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Wi-CSNet: A Spatio-Temporal Model for CSI-Based Human Activity Recognition

Author

Listed:
  • Zhongjian Gao

    (College of Mechanical and Electrical Engineering, Sanming University, Sanming 365004, China
    Key Laboratory of Universities in Fujian Province for Intelligent Control of Equipment, Sanming 365004, China)

  • Ruige Zhang

    (College of Mechanical and Electrical Engineering, Sanming University, Sanming 365004, China
    Key Laboratory of Universities in Fujian Province for Intelligent Control of Equipment, Sanming 365004, China
    Strait Institute of Technology, Sanming University, Sanming 365004, China)

  • Yuwei Cai

    (College of Mechanical and Electrical Engineering, Sanming University, Sanming 365004, China
    Key Laboratory of Universities in Fujian Province for Intelligent Control of Equipment, Sanming 365004, China)

  • Lianhui Zheng

    (New Engineering Industry College, Putian University, Putian 351100, China)

  • Han Yang

    (Practice Division of the Academic Affairs Office, Sanming University, Sanming 365004, China)

  • Yao Li

    (College of Mechanical and Electrical Engineering, Sanming University, Sanming 365004, China
    Key Laboratory of Universities in Fujian Province for Intelligent Control of Equipment, Sanming 365004, China)

Abstract

Human Activity Recognition (HAR) based on Channel State Information (CSI) has attracted considerable attention as a privacy-preserving sensing paradigm. However, CSI-based HAR faces several challenges, including environmental noise, long-range temporal dependencies, and the anisotropic structure of CSI tensors. To address these challenges, this paper presents Wi-CSNet, a lightweight CSI-oriented framework that integrates Discrete Wavelet Transform (DWT) preprocessing, asymmetric-stride convolutions, and a Cross-Scanning State Space Duality (CS-SSD) block derived from Mamba2. DWT preprocessing compresses temporal signals while preserving motion-related trends and reducing input dimensionality. Asymmetric-stride convolutions balance feature scales across heterogeneous CSI dimensions, while the lightweight CS-SSD module captures global dependencies with only a 0.63% parameter overhead. Extensive experiments demonstrate that Wi-CSNet achieves accuracies of 97.81% on HHI, 99.92% on UT-HAR, and 100% on NTU-HAR. These results confirm the effectiveness and robustness of Wi-CSNet for fine-grained CSI-based activity recognition in complex environments.

Suggested Citation

  • Zhongjian Gao & Ruige Zhang & Yuwei Cai & Lianhui Zheng & Han Yang & Yao Li, 2026. "Wi-CSNet: A Spatio-Temporal Model for CSI-Based Human Activity Recognition," Future Internet, MDPI, vol. 18(8), pages 1-15, August.
    • Handle: RePEc:gam:jftint:v:18:y:2026:i:8:p:417-:d:2009998
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