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dc.contributor.authorIscen, A.en_US
dc.contributor.authorWang Y.en_US
dc.contributor.authorDuygulu, P.en_US
dc.contributor.authorHauptmann, A.en_US
dc.date.accessioned2016-02-08T12:12:32Z
dc.date.available2016-02-08T12:12:32Z
dc.date.issued2015en_US
dc.identifier.issn0302-9743
dc.identifier.urihttp://hdl.handle.net/11693/28152
dc.description.abstractDue to increasing hospital costs and traveling time, more and more patients decide to use medical devices at home without traveling to the hospital. However, these devices are not always very straight-forward for usage, and the recent reports show that there are many injuries and even deaths caused by the wrong use of these devices. Since human supervision during every usage is impractical, there is a need for computer vision systems that would recognize actions and detect if the patient has done something wrong. In this paper, we propose to use Snippet Based Trajectory Statistics Histograms descriptor to recognize actions in two medical device usage problems; inhaler device usage and infusion pump usage. Snippet Based Trajectory Statistics Histograms encodes the motion and position statistics of densely extracted trajectories from a video. Our experiments show that by using Snippet Based Trajectory Statistics Histograms technique, we improve the overall performance for both tasks. Additionally, this method does not require heavy computation, and is suitable for real-time systems. © Springer International Publishing Switzerland 2015.en_US
dc.language.isoEnglishen_US
dc.source.titleLecture Notes in Computer Scienceen_US
dc.relation.isversionofhttps://doi.org/10.1007/978-3-319-16220-1_1en_US
dc.subjectAction recognitionen_US
dc.subjectAssisted living systemsen_US
dc.subjectMedical device usageen_US
dc.subjectBiomedical equipmenten_US
dc.subjectComputer visionen_US
dc.subjectGraphic methodsen_US
dc.subjectHospitalsen_US
dc.subjectInteractive computer systemsen_US
dc.subjectMedical computingen_US
dc.subjectMedical problemsen_US
dc.subjectStatisticsen_US
dc.subjectTrajectoriesen_US
dc.subjectAction recognitionen_US
dc.subjectAssisted livingen_US
dc.subjectAssistive technologyen_US
dc.subjectComputer vision systemen_US
dc.subjectHuman supervisionen_US
dc.subjectMedical Devicesen_US
dc.subjectStatistics histogramsen_US
dc.subjectTraveling timeen_US
dc.subjectReal time systemsen_US
dc.titleSnippet based trajectory statistics histograms for assistive technologiesen_US
dc.typeConference Paperen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.citation.spage3en_US
dc.citation.epage16en_US
dc.citation.volumeNumber8928en_US
dc.identifier.doi10.1007/978-3-319-16220-1_1en_US
dc.publisherSpringeren_US


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