Lu, HP, Eng, H-L, Mandal, B, Chan, DWS and Ng, Y-L (2011) Markerless Video Analysis for Movement Quantification in Pediatric Epilepsy Monitoring. Proceedings of the 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'11). pp. 8275-8278. ISSN 1557-170X

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Abstract

This paper proposes a markerless video analytic system for quantifying body part movements in pediatric epilepsy monitoring. The system utilizes colored pajamas worn by a patient in bed to extract body part movement trajectories, from which various features can be obtained for seizure detection and analysis. Hence, it is non-intrusive and it requires no sensor/marker to be attached to the patient’s body. It takes raw video sequences as input and a simple user-initialization indicates the body parts to be examined. In background/foreground modeling, Gaussian mixture models are employed in conjunction with HSV-based modeling. Body part detection follows a coarse-to-fine paradigm with graphcut-based segmentation. Finally, body part parameters are estimated with domain knowledge guidance. Experimental studies are reported on sequences captured in an Epilepsy Monitoring Unit at a local hospital. The results demonstrate the feasibility of the proposed system in pediatric epilepsy monitoring and seizure detection.

Item Type: Article
Additional Information: © 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords: epilepsy, monitoring, image color analysis, oscillators, pediatrics, trajectory, electroencephalography
Subjects: R Medicine > R Medicine (General)
Divisions: Faculty of Natural Sciences > School of Computing and Maths
Depositing User: Symplectic
Date Deposited: 07 May 2018 13:32
Last Modified: 07 May 2018 13:34
URI: http://eprints.keele.ac.uk/id/eprint/4859

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