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• The proposed PCFEx techniques capture meaningful information at point, edge, and graph levels of point clouds by treating them as graphs.method 90%
• The approach achieves significantly reduced errors in all three human pose estimation benchmarks compared to existing methods.result 90%
• The method achieves 98.8% overall accuracy in mmWave-based human activity recognition, outperforming state-of-the-art models.result 95%
• The work demonstrates that feature extraction combined with GNN modeling enhances precision of point cloud processing.technical 85%
• The evaluation uses four popular publicly available millimeter wave radar datasets.method 90%
• The proposed GNN architecture is designed to efficiently process point cloud features extracted by PCFEx.method 85%
• The research focuses on applying GNNs to 3D point cloud data specifically for human pose estimation and human activity recognition.technical 90%

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