Abstract
The recognition of human gait states holds paramount importance for disease diagnosis and the formulation of rehabilitation protocols. Compared to traditional methods, Wi-Fi-based gait recognition techniques offer the benefits of non-invasiveness and preservation of privacy. Nevertheless, owing to the rapid transitions in gait states during human walking, prevailing gait recognition methodologies relying on Wi-Fi Channel State Information (CSI) fall short in achieving fine-grained gait state recognition, thereby impeding their applicability in domains with stringent granularity requirements, such as medical care. In this paper, we introduce ID-Gait, a precise gait state recognition system aimed at detecting and characterizing human gait with fine-grained sensing capabilities. ID-Gait first undertakes a broad recognition of the user’s gait state (e.g., fast walking, slow walking, running), and subsequently perform fine-grained state recognition within the gait cycle to monitor the user’s health. Specifically, we initially enhance the Wi-Fi CSI data through quantization of noisy signals, followed by the estimation of gait speed and fine-grained gait state recognition facilitated by the extraction of Doppler Frequency Shift (DFS). Extensive experimentation conducted across diverse scenarios of 5 users demonstrates that the proposed ID-Gait method achieves an average recognition accuracy exceeding \(85\%\).
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Acknowledgements
The work was supported by “Natural Science Foundation of Shandong”(No.ZR2021LZH010), by “National Key Research and Development Program of China” (No.2023YFE020880), by “Scientific Research Leaders Studio of Jinan”( No.2021GXRC081).
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Yao, M. et al. (2025). ID-Gait: Fine-Grained Human Gait State Recognition Using Wi-Fi Signal. In: Cai, Z., Takabi, D., Guo, S., Zou, Y. (eds) Wireless Artificial Intelligent Computing Systems and Applications. WASA 2024. Lecture Notes in Computer Science, vol 14997. Springer, Cham. https://doi.org/10.1007/978-3-031-71464-1_6
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DOI: https://doi.org/10.1007/978-3-031-71464-1_6
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