Recent studies have developed simple techniques for monitoring and assessing sleep. However, several issues remain to be solved for example high-cost sensor and algorithm as a home-use device. In this study, we aimed to develop an inexpensive and simple sleep monitoring system using a camera and video processing. Polysomnography (PSG) recordings were performed in six subjects for four consecutive nights. Subjects' body movements were simultaneously recorded by the web camera. Body movement was extracted by video processing from the video data and fi ve parameters were calculated for machine learning. Four sleep stages (WAKE, LIGHT, DEEP and REM) were estimated by applying these fi ve parameters to a support vector machine. The overall estimation accuracy was 70.3 ± 11.3% with the highest accuracy for DEEP (82.8 ± 4.7%) and the lowest for LIGHT (53.0 ± 4.0%) compared with correct sleep stages manually scored on PSG data by a sleep technician. Estimation accuracy for REM sleep was 68.0 ± 6.8%. The kappa was 0.19 ± 0.04 for all subjects. The present non-contact sleep monitoring system showed suffi cient accuracy in sleep stage estimation with REM sleep detection being accomplished. Low-cost computing power of this system can be advantageous for mobile application and modularization into home-device.
Machine Learning ; Methods ; Mobile Applications ; Polysomnography ; Sleep Stages ; Sleep, REM ; Support Vector Machine