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Gait-Based Identification Using Deep Recurrent Neural Networks and Acceleration Patterns
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 opt...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7729817/ https://www.ncbi.nlm.nih.gov/pubmed/33287142 http://dx.doi.org/10.3390/s20236900 |
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author | Peinado-Contreras, Angel Munoz-Organero, Mario |
author_facet | Peinado-Contreras, Angel Munoz-Organero, Mario |
author_sort | Peinado-Contreras, Angel |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7729817 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-77298172020-12-12 Gait-Based Identification Using Deep Recurrent Neural Networks and Acceleration Patterns Peinado-Contreras, Angel Munoz-Organero, Mario Sensors (Basel) Article 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. MDPI 2020-12-03 /pmc/articles/PMC7729817/ /pubmed/33287142 http://dx.doi.org/10.3390/s20236900 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Peinado-Contreras, Angel Munoz-Organero, Mario Gait-Based Identification Using Deep Recurrent Neural Networks and Acceleration Patterns |
title | Gait-Based Identification Using Deep Recurrent Neural Networks and Acceleration Patterns |
title_full | Gait-Based Identification Using Deep Recurrent Neural Networks and Acceleration Patterns |
title_fullStr | Gait-Based Identification Using Deep Recurrent Neural Networks and Acceleration Patterns |
title_full_unstemmed | Gait-Based Identification Using Deep Recurrent Neural Networks and Acceleration Patterns |
title_short | Gait-Based Identification Using Deep Recurrent Neural Networks and Acceleration Patterns |
title_sort | gait-based identification using deep recurrent neural networks and acceleration patterns |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7729817/ https://www.ncbi.nlm.nih.gov/pubmed/33287142 http://dx.doi.org/10.3390/s20236900 |
work_keys_str_mv | AT peinadocontrerasangel gaitbasedidentificationusingdeeprecurrentneuralnetworksandaccelerationpatterns AT munozorganeromario gaitbasedidentificationusingdeeprecurrentneuralnetworksandaccelerationpatterns |