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

Deng, Feiqi; Dong, Jianwu; Wang, Xiangyu; Fang, Ying; Liu, Yu; Yu, Zhaofei; Liu, Jing; Feng, Chen

doi:10.1109/tim.2017.2779358

Cited by 66 publications

(32 citation statements)

References 30 publications

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“…In the applications of adults, Deng et al [10] presented a design and implementation of a novel sleep monitoring system that simultaneously analyzed respiration, head posture, and body posture for adults. For respiration, the region of breathing movement was automatically determined and the intensity estimated, yielding a waveform indicating respiratory rhythms with an accuracy of 96% in recognizing abnormal breathing.…”

Section: Related Workmentioning

confidence: 99%

Respiration Monitoring for Premature Neonates in NICU

et al. 2019

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In this paper, we investigate an automated pipeline to estimate respiration signals from videos for premature infants in neonatal intensive care units (NICUs). Two flow estimation methods, namely the conventional optical flow- and deep learning-based flow estimation methods, were employed and compared to estimate pixel motion vectors between adjacent video frames. The respiratory signal is further extracted via motion factorization. The proposed methods were evaluated by comparing our automated extracted respiration signals to that extracted from chest impedance on videos of five premature infants. The overall average cross-correlation coefficients are 0.70 for the optical flow-based method and 0.74 for the deep flow-based method. The average root mean-squared errors are 6.10 and 4.55 for the optical flow- and the deep flow-based methods, respectively. The experimental results are promising for further investigation and clinical application of the video-based respiration monitoring method for infants in NICUs.

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Section: Related Workmentioning

confidence: 99%

Respiration Monitoring for Premature Neonates in NICU

et al. 2019

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“…Contactless monitoring systems may overcome these issues related to placing sensors on patients and influence the measurand [13]. Mainly, solutions based on the analysis of depth changes of the torso using time-of-flight sensors [14] during breathing, low-power ultra wideband impulse radio radar [15,16], and laser Doppler vibrometers [17,18,19] have been designed and tested. Principal limitations of such solutions are related to the high cost of the instrumentation, need for specialized operators, and, in some cases, a low signal-to-noise ratio.…”

Section: Introductionmentioning

confidence: 99%

Non-Contact Monitoring of Breathing Pattern and Respiratory Rate via RGB Signal Measurement

Massaroni

Presti

Formica

et al. 2019

Sensors

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Among all the vital signs, respiratory rate remains the least measured in several scenarios, mainly due to the intrusiveness of the sensors usually adopted. For this reason, all contactless monitoring systems are gaining increasing attention in this field. In this paper, we present a measuring system for contactless measurement of the respiratory pattern and the extraction of breath-by-breath respiratory rate. The system consists of a laptop’s built-in RGB camera and an algorithm for post-processing of acquired video data. From the recording of the chest movements of a subject, the analysis of the pixel intensity changes yields a waveform indicating respiratory pattern. The proposed system has been tested on 12 volunteers, both males and females seated in front of the webcam, wearing both slim-fit and loose-fit t-shirts. The pressure-drop signal recorded at the level of nostrils with a head-mounted wearable device was used as reference respiratory pattern. The two methods have been compared in terms of mean of absolute error, standard error, and percentage error. Additionally, a Bland–Altman plot was used to investigate the bias between methods. Results show the ability of the system to record accurate values of respiratory rate, with both slim-fit and loose-fit clothing. The measuring system shows better performance on females. Bland–Altman analysis showed a bias of −0.01 breaths · min − 1 , with respiratory rate values between 10 and 43 breaths · min − 1 . Promising performance has been found in the preliminary tests simulating tachypnea.

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“…However, these imaging modalities are expensive, not portable, and is unable to perform long-time nocturnal video recording. More works adopted NIR camera [30,31] and computer vision techniques to extract body movement and chest respiration. The use of a NIR video camera to analyze movement patterns of a sleeping subject promises an attractive alternative.…”

Section: Noncontact Sensors For Sleep Behavior Analysismentioning

confidence: 99%

Unobtrusive Sleep Monitoring Using Movement Activity by Video Analysis

Wang

Chen

2019

Electronics

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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.

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

Cited by 66 publications

References 30 publications

Respiration Monitoring for Premature Neonates in NICU

Respiration Monitoring for Premature Neonates in NICU

Non-Contact Monitoring of Breathing Pattern and Respiratory Rate via RGB Signal Measurement

Unobtrusive Sleep Monitoring Using Movement Activity by Video Analysis

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