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Gait Identification Using Hip Joint Movement and Deep Machine Learning

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Intelligent Computing Methodologies (ICIC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13395))

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Abstract

Person identification is a challenging problem which has recently received significant interest mainly due to accelerated advances in sensor technologies and machine learning. It offers potential to support diverse applications that includes crime suspect identification, biometric authentication, and missing person identification. Many existing works involving wearable sensors such as accelerometers and gyroscopes either attach a single sensor to the participants’ (i.e., person) trunk or waist to obtain generic movement data or, rely upon a network of many sensors to obtain a full body movement data. However, it is an unsolved challenge to obtain reliable performance through an individual joint’s motion. In this work, we introduce a gait-based person identification method using a Long Short-Term Memory model trained over the unique statistical features extracted from a single hip joint movement. Experiments are conducted with varying configurations of multiple classification models and validation metrics. Our approach outperformed the existing methods and achieved gait identification accuracy of up to 95.65% when evaluated over the purely unseen data samples. We further introduce a simple filtering method which may increase accuracy up to 100% where larger sequences are provided.

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Authors and Affiliations

  1. Liverpool John Moores University, Liverpool, L3 3AF, UK

    Luke Topham, Wasiq Khan, Dhiya Al-Jumeily & Atif Waraich

  2. University of Sharjah, Sharjah, United Arab Emirates

    Abir Hussain

Authors
  1. Luke Topham
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  2. Wasiq Khan
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  3. Dhiya Al-Jumeily
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  4. Atif Waraich
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  5. Abir Hussain
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Corresponding author

Correspondence to Luke Topham .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Xi’an Polytechnic University, Xi’an, China

    Junfeng Jing

  4. The University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

  5. Polytecnic of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  6. Liverpool John Moores University, Liverpool, UK

    Abir Hussain

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Topham, L., Khan, W., Al-Jumeily, D., Waraich, A., Hussain, A. (2022). Gait Identification Using Hip Joint Movement and Deep Machine Learning. In: Huang, DS., Jo, KH., Jing, J., Premaratne, P., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2022. Lecture Notes in Computer Science(), vol 13395. Springer, Cham. https://doi.org/10.1007/978-3-031-13832-4_19

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  • DOI: https://doi.org/10.1007/978-3-031-13832-4_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-13831-7

  • Online ISBN: 978-3-031-13832-4

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