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NeuralWiGait: an accurate WiFi-based gait recognition system using hybrid deep learning framework

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Abstract

WiFi-based human authentication systems are garnering substantial attention for its non-intrusiveness, privacy-preserving, and cost-effectiveness. Identity recognition in a WiFi sensing is typically achieved by analyzing the Channel State Information (CSI) that is generated as people walk. However, existing systems largely rely on models that extract an individual feature, leading to suboptimal accuracy. To address this issue, we propose a novel WiFi-based gait recognition system(NeuralWiGait), which authenticates identities by automatically learning the gait features of various users. A data preprocessing scheme is first applied, effectively reducing the signal noise and complexity of the CSI samples. In particular, a new hybrid deep learning framework (WiGaitNet) is used for automatic feature extraction for WiFi-based gait recognition. WiGaitNet integrates a specifically designed convolutional neural network (CNN) with a Bidirectional Gated Recurrent Unit(BiGRU), capable of extracting spatial and temporal features from human gait CSI samples. Subsequently, the concatenated features are fed into a softmax classifier for identification. Experimental results on public datasets (Widar 3.0 and NTU-Fi-HumanID) show that the proposed system achieves an average accuracy of 99%, demonstrating tremendous potential for application.

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Data availability

The study utilized two publicly accessible datasets: WIDAR 3.0 and NTU-FI-HumanID.

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Funding

No funding was received for this study.

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Authors and Affiliations

  1. College of Artificial Intelligence, Southwest University, Chongqing, 400715, China

    Chenlu Wang, Xiaoyi Fu, Ziyi Yang & Shenglin Li

Authors
  1. Chenlu Wang
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  2. Xiaoyi Fu
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  3. Ziyi Yang
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  4. Shenglin Li
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Contributions

All listed authors contributed to the conception and design of the study, the acquisition and analysis of data, and the preparation of the manuscript. All authors reviewed and approved the final manuscript.

Corresponding author

Correspondence to Shenglin Li.

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The authors declare no competing interests.

Ethical approval

This research involved the identification of individuals using publicly available datasets. As the research did not involve direct interaction with human participants and solely used publicly available data, no ethics approval committee was required. The study adhered to ethical standards and did not raise any ethical concerns.

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Wang, C., Fu, X., Yang, Z. et al. NeuralWiGait: an accurate WiFi-based gait recognition system using hybrid deep learning framework. J Supercomput 81, 373 (2025). https://doi.org/10.1007/s11227-024-06878-0

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  • DOI: https://doi.org/10.1007/s11227-024-06878-0

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