IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i13p2283-d851737.html
   My bibliography  Save this article

User Authentication by Gait Data from Smartphone Sensors Using Hybrid Deep Learning Network

Author

Listed:
  • Qian Cao

    (School of E-Business and Logistic, Beijing Technology and Business University, Beijing 100048, China
    National Engineering Laboratory for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China)

  • Fei Xu

    (School of E-Business and Logistic, Beijing Technology and Business University, Beijing 100048, China
    National Engineering Laboratory for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China)

  • Huiyong Li

    (School of Computer Science and Engineering, Beihang University, Beijing 100191, China)

Abstract

User authentication and verification by gait data based on smartphones’ inertial sensors has gradually attracted increasing attention due to their compact size, portability and affordability. However, the existing approaches often require users to walk on a specific road at a normal walking speed to improve recognition accuracy. In order to recognize gaits under unconstrained conditions on where and how users walk, we proposed a Hybrid Deep Learning Network (HDLN), which combined the advantages of a long short-term memory (LSTM) network and a convolutional neural network (CNN) to reliably extract discriminative features from complex smartphone inertial data. The convergence layer of HDLN was optimized through a spatial pyramid pooling and attention mechanism. The former ensured that the gait features were extracted from more dimensions, and the latter ensured that only important gait information was processed while ignoring unimportant data. Furthermore, we developed an APP that can achieve real-time gait recognition. The experimental results showed that HDLN achieved better performance improvements than CNN, LSTM, DeepConvLSTM and CNN+LSTM by 1.9%, 2.8%, 2.0% and 1.3%, respectively. Furthermore, the experimental results indicated our model’s high scalability and strong suitability in real application scenes.

Suggested Citation

  • Qian Cao & Fei Xu & Huiyong Li, 2022. "User Authentication by Gait Data from Smartphone Sensors Using Hybrid Deep Learning Network," Mathematics, MDPI, vol. 10(13), pages 1-17, June.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:13:p:2283-:d:851737
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/13/2283/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/10/13/2283/
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Nannan Xu & Xinze Cui & Xin Wang & Wei Zhang & Tianyu Zhao, 2022. "An Intelligent Athlete Signal Processing Methodology for Balance Control Ability Assessment with Multi-Headed Self-Attention Mechanism," Mathematics, MDPI, vol. 10(15), pages 1-16, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:13:p:2283-:d:851737. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.