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.

翻译：摘要：阻塞性睡眠呼吸暂停（OSA）是一种常见的睡眠障碍，影响全球约十亿人口。当前诊断OSA的金标准多导睡眠监测（PSG）需要患者整夜住院并佩戴多个传感器，因首夜效应可能导致诊断不准确。为解决这一问题，本文提出SlAction——一种适用于日常睡眠环境的非侵入式OSA检测系统，采用红外视频。鉴于睡眠视频中运动幅度极小，本研究探讨了一个基本问题：“睡眠期间呼吸事件能否充分反映在人体运动中？”通过分析包含5,098小时的最大睡眠视频数据集，我们建立了OSA事件与睡眠期间人体运动之间的相关性。所提方法采用低帧率（2.5 FPS）、大窗口尺寸（60秒）和滑动步长（30秒）进行滑动窗口分析，以捕捉与OSA相关的缓慢、长时程运动。此外，我们针对资源受限设备设计了轻量级深度神经网络，确保所有视频流在本地处理，不泄露隐私。评估结果显示，SlAction在不同环境下检测OSA的平均F1分数达87.6%。在NVIDIA Jetson Nano上部署后，SlAction可实现实时推理（60秒视频片段约需3秒处理），凸显其在OSA早期诊断与个性化治疗中的应用潜力。