Non-contact on-device detection of obstructive sleep apnea from infrared video

Obstructive sleep apnea (OSA) affects nearly one billion people worldwide, yet most cases remain undiagnosed because standard testing requires overnight polysomnography, which is costly and inconvenient. Here we show that SlAction, a contact-free, wall-mounted near-infrared (NIR) video system, can estimate the apnea-hypopnea index (AHI) and detect positional OSA entirely on-device, without attached physiological sensors or cloud-based data transfer. The system was trained and validated on 936 clinical recordings (>5,000 hours) from three hospitals. Motivated by the physiology of respiratory arousals (RA), we identify RA through their associated body movements visible in video as surrogates of breathing disturbances that correlate with apnea severity, enabling real-time analysis with a lightweight deep learning model on low-cost hardware. Across internal and external test sets, SlAction substantially reduces AHI estimation error, improves classification performance for severity and positional OSA, and operates markedly faster than prior video-based approaches. Although evaluated in controlled clinical settings, these findings support the potential of SlAction as a scalable, contact-free, and privacy-preserving approach for OSA screening.