Title: Current trends in gait analysis for biometric recognition using wearable sensors: a systematic review

Authors: Abdullai Dwumfour; Jamal-Deen Abdulai; Ebenezer Owusu; Godwin B. Akrong; Solomon Mensah

Addresses: School of Physical and Mathematical Sciences, Department of Computer Science, University of Ghana, Accra, Ghana ' School of Physical and Mathematical Sciences, Department of Computer Science, University of Ghana, Accra, Ghana ' School of Physical and Mathematical Sciences, Department of Computer Science, University of Ghana, Accra, Ghana ' School of Information Studies, McGill University, Montreal, Canada ' School of Physical and Mathematical Sciences, Department of Computer Science, University of Ghana, Accra, Ghana

Abstract: This paper systematically reviews recent advances in gait-based biometric recognition using wearable sensors, addressing the growing need for secure authentication technologies. While prior reviews focused on vision-based methods, sensor-based approaches lacked comprehensive insights and systematic methodologies. This study bridges the gap by examining wearable sensing modalities, publicly available gait datasets, and techniques for classification, feature extraction, and feature reduction to enhance recognition accuracy. Six research questions guided the review, retrieving 11,321 studies from databases like IEEE, ScienceDirect, ACM, and Scopus, and were later refined to 90 articles using the PRISMA method. The findings show accelerometers, gyroscopes, and IMUs as the top wearable sensors in gait analysis, with secondary datasets, particularly OU-ISIR, being widely used. Convolutional neural networks (CNNs) emerged as the leading method for classification, feature extraction, and reduction. This research offers insightful observations about state-of-the-art techniques and trends in gait-based biometric recognition through wearable sensors.

Keywords: gait analysis; biometrics; authentication; wearable sensors; gait recognition.

DOI: 10.1504/IJBM.2026.156239

International Journal of Biometrics, 2026 Vol.18 No.5, pp.516 - 545

Received: 20 Mar 2025
Accepted: 10 Aug 2025
Published online: 08 Sep 2026 *

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