A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 10 Issue 6
Jun.  2023

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 11.8, Top 4% (SCI Q1)
    CiteScore: 17.6, Top 3% (Q1)
    Google Scholar h5-index: 77, TOP 5
Turn off MathJax
Article Contents
M. Shang and X. P. Hong, “Recurrent ConFormer for WiFi activity recognition,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 6, pp. 1491–1493, Jun. 2023. doi: 10.1109/JAS.2023.123291
Citation: M. Shang and X. P. Hong, “Recurrent ConFormer for WiFi activity recognition,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 6, pp. 1491–1493, Jun. 2023. doi: 10.1109/JAS.2023.123291

Recurrent ConFormer for WiFi Activity Recognition

doi: 10.1109/JAS.2023.123291
More Information
  • loading
  • 11 Considering the time-sequence characteristics of CSI signals, 1-D convolutional operation (Conv-1D) is used to capture the feature along the temporal dimension.
  • [1]
    F. Wang, J. Feng, Y. Zhao, X. Zhang, S. Zhang, and J. Han, “Joint activity recognition and indoor localization with WiFi fingerprints,” IEEE Access, vol. 7, pp. 80058–80068, 2019.
    [2]
    R. Memmesheimer, N. Theisen, and D. Paulus, “Gimme signals: Discriminative signal encoding for multimodal activity recognition,” in Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., 2020, pp. 10394–10401.
    [3]
    R. Zhang, C. Jiang, S. Wu, Q. Zhou, X. Jing, and J. Mu, “Wi-Fi sensing for joint gesture recognition and human identification from few samples in human-computer interaction,” IEEE J. Sel. Areas Commun., vol. 40, no. 7, pp. 2193–2205, 2022.
    [4]
    K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit, 2016, 770–778.
    [5]
    M. Tan and Q. Le, “Efficientnet: Rethinking model scaling for convolutional neural networks,” in Proc. Int. Conf. Machine Learning, 2019, pp. 6105–6114.
    [6]
    S. Yousefi, H. Narui, S. Dayal, S. Ermon, and S. Valaee, “A survey on behavior recognition using wifi channel state information,” IEEE Commun. Mag., vol. 55, no. 10, pp. 98–104, 2017. doi: 10.1109/MCOM.2017.1700082
    [7]
    W. Meng, X. Chen, W. Cui, and J. Guo, “Wihgr: A robust wifi-based human gesture recognition system via sparse recovery and modified attention-based BGRU,” IEEE Internet Things J., vol. 9, no. 12, pp. 10272–10282, 2021.
    [8]
    Y. Zhang, Y. Zheng, K. Qian, G. Zhang, Y. Liu, C. Wu, and Z. Yang, “Widar3.0: Zero-effort cross-domain gesture recognition with Wi-Fi,” IEEE Trans. Pattern Anal. Mach. Intell, vol. 44, no. 11, pp. 8671–8688, 2021. doi: 10.1109/TPAMI.2021.3105387
    [9]
    S. K. Yadav, S. Sai, A. Gundewar, H. Rathore, K. Tiwari, H. M. Pandey, and M. Mathur, “Csitime: Privacy-preserving human activity recognition using WiFi channel state information,” Neural Networks, vol. 146, pp. 11–21, 2022. doi: 10.1016/j.neunet.2021.11.011
    [10]
    R. Xiao, J. Liu, J. Han, and K. Ren, “OneFi: One-shot recognition for unseen gesture via cots WiFi,” in Proc. Conf. Embed. Networked Sens., 2021, pp. 206–219.
    [11]
    B. Li, W. Cui, W. Wang, L. Zhang, Z. Chen, and M. Wu, “Two-stream convolution augmented transformer for human activity recognition,” in Proc. AAAI Conf. Artificial Intelligence, 2021, vol. 35, no. 1, pp. 286–293.
    [12]
    A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Adv. Neural Inf. Process. Syst., vol. 30, 6000–6010, 2017.
    [13]
    M. Liang and X. Hu, “Recurrent convolutional neural network for object recognition,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2015, pp. 3367–3375.

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(4)  / Tables(2)

    Article Metrics

    Article views (236) PDF downloads(20) Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return