FilterNet: A Filtered Gait Motion Fusion Network for Classifying Adult Spinal Deformity
Published in 2026 International Conference on Electronics, Information, and Communication (ICEIC), 2026
Analyzing periodic human motion from videos is vital for applications such as action recognition and healthcare. In gait analysis, models must capture subtle phase-specific motion patterns. However, processing entire sequences often introduces noise and reduces accuracy. We propose FilterNet, a frameselection framework that identifies phase-relevant frames to improve temporal modeling and enhance discriminative representation. On a clinical gait video dataset, FilterNet achieves 74.5% accuracy, 75.2% precision, and 74.3% F1-score, outperforming baseline methods. Though demonstrated in a medical context, the framework is broadly applicable to other periodic motion analysis tasks. Code is available at: https://github.com/ChenKaiXuSan/FilterNet ASD PyTorch.
Recommended citation: K. Chen et al., "FilterNet: A Filtered Gait Motion Fusion Network for Classifying Adult Spinal Deformity," 2026 International Conference on Electronics, Information, and Communication (ICEIC), Macau, China, 2026, pp. 1-6, doi: 10.1109/ICEIC69189.2026.11386438.
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