A hybrid CNN-Transformer with Dynamic Variance Gate and physics-aware augmentation achieves 97.6% accuracy in NLoS and 98.8% in unseen environments for cross-domain WiFi fall detection without fine-tuning.
DapFall: Dynamic amplitude probability density profile-based Wi-Fi CSI sensing for fall detection
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Robust Cross-Domain WiFi Fall Detection via Physics-Driven Attention-Enhanced Transformers
A hybrid CNN-Transformer with Dynamic Variance Gate and physics-aware augmentation achieves 97.6% accuracy in NLoS and 98.8% in unseen environments for cross-domain WiFi fall detection without fine-tuning.