyzing time-frequency features derived from channel state information. However, the relatively low carrier frequencies (e.g., 12.5 cm wavelength at 2.4 GHz) result in strong diffraction effects and poor spatial resolution, making fine-grained skeletal localization particularly difficult. The mmWave Frequency-Modulated Continuous-Wave (FMCW) radar offers distinct advantages for human sensing due to its favorable physical properties. For example, a 60 GHz mmWave FMCW radar with a 4 GHz bandwidth can achieve a wavelength of 5 mm and a minimum range resolution of 3.75 cm. The wavelength \(\lambda\) is inversely proportional to the center frequency \(f_{c}\), as given by \(\lambda=\frac{c}{f_{c}}\), and the range resolution \(\Delta r\) is inversely proportional to the bandwidth \(BW\), following \(\Delta r=\frac{c}{2 BW}\), where \(c\) is the speed of light [12; 13]. The combination of a high carrier frequency and wide bandwidth enables high-resolution, multi-dimensional motion perception by accurately measuring range, velocity, and angle. In particular, the wide bandwidth is critical for achieving fine range resolution, allowing the system to distinguish between closely spaced objects and improving perception accuracy in complex environments. Furthermore, recent advances in compact and low-cost mmWave FMCW radar chipsets have significantly reduced deployment barriers [14], paving the way for consumer-grade and ubiquitous HSE applications. Current mmWave FMCW radar-based HSE approaches mainly follow two paradigms. The first is the point cloud-based method [15; 16; 17; 18], which processes sparse radar point cloud data using spatiotemporal modeling and attention mechanisms to estimate human poses. These methods preserve geometric structure and spatial topology [19], but the inherent sparsity and noise in radar point clouds lead to substantial information loss, ultimately limiting pose estimation accuracy.