Sleep is essential for health and well-being, yet the gold-standard method for sleep stage assessment, polysomnography (PSG), requires overnight monitoring with numerous wired sensors and manual scoring, limiting its scalability and comfort in real-world settings. We present ViSS, the first end-to-end deep learning framework for fully non-contact sleep staging directly from raw infrared video. To capture the dual temporal structure of sleep, ViSS integrates per-epoch motion encoding and long-range sequence modeling in a compact hierarchical architecture, without relying on intermediate physiological signals. Evaluated on the largest infrared video dataset to date, including both healthy individuals and patients, ViSS achieves 80% accuracy and 0.78 macro F1-score using fewer than 7 million parameters. These results establish infrared video as a viable modality for automated sleep staging and highlight the potential of ViSS for scalable, privacy-preserving home-based assessment.

Obstructive sleep apnea (OSA) is a prevalent sleep disorder, affecting approximately one billion individuals globally. In this study, we aim to address the limitations of Polysomnography (PSG), the gold standard for OSA diagnosis, by developing SlAction, a non-intrusive system that utilizes infrared videos for OSA detection in daily sleep settings. Considering the privacy-sensitive nature of sleep videos, SlAction is designed to analyze data directly on the camera-capturing device, eliminating the need to transmit video data to a server. With the collaboration of clinical experts, we extensively analyze the largest dataset worldwide that we collected, establishing correlations between OSA events and human motions during sleep. Our novel approach achieved an OSA prediction performance with an F1 score of 0.88. Notably, even when running on a low-spec CPU, our SlAction operates approximately 75 times faster than previous work evaluated on high-performance GPU servers.