Contactless Radar Heart Rate Variability Monitoring Via Deep Spatio-Temporal Modeling

Radar sensing has been a promising solution for contactless monitoring of Heart Rate Variability (HRV), an essential indicator of the cardiovascular and autonomic nervous systems. However, existing works neglect heartbeat-driven body surface motions spreading across the entire body with spatial variations, which limits their accuracy in identifying fine-grid consecutive heartbeat timings and overall HRV performance. In this paper, we propose to exploit the entire body reflections and model the inherent spatial-temporal relationship between these reflections and heartbeats by deep neural network for contactless HRV monitoring. Specifically, a hybrid convolution-transformer-based network is designed to convert the complex multi-dimensional spatial-temporal modeling problem into an efficient sequence modeling process. Experimental results demonstrate its superiority over the baseline method, achieving the median IBI estimation error of 12ms (w.r.t. 98.47% accuracy), RMSDD error of 7.3ms, SDRR error of 2.9ms, pNN50 error of 5.5%.