{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T15:28:28Z","timestamp":1780414108583,"version":"3.54.1"},"reference-count":38,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2019,9,19]],"date-time":"2019-09-19T00:00:00Z","timestamp":1568851200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In 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.<\/jats:p>","DOI":"10.3390\/s19184054","type":"journal-article","created":{"date-parts":[[2019,9,20]],"date-time":"2019-09-20T02:51:11Z","timestamp":1568947871000},"page":"4054","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Recurrent Neural Network for Inertial Gait User Recognition in Smartphones"],"prefix":"10.3390","volume":"19","author":[{"given":"Pablo","family":"Fernandez-Lopez","sequence":"first","affiliation":[{"name":"University Group for ID Technologies (GUTI), University Carlos III of Madrid (UC3M), Av. de la Universidad 30, 28911 Leganes, Madrid, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Judith","family":"Liu-Jimenez","sequence":"additional","affiliation":[{"name":"University Group for ID Technologies (GUTI), University Carlos III of Madrid (UC3M), Av. de la Universidad 30, 28911 Leganes, Madrid, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kiyoshi","family":"Kiyokawa","sequence":"additional","affiliation":[{"name":"Cybernetics and Reality Engineering Laboratory (CARE), Nara Institute of Science and Technology (NAIST), 8916-5 Takayama-cho, Ikoma, Nara 630-0192, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Wu","sequence":"additional","affiliation":[{"name":"International Collaborative Laboratory for Robotics Vision, NAIST, 8916-5 Takayama-cho, Ikoma, Nara 630-0192, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4239-985X","authenticated-orcid":false,"given":"Raul","family":"Sanchez-Reillo","sequence":"additional","affiliation":[{"name":"University Group for ID Technologies (GUTI), University Carlos III of Madrid (UC3M), Av. de la Universidad 30, 28911 Leganes, Madrid, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,9,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"101","DOI":"10.3390\/sym9070101","article-title":"Threats of Password Pattern Leakage Using Smartwatch Motion Recognition Sensors","volume":"9","author":"Kim","year":"2017","journal-title":"Symmetry"},{"key":"ref_2","unstructured":"Burmester, M., Tsudik, G., Magliveras, S., and Ili\u0107, I. 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