A sampling-rate-versatile transformer network with dynamic augmentation achieves stable high accuracy for Wi-Fi-based motion and gesture recognition across variable sampling rates and traffic patterns.
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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.
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Practical Wi-Fi-based Motion Recognition Under Variable Traffic Patterns
A sampling-rate-versatile transformer network with dynamic augmentation achieves stable high accuracy for Wi-Fi-based motion and gesture recognition across variable sampling rates and traffic patterns.
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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.