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dc.contributor.authorOrtells Lorenzo, Javier
dc.contributor.authorHerrero Ezquerro, María Trinidad
dc.contributor.authorMollineda, Ramón A.
dc.date.accessioned2018-05-29T09:25:06Z
dc.date.available2018-05-29T09:25:06Z
dc.date.issued2018
dc.identifier.citationOrtells, J., Herrero-Ezquerro, M.T. & Mollineda, R.A. Med Biol Eng Comput (2018). https://doi.org/10.1007/s11517-018-1795-2ca_CA
dc.identifier.issn0140-0118
dc.identifier.issn1741-0444
dc.identifier.urihttp://hdl.handle.net/10234/174854
dc.description.abstractGait is a firsthand reflection of health condition. This belief has inspired recent research efforts to automate the analysis of pathological gait, in order to assist physicians in decision-making. However, most of these efforts rely on gait descriptions which are difficult to understand by humans, or on sensing technologies hardly available in ambulatory services. This paper proposes a number of semantic and normalized gait features computed from a single video acquired by a low-cost sensor. Far from being conventional spatio-temporal descriptors, features are aimed at quantifying gait impairment, such as gait asymmetry from several perspectives or falling risk. They were designed to be invariant to frame rate and image size, allowing cross-platform comparisons. Experiments were formulated in terms of two databases. A well-known generalpurpose gait dataset is used to establish normal references for features, while a new database, introduced in this work, provides samples under eight different walking styles: one normal and seven impaired patterns. A number of statistical studies were carried out to prove the sensitivity of features at measuring the expected pathologies, providing enough evidence about their accuracy.ca_CA
dc.format.extent13 p.ca_CA
dc.format.mimetypeapplication/pdfca_CA
dc.language.isoengca_CA
dc.publisherSpringer Verlagca_CA
dc.relation.isPartOfMed Biol Eng Comput (2018)ca_CA
dc.rights“This is a post-peer-review, pre-copyedit version of an article published in Medical and Biological Engineering and Computing. The final authenticated version is available online at: http://dx.doi.org/10.1007/s11517-018-1795-2.” © International Federation for Medical and Biological Engineering 2018ca_CA
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/*
dc.subjectGait impairmentca_CA
dc.subjectVideo-based gait analysisca_CA
dc.subjectGait databaseca_CA
dc.subjectComputer-aided diagnosisca_CA
dc.titleVision-based gait impairment analysis for aided diagnosisca_CA
dc.typeinfo:eu-repo/semantics/articleca_CA
dc.identifier.doihttps://doi.org/10.1007/s11517-018-1795-2
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_CA
dc.relation.publisherVersionhttps://link.springer.com/article/10.1007/s11517-018-1795-2ca_CA
dc.date.embargoEndDate2019-02-13
dc.type.versioninfo:eu-repo/semantics/acceptedVersionca_CA


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