Enhanced Radar Fall Detection via Compressed Sensing and Deep Learning-Assisted Point Cloud Generation

Existing fall detection based on radar point cloud faces the challenges of low resolution point cloud estimation and sparsity, which seriously restricts the system detection performance. This study proposes an innovative radar point cloud generation method combining compressed sensing and a depth camera to improve the accuracy and robustness of the fall detection system. Specifically, the study reformulates radar point cloud direction of arrival (DOA) estimation as a linear inverse problem and solves it using compressed sensing (CS) with the fast approximate message passing (AMP) algorithm, significantly improving point cloud positional resolution compared with the traditional fast Fourier transform (FFT) method. Subsequently, an Encoder-Decoder network based on the attention mechanism is designed to effectively enhance the density and expression ability of the point cloud by capturing the local features of the radar point cloud. The depth camera in this article is only used in the network training process. In the actual fall detection stage, the system only uses radar signals for classification. The experimental results verify the advantages of the proposed method in indoor fall detection. Compared with the traditional FFT-based point cloud generation results, the detection accuracy is improved from ∼ 91% to ∼ 96%. The innovative method proposed in this paper provides an efficient, economical, and easy-to-deploy technical solution for fall detection systems in the smart home environment, which has significant potential applications.