{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T05:06:11Z","timestamp":1787029571291,"version":"build-2736575974"},"reference-count":26,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2020,12,3]],"date-time":"2020-12-03T00:00:00Z","timestamp":1606953600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100011033","name":"Agencia Estatal de investigaci\u00f3n","doi-asserted-by":"publisher","award":["TIN2016-77158-C4-1-R"],"award-info":[{"award-number":["TIN2016-77158-C4-1-R"]}],"id":[{"id":"10.13039\/501100011033","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100011033","name":"Agencia Estatal de Investigaci\u00f3n","doi-asserted-by":"publisher","award":["PID2019-105221RB-C44"],"award-info":[{"award-number":["PID2019-105221RB-C44"]}],"id":[{"id":"10.13039\/501100011033","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2019s 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.<\/jats:p>","DOI":"10.3390\/s20236900","type":"journal-article","created":{"date-parts":[[2020,12,3]],"date-time":"2020-12-03T11:15:43Z","timestamp":1606994143000},"page":"6900","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Gait-Based Identification Using Deep Recurrent Neural Networks and Acceleration Patterns"],"prefix":"10.3390","volume":"20","author":[{"given":"Angel","family":"Peinado-Contreras","sequence":"first","affiliation":[{"name":"School of Engineering, Universidad Carlos III de Madrid, 28911 Legan\u00e9s, Madrid, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4199-2002","authenticated-orcid":false,"given":"Mario","family":"Munoz-Organero","sequence":"additional","affiliation":[{"name":"Department of Telematic Engineering and UC3M-BS Institute of Financial Big Data, Universidad Carlos III de Madrid, 28911 Legan\u00e9s, Madrid, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,3]]},"reference":[{"key":"ref_1","unstructured":"Simon, K. 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