Fine-grained Heartbeat Waveform Monitoring with RFID: A Latent Diffusion Model

Cardiovascular disease remains a leading cause of mortality, necessitating continuous heart rate monitoring for early detection and prevention. Contactless technologies have gained significant attention due to their noninvasive and user-friendly nature. This paper introduces a contactless heartbeat waveform monitoring system utilizing commercial off-the-shelf (COTS) radio frequency identification (RFID) devices. The system extracts heartbeat signals by calculating the phase difference between two RFID tags placed on the body. To separate overlapping heartbeat and respiratory signals, empirical mode decomposition (EMD) is employed. More importantly, we propose a latent diffusion model that combines a variational autoencoder (VAE) and a denoising diffusion probabilistic model (DDPM) to recover the fine-grained heartbeat waveform. Experimental results show that our system achieves heart rate monitoring with a cosine similarity of 0.88 compared to the ground truth of an electrocardiogram (ECG).