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Recurrent neural network for inertial gait user recognition in smartphones

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dc.affiliation.dptoUC3M. Departamento de TecnologĂ­a ElectrĂłnicaes
dc.affiliation.grupoinvUC3M. Grupo de InvestigaciĂłn: Universitario de TecnologĂ­as de IdentificaciĂłn (GUTI)es
dc.contributor.authorFernandez Lopez, Pablo
dc.contributor.authorLiu Jiménez, Judith
dc.contributor.authorKiyokawa, Kiyoshi
dc.contributor.authorWu, Yang
dc.contributor.authorSanchez-Reillo, Raul
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.date.accessioned2020-05-25T10:06:36Z
dc.date.available2020-05-25T10:06:36Z
dc.date.issued2019-09-19
dc.descriptionThis article belongs to the Special Issue Sensors for Gait Biometricsen
dc.description.abstractIn this article, a gait recognition algorithm is presented based on the information obtained from inertial sensors embedded in a smartphone, in particular, the accelerometers and gyroscopes typically embedded on them. The algorithm processes the signal by extracting gait cycles, which are then fed into a Recurrent Neural Network (RNN) to generate feature vectors. To optimize the accuracy of this algorithm, we apply a random grid hyperparameter selection process followed by a hand-tuning method to reach the final hyperparameter configuration. The different configurations are tested on a public database with 744 users and compared with other algorithms that were previously tested on the same database. After reaching the best-performing configuration for our algorithm, we obtain an equal error rate (EER) of 11.48% when training with only 20% of the users. Even better, when using 70% of the users for training, that value drops to 7.55%. The system manages to improve on state-of-the-art methods, but we believe the algorithm could reach a significantly better performance if it was trained with more visits per user. With a large enough database with several visits per user, the algorithm could improve substantially.en
dc.description.sponsorshipThis work was partially supported by the Spanish National Cybersecurity Institute (INCIBE) under the Grants Program “Excellence of Advanced Cybersecurity Research Teams.” The work of this paper has been partly funded by PREVIEW project, granted by the Spanish Ministry of Economy and Competence, with the code TEC2015-68784-R (MINECO/FEDER). This project is partially funded by Microsoft Research Asiaen
dc.format.extent16
dc.identifier.bibliographicCitationFernandez-Lopez P... [et al.]. Recurrent Neural Network for Inertial Gait User Recognition in Smartphones. Sensors. 2019; 19(18):4054.en
dc.identifier.doihttps://doi.org/10.3390/s19184054
dc.identifier.issn14248220 (ISSN)
dc.identifier.publicationfirstpage1
dc.identifier.publicationissue18
dc.identifier.publicationlastpage16
dc.identifier.publicationtitleSensors (Switzerland)en
dc.identifier.publicationvolume19
dc.identifier.urihttps://hdl.handle.net/10016/30488
dc.identifier.uxxiAR/0000024619
dc.language.isoengen
dc.publisherMDPIen
dc.relation.projectIDGobierno de España. TEC2015-68784-Res
dc.rights© This 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 citeden
dc.rightsAtribución 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subject.ecienciaTelecomunicacioneses
dc.subject.otherBiometricsen
dc.subject.otherGait Recognitionen
dc.subject.otherPattern Recognitionen
dc.subject.otherRecurrent Neural Networken
dc.subject.otherSmartphoneen
dc.titleRecurrent neural network for inertial gait user recognition in smartphonesen
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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