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dc.contributor.authorHenry, Christopher
dc.date.accessioned2019-01-10T22:07:09Z
dc.date.available2019-01-10T22:07:09Z
dc.date.issued2011
dc.identifier.citationHenry, Christopher, and James F. Peters. "Arthritic hand-finger movement similarity measurements: Tolerance near set approach," Computational and Mathematical Methods in Medicine, article ID 569898, 14 pp., 2011. DOI: 10.1155/2011/569898.en_US
dc.identifier.issn1748-670X
dc.identifier.urihttp://hdl.handle.net/10680/1605
dc.description.abstractThe problem considered in this paper is how to measure the degree of resemblance between nonarthritic and arthritic hand movements during rehabilitation exercise. The solution to this problem stems from recent work on a tolerance space view of digital images and the introduction of image resemblance measures. The motivation for this work is both to quantify and to visualize differences between hand-finger movements in an effort to provide clinicians and physicians with indications of the efficacy of the prescribed rehabilitation exercise. The more recent introduction of tolerance near sets has led to a useful approach for measuring the similarity of sets of objects and their application to the problem of classifying image sequences extracted from videos showing finger-hand movement during rehabilitation exercise. The approach to measuring the resemblance between hand movement images introduced in this paper is based on an application of the well-known Hausdorff distancemeasure and a tolerance nearness measure. The contribution of this paper is an approach to measuring as well as visualizing the degree of separation between images in arthritic and nonarthritic hand-finger motion videos captured during rehabilitation exercise.en_US
dc.description.urihttps://www.hindawi.com/journals/cmmm/2011/569898/en_US
dc.language.isoenen_US
dc.publisherHindawi Publishing Corporationen_US
dc.rightsinfo:eu-repo/semantics/openAccess
dc.titleArthritic hand-finger movement similarity measurements: Tolerance near set approachen_US
dc.typeArticleen_US
dc.rights.licenseThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
dc.identifier.doi10.1155/2011/569898en_US


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