Robust Cross-Domain WiFi Fall Detection via Physics-Driven Attention-Enhanced Transformers

TL;DR

This paper introduces a physics-driven, attention-enhanced Transformer framework for WiFi-based fall detection that generalizes well across different environments and NLoS conditions, validated through extensive cross-domain tests and real-world deployment.

Contribution

It proposes a novel hybrid CNN-Transformer architecture with physics-inspired modules and data augmentation to improve cross-environment robustness in WiFi fall detection.

Findings

Achieves 97.6% accuracy in NLoS scenarios

Attains 98.8% accuracy in unseen environments

Demonstrates real-time robustness on edge computing devices

Abstract

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, acting as a soft-attention mask that eliminates static environmental DC components while amplifying dynamic human motion. Second, we introduce a Physics-Aware Data Augmentation strategy to force the network to learn invariant morphological signatures rather than environment-specific noise. Furthermore, a Convolutional Block Attention Module (CBAM) is integrated to refine spatiotemporal features prior to Transformer-based sequence modeling. Extensive cross-domain evaluations across four distinct indoor environments demonstrate that our method achieves 97.6\% accuracy in NLoS scenarios and 98.8\% in completely unseen environments without target-domain fine-tuning. Finally, we deploy the proposed framework on an edge computing system equipped with commercial WiFi NICs. Real-world live inference field tests confirm the system's robustness against unseen environmental layouts and its capability for continuous, low-latency whole-home safety monitoring.