  Obstructive sleep apnea (OSA) is a prevalent sleep disorder affecting
approximately one billion people world-wide. The current gold standard for
diagnosing OSA, Polysomnography (PSG), involves an overnight hospital stay with
multiple attached sensors, leading to potential inaccuracies due to the
first-night effect. To address this, we present SlAction, a non-intrusive OSA
detection system for daily sleep environments using infrared videos.
Recognizing that sleep videos exhibit minimal motion, this work investigates
the fundamental question: "Are respiratory events adequately reflected in human
motions during sleep?" Analyzing the largest sleep video dataset of 5,098
hours, we establish correlations between OSA events and human motions during
sleep. Our approach uses a low frame rate (2.5 FPS), a large size (60 seconds)
and step (30 seconds) for sliding window analysis to capture slow and long-term
motions related to OSA. Furthermore, we utilize a lightweight deep neural
network for resource-constrained devices, ensuring all video streams are
processed locally without compromising privacy. Evaluations show that SlAction
achieves an average F1 score of 87.6% in detecting OSA across various
environments. Implementing SlAction on NVIDIA Jetson Nano enables real-time
inference (~3 seconds for a 60-second video clip), highlighting its potential
for early detection and personalized treatment of OSA.
