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Open AccessArticle

Recurrent Neural Network for Inertial Gait User Recognition in Smartphones

1
University Group for ID Technologies (GUTI), University Carlos III of Madrid (UC3M), Av. de la Universidad 30, 28911 Leganes, Madrid, Spain
2
Cybernetics and Reality Engineering Laboratory (CARE), Nara Institute of Science and Technology (NAIST), 8916-5 Takayama-cho, Ikoma, Nara 630-0192, Japan
3
International Collaborative Laboratory for Robotics Vision, NAIST, 8916-5 Takayama-cho, Ikoma, Nara 630-0192, Japan
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(18), 4054; https://doi.org/10.3390/s19184054
Received: 1 August 2019 / Revised: 5 September 2019 / Accepted: 12 September 2019 / Published: 19 September 2019
(This article belongs to the Special Issue Sensors for Gait Biometrics)
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. View Full-Text
Keywords: Recurrent Neural Network; gait recognition; smartphone; pattern recognition; biometrics Recurrent Neural Network; gait recognition; smartphone; pattern recognition; biometrics
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MDPI and ACS Style

Fernandez-Lopez, P.; Liu-Jimenez, J.; Kiyokawa, K.; Wu, Y.; Sanchez-Reillo, R. Recurrent Neural Network for Inertial Gait User Recognition in Smartphones. Sensors 2019, 19, 4054. https://doi.org/10.3390/s19184054

AMA Style

Fernandez-Lopez P, Liu-Jimenez J, Kiyokawa K, Wu Y, Sanchez-Reillo R. Recurrent Neural Network for Inertial Gait User Recognition in Smartphones. Sensors. 2019; 19(18):4054. https://doi.org/10.3390/s19184054

Chicago/Turabian Style

Fernandez-Lopez, Pablo; Liu-Jimenez, Judith; Kiyokawa, Kiyoshi; Wu, Yang; Sanchez-Reillo, Raul. 2019. "Recurrent Neural Network for Inertial Gait User Recognition in Smartphones" Sensors 19, no. 18: 4054. https://doi.org/10.3390/s19184054

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