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dc.contributor.authorBrull Mesanza, Asier ORCID
dc.contributor.authorLucas Hernáez, Sergio
dc.contributor.authorZubizarreta Pico, Asier ORCID
dc.contributor.authorPortillo Pérez, Eva
dc.contributor.authorCabanes Axpe, Itziar
dc.contributor.authorRodríguez Larrad, Ana
dc.date.accessioned2024-06-10T14:47:41Z
dc.date.available2024-06-10T14:47:41Z
dc.date.issued2020-11-24
dc.identifier.citationIEEE Access 8 : 210023-210034 (2020)es_ES
dc.identifier.issn2169-3536
dc.identifier.urihttp://hdl.handle.net/10810/68390
dc.description.abstract[EN] In recent years, interest in monitoring Physical Activity (PA) has increased due to its positive effect on health. New technological devices have been proposed for this purpose, mainly focused on sports, which include Machine Learning algorithms to identify the type of PA being performed. However, PA monitoring can also provide data useful for assessing the recovery process of people with impaired lower-limbs. In this work, a Machine-Learning based Physical Activity classifier design procedure is proposed, which makes use of the data provided by a Sensorized Tip that can be adapted to different Assistive Devices for Walking (ADW) such as canes or crutches. The procedure is based on three main stages: 1) defining a wide set of potential features to perform the classification; 2) optimizing the number of features by a Random-Forest approach, detecting the most relevant ones to classify five relevant activities (walking at a normal pace, walking fast, standing still, going up stairs and going down stairs); 3) training the ML-based classifiers considering the optimized feature set. A comparative analysis is carried out to evaluate the proposed procedure, using three ML-based classifier (Support Vector Machines, K-Nearest Neighbour and Artificial Neural Networks), demonstrating that the proposed approach can provide very high success rates if proper feature selection is carried out. This work presents four relevant contributions to the PA monitoring area: 1) the approach is focused on people that require ADW, which are not considered in other approaches; 2) an analysis of the features to characterize gait in people that require ADW is carried out; 3) a design procedure to optimize the number of features using a Random-Forest approach is used, avoiding a typical “brute force” procedure; and 4) a comparative analysis is carried out to demonstrate the validity of the approach.es_ES
dc.description.sponsorshipThis work was supported in part by the University of the Basque Country [University of the Basque Country (UPV/EHU)] under Grant PIF18/067, in part by the UPV/EHU under Project GIU19/045 (GV/EJ IT1381-19), and in part by the Ministerio de Ciencia e Innovación (MCI) under Grant DPI2017-82694-R (AEI/FEDER, UE).es_ES
dc.language.isoenges_ES
dc.publisherIEEEes_ES
dc.relationinfo:eu-repo/grantAgreement/MCIN/DPI2017-82694-Res_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectmonitoringes_ES
dc.subjectfeature extractiones_ES
dc.subjectlegged locomotiones_ES
dc.subjectstairses_ES
dc.subjectperformance evaluationes_ES
dc.subjectforcees_ES
dc.subjectsupport vector machineses_ES
dc.subjectinstrumented crutches_ES
dc.subjectrehabilitationes_ES
dc.subjectmachine learninges_ES
dc.subjectphysical activity classificationes_ES
dc.subjectrandom forestes_ES
dc.subjectartificial neural networkes_ES
dc.subjectsupport vector machinees_ES
dc.subjectk-nearest neighbores_ES
dc.titleA Machine Learning Approach to Perform Physical Activity Classification Using a Sensorized Crutch Tipes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.holder(cc) 2020 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License.es_ES
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/9269370es_ES
dc.identifier.doi10.1109/ACCESS.2020.3039885
dc.departamentoesIngeniería de sistemas y automáticaes_ES
dc.departamentoeuSistemen ingeniaritza eta automatikaes_ES


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(cc) 2020 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License.
Except where otherwise noted, this item's license is described as (cc) 2020 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License.