Design and Implementation of a Noncontact Sleep Monitoring System Using Infrared Cameras and Motion Sensor

TLDR

Quality sleep is vital for health, and recent advances in sleep monitoring aim to detect disorders by tracking breathing and posture, yet head posture has received little attention. This study presents a novel sleep monitoring system that simultaneously analyzes respiration, head posture, and body posture. The system uses cost‑effective infrared cameras and a Kinect sensor to capture breathing, head, and body motion, automatically extracting respiratory waveforms, tracking head pose with template matching, and classifying body posture via machine‑learning on skeleton data. Experiments demonstrate the system’s feasibility, achieving 96 % accuracy in detecting abnormal breathing and body movements, 87.6 % in head tracking, and over 90 % in classifying body postures.

Abstract

Quality sleep is essential for human health. Recent developments in sleep monitoring techniques show great promise of detecting sleep disorders and improving sleep quality. Two important indicators for sleep disorders are breathing and posture, but existing methods deal with them separately, and hardly any effort has been devoted to head posture. In this paper, we present the design and implementation of a novel sleep monitoring system that simultaneously analyzes respiration, head posture, and body posture. The system consists solely of cost-effective vision-based devices, operating in a quiet and noncontact way with a little disturbance to natural sleep. Specifically, we use an infrared camera to record the sleep process. From the infrared video, the region of breathing movement is automatically determined and the intensity estimated, yielding a waveform indicating respiratory rhythms. Five additional infrared cameras are employed to capture the subject's face from different orientations, and we exploit template matching to perform head tracking. A Kinect motion sensor is also utilized to obtain skeleton description of body posture that is robust to self-occlusion, and after noise being filtered, we apply machine learning techniques for body posture classification. Experimental results show that elementary breathing and posture analysis are feasible based on the information acquired by the proposed system, and high accuracy can be achieved: 96% in recognizing abnormal breathing and body movements, 87.6% in head tracking, and over 90% in classifying most body postures.