A Comparison of Video-based Methods for Neonatal Body Motion Detection

Autor: Zheng Peng, Dennis van de Sande, Ilde Lorato, Xi Long, Rong-Hao Liang, Peter Andriessen, Ward Cottaar, Sander Stuijk, Carola van Pul
Přispěvatelé: Eindhoven MedTech Innovation Center, EngD School AP, School of Med. Physics and Eng. Eindhoven, Medical Image Analysis, Applied Physics and Science Education, Electronic Systems, Signal Processing Systems, Future Everyday, EAISI Health, Center for Care & Cure Technology Eindhoven, Clinical Informatics, EAISI Foundational, EAISI High Tech Systems, Efficient Stream Processing Lab
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022, 3047-3050
STARTPAGE=3047;ENDPAGE=3050;TITLE=44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022
Popis: Preterm infants in a neonatal intensive care unit (NICU) are continuously monitored for their vital signs, such as heart rate and oxygen saturation. Body motion patterns are documented intermittently by clinical observations. Changing motion patterns in preterm infants are associated with maturation and clinical events such as late-onset sepsis and seizures. However, continuous motion monitoring in the NICU setting is not yet performed. Video-based motion monitoring is a promising method due to its non-contact nature and therefore unobtrusiveness. This study aims to determine the feasibility of simple video-based methods for infant body motion detection. We investigated and compared four methods to detect the motion in videos of infants, using two datasets acquired with different types of cameras. The thermal dataset contains 32 hours of annotated videos from 13 infants in open beds. The RGB dataset contains 9 hours of annotated videos from 5 infants in incubators. The compared methods include background substruction (BS), sparse optical flow (SOF), dense optical flow (DOF), and oriented FAST and rotated BRIEF (ORB). The detection performance and computation time were evaluated by the area under receiver operating curves (AUC) and run time. We conducted experiments to detect motion and gross motion respectively. In the thermal dataset, the best performance of both experiments is achieved by BS with mean (standard deviation) AUCs of 0.86 (0.03) and 0.93 (0.03). In the RGB dataset, SOF outperforms the other methods in both experiments with AUCs of 0.82 (0.10) and 0.91 (0.05). All methods are efficient to be integrated into a camera system when using low-resolution thermal cameras.
Databáze: OpenAIRE
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