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WiPD: A Robust Framework for Phase Difference-based Activity Recognition

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Abstract

Using Wi-Fi signals to sense target activity is a promising study field, accounting for convenience concerns. However, it remains challenging to recognize target activity as a way of high-precision and stability due to the multi-path effect in Wi-Fi signals. In this paper, we propose a robust framework named WiPD, for accurate activity recognition based on Wi-Fi phase difference data. Firstly, a novel feature representation mechanism named visualized spectrum matrix (VSM) for Wi-Fi activity recognition is proposed. VSM is generated by performing a Short Time Fourier Transform operation on Wi-Fi phase difference data. Then, we design a neural network with the input type of VSM, namely, WiPD-Net, in which the activity features are extracted by both four convolutional neural network submodules and two WiPD-Block submodules. Experiment results show that our proposed WiPD-Net outperforms the existing baselines on our dataset and one public dataset. In particular, WiPD-Net can reach up to an accuracy of 99.80%, and achieve a good migration performance among five Wi-Fi environments.

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under Grant 61972092; in part by the Research Foundation Plan in Higher Education Institutions of Henan Province under Grant 21A520043; and in part by the Collaborative Innovation Major Project of Zhengzhou under Grant 20XTZX06013.

The authors have no conflicts of interest to declare that are relevant to t he content of this article.

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Authors and Affiliations

  1. School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, China

    Pengsong Duan

  2. School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou, China

    Pengsong Duan, Chen Li & Bo Zhang

  3. Inspur Group, Jinan, China

    Endong Wang

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  1. Pengsong Duan
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  2. Chen Li
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Correspondence to Bo Zhang.

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Duan, P., Li, C., Zhang, B. et al. WiPD: A Robust Framework for Phase Difference-based Activity Recognition. Mobile Netw Appl 27, 2280–2291 (2022). https://doi.org/10.1007/s11036-022-02007-4

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