A Semi-Supervised Learning Method for Human Keypoints Detection with FMCW Radar

FMCW radar-based human keypoints detection has been increasingly used in health monitoring, human-computer interaction, safety detection and other fields due to its characteristics of contact-free, privacy-preserving and less environmentdependence. Recently, most of studies have adopted deep learning method to detect human keypoints for its powerful feature extraction ability. However, its excellent performance relies on large-scale labeled datasets participating in model training, while the data labeling process is difficult and time-consuming, making it unsuitable for promotion and application. To address this issue, we propose a two-branch semi-supervised method to improve the accuracy and generalization of keypoints detection through using both labeled and unlabeled radar data. Models of two branches deal with different radar point clouds generated by same human activity, and promote the consistency and complementarity in the learning features of two branches through introducing a consistency loss function. Optimal performance in human keypoints detection can be achieved through adjusting weights between consistency loss and supervised loss. In addition, we develop a grouping processing technique of radar point clouds to obtain two different point cloud clusters with minimal feature overlap. We evaluate our model on a real-world radar dataset and compare its performance with a supervised method that only uses 20 labeled subjects. The experimental results demonstrate that the proposed method achieves a 10.7% improvement in accuracy compared to traditional supervised learning method, highlighting the significant advantages of our method in human keypoints detection and its substantial potential for broad application and promotion.