On Detection and Browsing of Sleep Events
Date Issued
2016
Date
2016
Author(s)
Chen, Chao-Ling
Abstract
The purpose of the study was to provide an objective measurement tool for sleep self-examination, and to propose a method for sleep posture recognition of a subject under covering. Polysomnography (PSG) in clinical therapy requires attached devices to obtain bioinformation; however, the attached devices may result in uncomfortable sleeping, and the cost of the examination is expensive. In the study, using a common depth camera device, an unconstrained sleep browsing system has been developed for applying to home scenario. Current methods based on image processing technique in sleep posture recognition, classified sleep postures in the condition of a subject without covering, and were unable to apply to real sleep scenario. Using three-dimensional information of depth image, a method of sleep posture recognition was realized in the condition of a subject under covering. In the study, epoch method was proposed for recording sleep events, and a method of sleep posture recognition based on depth image was proposed for classifying sleep postures. Using a device with multiple sensors, the sleep browsing system detected sleep events from a subject in bed and the surrounding environment. Based on the multiple sensors of the device, including an infrared depth camera, a color camera, and a four-microphone array, three types of sleep events were detected: motion event, lighting event and sound event. From the input of depth image stream and the input of color image stream, background modeling in the system was used to measure body movements and lighting changes, and the three types of signals were quantified simultaneously. When type of signal score was greater than each empirical threshold, the epoch method was triggered for recording independent sleep events, and the recording contained depth images, color images and audio files. The system provided a browsing interface with sleep diagram, presenting the score curves of sleep events and integrated videos. The method of sleep posture recognition classified sleep postures into four classes: left side, right side, supine and stomach, in which left side and right side contained fetus, yearner and log types of sleep postures, supine contained soldier and starfish types of sleep postures, and stomach contained log and freefaller types of sleep postures. In preliminary stage, the depth image capturing an empty bed was transformed into world coordinate for calculating the bed plane. Each depth image capturing a subject was transformed into world coordinate, and the vertical distance of each depth pixel to the bed plane was calculated. From the input distance array of each depth image, the Support Vector Machine (SVM) method was adopted for classifying sleep postures. Experiments for simulating real sleep scenario with three conditions were carried out: without covering condition, blanket covering condition and quilt covering condition. The survey concluded: (1) The sleep browsing system had efficiency and reliability that users browsed the recording of sleep events efficiently, and examined the content of sleep events by watching the videos. (2) The method of sleep posture recognition had a better performance in quilt covering condition than without covering condition and blanket covering condition, because the layer of quilt enhances the features of sleep postures. The study findings may serve as a guide for future research on sleep event detection and sleep posture recognition in home scenario.
Subjects
Event detection
Image sequence analysis
psychophysiological insomnia
sleep browsing
unconstrained sleep detection
Type
thesis
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