Robust Cross-Domain WiFi Fall Detection via Physics-Driven Attention-Enhanced Transformers
Device-free fall detection utilizing WiFi Channel State Information (CSI) has emerged as a promising, privacy-preserving solution for elderly health monitoring in the Internet of Things (IoT) era. However, existing deep learning approaches suffer from severe performance degradation when deployed in unseen environments due to static background overfitting and Non-Line-of-Sight (NLoS) signal attenuation. To address these critical bottlenecks, we propose a robust, domain-generalizable framework featuring a novel Attention-Enhanced CNN-Transformer hybrid architecture. First, we design a physics-driven \textbf{Dynamic Variance Gate (DVG)} to dynamically calculate local temporal variance, actin...
| Authors | Yingzhe Wang, Cunhua Pan, Ruijing Liu et al. |
|---|---|
| Primary category | eess.SP |
| Published | 23 April 2026 |
| Last updated at source | 23 April 2026 |
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