Leveraging Attention-reinforced UWB Signals to Monitor Respiration During Sleep

The respiration state during overnight sleep is an important indicator of human health. However, existing contactless solutions for sleep respiration monitoring either perform in controlled environments and have low usability in practical scenarios, or only provide coarse-grained respiration rates, being unable to accurately detect abnormal events in patients. In this paper, we propose Respnea, a non-intrusive sleep respiration monitoring system using an ultra-wideband (UWB) device. Particularly, we propose a profiling algorithm, which can locate the sleep positions in non-controlled environments and identify different subject states. Further, we construct a deep learning model that adopts a multi-head self-attention mechanism and learns the patterns implicit in the respiration signals to distinguish sleep respiration events at a granularity of seconds. To improve the generalization of the model, we propose a contrastive learning strategy to learn a robust representation of the respiration signals. We deploy our system in hospital and home scenarios and conduct experiments on data from healthy subjects and patients with sleep disorders. The experimental results show that Respnea achieves high temporal coverage and low errors (a median error of 0.27 bpm) in respiration rate estimation and reaches an accuracy of 94.44% on diagnosing the severity of sleep apnea-hypopnea syndrome (SAHS).