Motion Estimation and Characterization in Premature Newborns Using Long Duration Video Recordings

Cabon, Sandie; Porée, Fabienne; Simon, Antoine; Ugolin, Melissa; Rosec, Olivier; Carrault, Guy; Pladys, Patrick

doi:10.1016/j.irbm.2017.05.003

Cited by 12 publications

(25 citation statements)

References 19 publications

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“…During the preliminary phases of this study in the framework of the Digi-NewB project we were able to evaluate the performance of the use of infrared cameras for motion detection and sleep evaluation. This was done by comparing the evaluations made by Nidcap certified nurses and the observation of videos as well as by comparing the motion curves obtained in automatic detection and the observation of videos ( 23 ). We concluded from these studies that it was possible to have a reliable evaluation of movement and sleep stages if two viewing angles and thus two cameras were used.…”

Section: Methodsmentioning

confidence: 99%

Quiet Sleep Organization of Very Preterm Infants Is Correlated With Postnatal Maturation

Cailleau¹,

Weber

Cabon

et al. 2020

Front. Pediatr.

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Background: Sleep is an important determinant of brain development in preterm infants. Its temporal organization varies with gestational age (GA) and post-menstrual age (PMA) but little is known about how sleep develops in very preterm infants. The objective was to study the correlation between the temporal organization of quiet sleep (QS) and maturation in premature infants without severe complications during their neonatal hospitalization. Methods: Percentage of time spent in QS and average duration of time intervals (ADI) spent in QS were analyzed from a cohort of newborns with no severe complications included in the Digi-NewB prospective, multicentric, observational study in 2017–19. Three groups were analyzed according to GA: Group 1 (27–30 weeks), Group 2 (33–37 weeks), Group 3 (>39 weeks). Two 8-h video recordings were acquired in groups 1 and 2: after birth (T1) and before discharge from hospital (T2). The annotation of the QS phases was performed by analyzing video recordings together with heart rate and respiratory traces thanks to a dedicated software tool of visualization and annotation of multimodal long-time recordings, with a double expert reading. Results are expressed as median (interquartile range, IQR). Correlations were analyzed using a linear mixed model. Results: Five newborns were studied in each group (160 h of recording). Median time spent in QS increased from 13.0% [IQR: 13–20] to 28.8% [IQR: 27–30] and from 17.0% [IQR: 15–21] to 29.6% [IQR: 29.5–31.5] in Group 1 and 2, respectively. Median ADI increased from 54 [IQR: 53–54] to 288 s [IQR: 279–428] and from 90 [IQR: 84–96] to 258 s [IQR: 168–312] in Group 1 and 2. Both groups reach values similar to that of group 3, respectively 28.2% [IQR: 24.5–31.3] and 270 s [IQR: 210–402]. The correlation between PMA and time spent in QS or ADI were, respectively 0.73 ( p < 10 −4 ) and 0.46 ( p = 0.06). Multilinear analysis using temporal organization of QS gave an accurate estimate of PMA ( r 2 = 0.87, p < 0.001). Conclusion: The temporal organization of QS is correlated with PMA in newborns without severe complication. An automated standardized continuous behavioral quantification of QS could be interesting to monitor during the hospitalization stay in neonatal units.

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Section: Methodsmentioning

confidence: 99%

Quiet Sleep Organization of Very Preterm Infants Is Correlated With Postnatal Maturation

Cailleau¹,

Weber

Cabon

et al. 2020

Front. Pediatr.

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“…While detecting motion/non-motion states, the body motion exhibited globally in the ROI could be used as total surface motion. Any presence of surface motion after noise elimination could be considered as a trigger to the presence of body motion [14]. However, as we were considering the classification of 8 perceptual PA categories, global surface motion was not sufficient.…”

Section: Body Motion Trackingmentioning

confidence: 98%

“…When a subject is lying on a bed, tracking PA or body motion has been mostly focused on detecting the presence of motion and non-motion states in temporal dimension [13][14][15]. However, certain health care applications would benefit from detecting more than just motion and non-motion states.…”

Section: Introductionmentioning

confidence: 99%

“…Methodological challenges are associated to the procedure of tracking body motion or PA in videos. Three popular methods have been previously used in the literature: frame differencing, optical flow, and block matching [14]. Frame differencing is used to identify areas with motion, and optical flow and block matching are used to estimate local displacement.…”

Section: Introductionmentioning

confidence: 99%

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Patient’s Body Motion Study Using Multimodal RGBDT Videos

Haque

Kjeldsen²,

Arguissain

et al. 2018

Advances in Visual Computing

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Automatic analysis of body movement to identify physical activity of patients who are at bed rest is crucial for treatment or rehabilitation purposes. Existing methods of physical activity analysis mostly focused on the detection of primitive motion/non-motion states in unimodal video data captured by either RGB or depth or thermal sensor. In this paper, we propose a multimodal visionbased approach to classify body motion of a person lying on a bed. We mimicked a real scenario of 'patient on bed' by recording multimodal video data from healthy volunteers in a hospital room in a neurorehabilitation center. We first defined a taxonomy of possible physical activities based on observations of patients with acquired brain injuries. We then investigated different motion analysis and machine learning approaches to classify physical activities automatically. A multimodal database including RGB, depth and thermal videos was collected and annotated with eight predefined physical activities. Experimental results show that we can achieve moderately high accuracy (77.68%) to classify physical activities by tracking the body motion using an optical flow-based approach. To the best of our knowledge this is the first multimodal RGBDT video analysis for such application.

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“…Moreover, video modality can be used for high-level analysis and proved to be relevant in several clinical applications in pediatrics, particularly with motion analysis [10]. Regarding clinical applications involving preterm newborns, video-based motion analysis has been investigated for instance for early cerebral palsy detection [11], estimation of sleep stages [12], or maturation characterization [13]. The latter is motivated by the fact that the motor activity evolves along with the age of the newborns [14].…”

Section: Introductionmentioning

confidence: 99%

Preterm Newborn Presence Detection in Incubator and Open Bed Using Deep Transfer Learning

Weber

Cabon

Simon

et al. 2021

IEEE J. Biomed. Health Inform.

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Video-based motion analysis recently appeared to be a promising approach in neonatal intensive care units for monitoring the state of preterm newborns since it is contact-less and noninvasive. However it is important to remove periods when the newborn is absent or an adult is present from the analysis. In this paper, we propose a method for automatic detection of preterm newborn presence in incubator and open bed. We learn a specific model for each bed type as the camera placement differs a lot and the encountered situations are different between both. We break the problem down into two binary classifications based on deep transfer learning that are fused afterwards: newborn presence detection on the one hand and adult presence detection on the other hand. Moreover, we adopt a strategy of decision intervals fusion in order to take advantage of temporal consistency. We test three deep neural network that were pre-trained on ImageNet: VGG16, MobileNetV2 and InceptionV3. Two classifiers are compared: support vector machine and a small neural network. Our experiments are conducted on a database of 120 newborns. The whole method is evaluated on a subset of 25 newborns including 66 days of video recordings. In incubator, we reach a balanced accuracy of 86%. In open bed, the performance is lower because of a much wider variety of situations whereas less data are available.

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Motion Estimation and Characterization in Premature Newborns Using Long Duration Video Recordings

Cited by 12 publications

References 19 publications

Quiet Sleep Organization of Very Preterm Infants Is Correlated With Postnatal Maturation

Quiet Sleep Organization of Very Preterm Infants Is Correlated With Postnatal Maturation

Patient’s Body Motion Study Using Multimodal RGBDT Videos

Preterm Newborn Presence Detection in Incubator and Open Bed Using Deep Transfer Learning

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