SlAction: Non-intrusive, Lightweight Obstructive Sleep Apnea Detection using Infrared Video

TL;DR

SlAction is a non-intrusive, infrared video-based system that detects obstructive sleep apnea in daily environments using lightweight neural networks, achieving high accuracy and real-time performance on resource-limited devices.

Contribution

This work introduces SlAction, the first to leverage infrared sleep videos and lightweight neural networks for non-intrusive, real-time OSA detection in home settings.

Findings

Achieves 87.6% F1 score in OSA detection

Operates in real-time (~3 seconds per 60-second clip) on NVIDIA Jetson Nano

Correlates sleep-related human motions with OSA events

Abstract

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.