Gait-based identification using deep recurrent neural networks and acceleration patterns

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Show simple item record Peinado Contreras, Angel Muñoz Organero, Mario 2021-02-25T11:12:00Z 2021-02-25T11:12:00Z 2020-12-03
dc.identifier.bibliographicCitation Sensors, 20(23), 6900, Dec. 2020, 18 pp.
dc.identifier.issn 1424-8220
dc.description.abstract This manuscript presents an approach to the challenge of biometric identification based on the acceleration patterns generated by a user while walking. The proposed approach uses the data captured by a smartphone's accelerometer and gyroscope sensors while the users perform the gait activity and optimizes the design of a recurrent neural network (RNN) to optimally learn the features that better characterize each individual. The database is composed of 15 users, and the acceleration data provided has a tri-axial format in the X-Y-Z axes. Data are pre-processed to estimate the vertical acceleration (in the direction of the gravity force). A deep recurrent neural network model consisting of LSTM cells divided into several layers and dense output layers is used for user recognition. The precision results obtained by the final architecture are above 97% in most executions. The proposed deep neural network-based architecture is tested in different scenarios to check its efficiency and robustness.
dc.description.sponsorship The research leading to these results has received funding from the "ANALYTICS USING SENSOR DATA FOR FLATCITY"; project TIN2016-77158-C4-1-R (MINECO/ERDF, EU) funded by the Spanish Agencia Estatal de Investigación (AEI) and the European Regional Development Fund (ERDF). This work was also been supported in part by the "ANALISIS EN TIEMPO REAL DE SENSORES SOCIALES Y ESTIMACION DE RECURSOS PARA TRANSPORTE MULTIMODAL BASADA EN APRENDIZAJE PROFUNDO"; project (MaGIST-RALES), funded by the Spanish Agencia Estatal de Investigación (AEI, doi 10.13039/501100011033) under grant PID2019-105221RB-C44.
dc.format.extent 18
dc.language.iso eng
dc.publisher MDPI
dc.rights © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
dc.rights This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license
dc.rights Atribución 3.0 España
dc.subject.other Accelerometry
dc.subject.other Gait
dc.subject.other Walk
dc.subject.other Identification
dc.subject.other Recognition
dc.subject.other Recurrent neural network
dc.subject.other LSTM
dc.subject.other Accuracy
dc.subject.other Smartphone
dc.title Gait-based identification using deep recurrent neural networks and acceleration patterns
dc.type article
dc.subject.eciencia Informática
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. TIN2016-77158-C4-1-R
dc.relation.projectID Gobierno de España. PID2019-105221RB-C44
dc.type.version publishedVersion
dc.identifier.publicationfirstpage 1
dc.identifier.publicationissue 23, 6900
dc.identifier.publicationlastpage 18
dc.identifier.publicationtitle SENSORS
dc.identifier.publicationvolume 20
dc.identifier.uxxi AR/0000026426
dc.contributor.funder Ministerio de Economía y Competitividad (España)
dc.affiliation.dpto UC3M. Departamento de Ingeniería Telemática
dc.affiliation.instituto UC3M. Instituto UC3M - Santander de Big Data
dc.affiliation.grupoinv UC3M. Grupo de Investigación: Aplicaciones y Servicios Telemáticos (GAST)
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