E2RespUNet: End-to-End Respiratory Signal Reconstruction and Rate Prediction Using a Unified Attention-Enhanced U-Net

Continuous, non-invasive respiratory rate (RR) monitoring is essential for the early diagnosis of many medi-cal problems. However, conventional contact-based sensors frequently under-perform in dynamic situations that can be un-comfortable and require human intervention. To overcome these limitations, we propose E2RespUNet, an end-to-end system that uses multimodal video data to estimate breathing rates and reconstruct respiratory signals using an attention-enhanced U-Net architecture. Our method combines optical flow analysis with preprocessing, detrending, and normalization to reliably extract chest motion features in a variety of settings. We validated our approach using a collection of RGB, near-infrared (NIR), and infrared (IR) videos from multiple sources, such as an in-house RGB dataset and a publicly available sleep dataset. According to our study in both the temporal and frequency domains, E2RespUNet surpasses existing baseline models by lowering the mean absolute error by up to 21 % in the sleep dataset and by up to 28% in the in-house dataset. Additionally, E2RespUNet addresses real-time deployment requirements on resource-constrained edge devices by processing each frame in less than 1 ms (0.99 ms in the Sleep dataset and 0.97 ms in the in-house dataset). These findings, together with a low KL divergence and a power spectral density (PSD) that corresponds to the ground truth, suggest that E2Resp UN et is accurate and fast enough for real-time use in emergency and clinical scenarios.