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dc.contributor.authorAguilera Morillo, María del Carmen
dc.contributor.authorAguilera Del Pino, Ana María 
dc.date.accessioned2021-12-13T08:37:26Z
dc.date.available2021-12-13T08:37:26Z
dc.date.issued2019-07
dc.identifier.citationAguilera-Morillo MC, Aguilera AM. Multi-class classification of biomechanical data: A functional LDA approach based on multi-class penalized functional PLS. Statistical Modelling. 2020;20(6):592-616. doi:10.1177/1471082X19871157es_ES
dc.identifier.urihttp://hdl.handle.net/10481/72027
dc.description.abstractA functional linear discriminant analysis approach to classify a set of kinematic data (human movement curves of individuals performing different physical activities) is performed. Kinematic data, usually collected in linear acceleration or angular rotation format, can be identified with functions in a continuous domain (time, percentage of gait cycle, etc.). Since kinematic curves are measured in the same sample of individuals performing different activities, they are a clear example of functional data with repeated measures. On the other hand, the sample curves are observed with noise. Then, a roughness penalty might be necessary in order to provide a smooth estimation of the discriminant functions, which would make them more interpretable. Moreover, because of the infinite dimension of functional data, a reduction dimension technique should be considered. To solve these problems, we propose a multi-class approach for penalized functional partial least squares (FPLS) regression. Then linear discriminant analysis (LDA) will be performed on the estimated FPLS components. This methodology is motivated by two case studies. The first study considers the linear acceleration recorded every two seconds in 30 subjects, related to three different activities (walking, climbing stairs and down stairs). The second study works with the triaxial angular rotation, for each joint, in 51 children when they completed a cycle walking under three conditions (walking, carrying a backpack and pulling a trolley). A simulation study is also developed for comparing the performance of the proposed functional LDA with respect to the corresponding multivariate and non-penalized approaches.es_ES
dc.description.sponsorshipMTM2017-88708-P, Spanish Ministry of Economy and Competitiveness (also supported by the FEDER program) and IJCI-2017-34038, Agencia Estatal de Investigación, Ministerio de Ciencia, Innovación y Universidades.es_ES
dc.language.isoenges_ES
dc.rightsAtribución-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nd/3.0/es/*
dc.subjectfunctional dataes_ES
dc.subjectlinear discriminant analysises_ES
dc.subjectmulti-class classificationes_ES
dc.subjectPLS regressiones_ES
dc.subjectP-spline penaltyes_ES
dc.titleMulti-class classification of biomechanical data: A functional LDA approach based on multi-class penalized functional PLSes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/embargoedAccesses_ES
dc.identifier.doihttps://doi.org/10.1177/1471082X19871157
dc.type.hasVersioninfo:eu-repo/semantics/submittedVersiones_ES


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Except where otherwise noted, this item's license is described as Atribución-SinDerivadas 3.0 España