Physics-Aware Lightweight Deep Learning Model for Human Breathing Detection via UWB Radar
34th Signal Processing and Communications Applications Conference, SIU 2026, İstanbul, Türkiye, 7 - 10 Temmuz 2026, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Doi Numarası: 10.1109/siu71813.2026.11636904
- Basıldığı Şehir: İstanbul
- Basıldığı Ülke: Türkiye
- Anahtar Kelimeler: convolution neural network, human detection, UWB radar
- Yıldız Teknik Üniversitesi Adresli: Evet
Özet
A physics-aware deep learning framework for human detection from raw ultra-wideband (UWB) radar signals is proposed in this paper. The proposed framework is based on a lightweight 2D Convolutional Neural Network (CNN) architecture that can be deployed on edge devices with limited computational resources. To better capture spatial-temporal and physiological patterns related to human respiration, physics-inspired features, including the Hilbert Envelope and Breathing Band Energy, are added as additional input channels to direct the network to concentrate on signal variations relevant to the presence of a human. In addition, model quantization methods are employed to drastically reduce the computational load and memory footprint, making it feasible to deploy on low-power embedded platforms. Experimental results show that the proposed framework achieves high detection accuracy with fast inference and minimal memory requirements.