Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Smartphone User Identification Using Accelerometer Data

Eyhab Al-Mahadeen
‡ ,
Mansoor Alghamdi
‡ ORCID logo ,
Ahmad S. Tarawneh
‡ ,
Majed Abdullah Alrowaily
‡ ,
Malek Alrashidi
‡ ORCID logo ,
Ibrahim S. Alkhazi
‡ ,
Almoutaz Mbaidin
‡ ,
Anas Ali Akasasbeh
‡ ,
Mohammad Ali Abbadi
‡ ,
Ahmad B. Hassanat
*,‡
‡
These authors contributed equally to the work.
Version 1 : Received: 26 May 2023 / Approved: 1 June 2023 / Online: 2 June 2023 (09:00:51 CEST)

A peer-reviewed article of this Preprint also exists.

Al-Mahadeen, E.; Alghamdi, M.; Tarawneh, A.S.; Alrowaily, M.A.; Alrashidi, M.; Alkhazi, I.S.; Mbaidin, A.; Alkasasbeh, A.A.; Abbadi, M.A.; Hassanat, A.B. Smartphone User Identification/Authentication Using Accelerometer and Gyroscope Data. Sustainability 2023, 15, 10456. Al-Mahadeen, E.; Alghamdi, M.; Tarawneh, A.S.; Alrowaily, M.A.; Alrashidi, M.; Alkhazi, I.S.; Mbaidin, A.; Alkasasbeh, A.A.; Abbadi, M.A.; Hassanat, A.B. Smartphone User Identification/Authentication Using Accelerometer and Gyroscope Data. Sustainability 2023, 15, 10456.

Abstract

With the growing popularity of smartphones, user identification has become an essential component of maintaining security and privacy. This study investigates how smartphone accelerometer data can be used to identify users, and it makes recommendations for the ideal application parts. Accelerometer data from the HMOG public dataset was used to train deep learning, conventional classifiers, and voting classifiers, which were then utilized to identify users. To enhance performance, feature selection and pre-processing techniques were researched. The results show that RFE feature selection outperforms other approaches and that LSTM followed by XGBoost has the best identification performance as indicated by a relatively large number of machine learning performance measures. The proposed identification system nevertheless performed well and outperformed existing methods, which were principally created and tested on the same HMOG public smartphone dataset, even with a larger number of users. Further work would be necessary for such an application to reach its full potential, though.

Keywords

biometrics; deep learning; time series; feature selection; classification; accelerometer; Sustainability

Subject

Computer Science and Mathematics, Security Systems

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Download PDF

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.

Leave a public comment
Send a private comment to the author(s)
* All users must log in before leaving a comment
Views 0
Downloads 0
Comments 0
Metrics 0
Get PDF Cite Share 0
Bookmark BibSonomy Mendeley Reddit Delicious
×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.