Modeling Human Motion for Predicting Usage of Hospital Operating Room

Simple item page
dc.contributor.advisorShah, Shishir Kirit
dc.contributor.committeeMemberGabriel, Edgar
dc.contributor.committeeMemberPrasad, Saurabh
dc.creatorSghir, Ilyes 1990-
dc.date.accessioned2016-08-28T18:17:02Z
dc.date.available2016-08-28T18:17:02Z
dc.date.createdAugust 2014
dc.date.issued2014-08
dc.date.updated2016-08-28T18:17:03Z
dc.description.abstractWe present a system that exploits existing video streams from an hospital operating room (OR) to infer the OR usage state through Bayesian modeling. We define OR states based on common surgical processes that are relevant for assessing OR efficiency. The human motion pattern within the OR is analyzed to ascertain usage states. The system proposed takes advantage of a discriminatively trained part-based human detector as well as a data association algorithm to reconstruct motion trajectories. Human motion patterns are then extracted using kernel density estimation and a Bayesian classifier is used to assess OR usage states during testing. Our model is tested on a large collection of videos and the results show that human motion patterns provide significant discriminative power in understanding usage of an OR.
dc.description.departmentComputer Science, Department of
dc.format.digitalOriginborn digital
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/10657/1458
dc.language.isoeng
dc.rightsThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).
dc.subjectHospital Operating Room
dc.subjectWorkflow Monitoring
dc.subjectPattern recognition
dc.subjectHuman motion
dc.subjectGaussian Kernel Density Estimation
dc.subjectBayesian inference
dc.titleModeling Human Motion for Predicting Usage of Hospital Operating Room
dc.type.dcmiText
dc.type.genreThesis
thesis.degree.collegeCollege of Natural Sciences and Mathematics
thesis.degree.departmentComputer Science, Department of
thesis.degree.disciplineComputer Science
thesis.degree.grantorUniversity of Houston
thesis.degree.levelMasters
thesis.degree.nameMaster of Science

Files

Original bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
SGHIR-THESIS-2014.pdf
Size:
2.38 MB
Format:
Adobe Portable Document Format
Download

License bundle

Now showing 1 - 1 of 1
No Thumbnail Available
Name:
LICENSE.txt
Size:
1.84 KB
Format:
Plain Text
Description:
Download

Collections

Published ETD Collection