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Open AccessArticle

Unobtrusive Sleep Monitoring Using Movement Activity by Video Analysis

1
Graduate Institute of Applied Science and Engineering, Fu Jen Catholic University, New Taipei 24205, Taiwan
2
Electrical Engineering, Fu Jen Catholic University, New Taipei 24205, Taiwan
*
Author to whom correspondence should be addressed.
Electronics 2019, 8(7), 812; https://doi.org/10.3390/electronics8070812
Received: 29 June 2019 / Revised: 14 July 2019 / Accepted: 17 July 2019 / Published: 20 July 2019
(This article belongs to the Special Issue Sensing and Signal Processing in Smart Healthcare)
Sleep healthcare at home is a new research topic that needs to develop new sensors, hardware and algorithms with the consideration of convenience, portability and accuracy. Monitoring sleep behaviors by visual sensors represents one new unobtrusive approach to facilitating sleep monitoring and benefits sleep quality. The challenge of video surveillance for sleep behavior analysis is that we have to tackle bad image illumination issue and large pose variations during sleeping. This paper proposes a robust method for sleep pose analysis with human joints model. The method first tackles the illumination variation issue of infrared videos to improve the image quality and help better feature extraction. Image matching by keypoint features is proposed to detect and track the positions of human joints and build a human model robust to occlusion. Sleep poses are then inferred from joint positions by probabilistic reasoning in order to tolerate occluded joints. Experiments are conducted on the video polysomnography data recorded in sleep laboratory. Sleep pose experiments are given to examine the accuracy of joint detection and tacking, and the accuracy of sleep poses. High accuracy of the experiments demonstrates the validity of the proposed method. View Full-Text
Keywords: sleep pose recognition; keypoints feature matching; Bayesian inference; near-infrared images; scale invariant feature transform sleep pose recognition; keypoints feature matching; Bayesian inference; near-infrared images; scale invariant feature transform
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Wang, Y.-K.; Chen, H.-Y.; Chen, J.-R. Unobtrusive Sleep Monitoring Using Movement Activity by Video Analysis. Electronics 2019, 8, 812.

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