Hypnos: A Contactless Sleep Stage Monitoring System Using UWB Signals

As a fundamental physiological process, sleep plays a vital role in human health. High-quality sleep requires a reasonable distribution of sleep duration over different sleep stages. Recently, contactless solutions have been used for in-home sleep stage monitoring via wireless signals as it enables monitoring daily sleep in a non-intrusive manner. However, various factors, such as the subject's physiological characteristics during sleep, the subject's health status, and even the sleep environment, pose challenges to wireless signal analysis. In this paper, we propose Hypnos, a contactless sleep monitoring system that identifies different sleep stages using an ultra-wideband (UWB) device. Hypnos enables automated bed localization and extracts signals containing coarse-grained body movements and fine-grained chest movements due to breathing and heartbeat from the subject, which acts as the preparation step for sleep staging. The key to our system is a seq2seq deep learning model, which adopts an attention-based sequence encoder to learn the patterns and transitions within and between sleep epochs and combines with contrastive learning to improve the generalizability of the encoder. Particularly, we incorporate sleep apnea detection as an auxiliary task into the model to reduce the interference of sleep apnea with sleep staging. Moreover, we design a two-step training for better adaptation of subjects with different severities of sleep disorders. We conduct extensive experiments on 100 subjects, including healthy individuals and patients with sleep disorders, and the experimental results show that Hypnos achieves excellent performance in multi-stage sleep classification (including 5-stage sleep classification), and outperforms other baseline methods.