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dc.contributor.authorLacey, Gerard
dc.contributor.authorLLorca, D.F.
dc.contributor.authorVilarino, F.
dc.contributor.authorZhou, Z.
dc.date.accessioned2019-10-25T10:26:19Z
dc.date.available2019-10-25T10:26:19Z
dc.date.createdSeptember 2007en
dc.date.issued2007
dc.date.submitted2007en
dc.identifier.citationD.F. Llorca, F. Vilarino, Z. Zhou & G. Lacey, A multi-class SVM classifier ensemble for automatic hand washing quality assessment, British Machine Vision Conference, Warwick, September 2007, 10 pagesen
dc.identifier.otherY
dc.description.abstractHand washing is a critical activity in preventing the spread of infection in health-care environments. Several guidelines recommended a hand washing protocol consisting of six steps that ensure that all areas of the hands are thoroughly cleaned. In this paper, we describe a novel approach that uses a computer vision system to monitor the user’s hands motions in order to ensure that the hand washing guidelines are followed. This work presents two main contributions: a description of a system which delivers robust segmentation of the hands using a combination of colour and motion analysis, and the implementation of a multi-class classification of the hand gestures using a SVM ensemble. The system performance is analysed and compared with human performance, showing an accuracy close to that of human experts.en
dc.format.extent10 pagesen
dc.language.isoenen
dc.rightsYen
dc.subjectHandwashingen
dc.subjectHand hygieneen
dc.subjectComputer visionen
dc.titleA multi-class SVM classifier ensemble for automatic hand washing quality assessmenten
dc.title.alternativeBritish Machine Vision Conferenceen
dc.typeConference Paperen
dc.type.supercollectionscholarly_publicationsen
dc.type.supercollectionrefereed_publicationsen
dc.identifier.peoplefinderurlhttp://people.tcd.ie/gjlacey
dc.identifier.rssinternalid49921
dc.identifier.doihttp://dx.doi.org/10.5244/c.21.40
dc.rights.ecaccessrightsopenAccess
dc.identifier.orcid_id0000-0002-1923-6852
dc.identifier.urihttp://www.bmva.org/bmvc/2007/papers/paper-223.html
dc.identifier.urihttp://hdl.handle.net/2262/89885


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