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A Global Correlation Action Recognition Framework Using WiFi Signal-Based DenseNet

  • Research Article-Computer Engineering and Computer Science
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Abstract

Intelligent human perception has promoted the development of Internet of Things, and privacy issues have also emerged. Human action recognition based on WiFi channel state information (CSI) is a new technology, which can realize the perception of human activity at low cost and avoid privacy leakage. Most conventional works recognize some postures with obvious differences in actions. However, they will face challenges in identifying some special actions, e.g., irregular actions and symmetrical actions. WiFi CSI signal has a strong global temporal and spatial correlation, the key question in identifying these special actions is how to combine their global correlation. Most prior work only considered its temporal correlation but ignored the spatial correlation. In this paper, we propose a deep learning framework based on DenseNet that can combine the global temporal and spatial correlation of WiFi CSI simultaneity. Specifically, we completely retain the continuity of the action sample and use DenseNet to mine its global correlation information. We collect action samples in the actual scene, including symmetrical actions that have not been explored before, and evaluate the performance of our proposed approach in different environments. The recognition accuracy of the proposed method exceeds 96% in different scenarios. We also compare with some benchmark methods, and the experimental results show that our proposed approach achieves the best recognition performance, the recognition accuracy of our proposed approach is 2% higher than that of the baseline method.

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Authors and Affiliations

  1. School of Information Science and Technology, Northwest University, Xi’an, 710069, China

    Zhao Jian, Huang Liang, He Chen, Chong Shangwu & Li Xin

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  1. Zhao Jian
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  2. Huang Liang
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  3. He Chen
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  4. Chong Shangwu
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  5. Li Xin
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Correspondence to He Chen.

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Jian, Z., Liang, H., Chen, H. et al. A Global Correlation Action Recognition Framework Using WiFi Signal-Based DenseNet. Arab J Sci Eng 48, 10949–10962 (2023). https://doi.org/10.1007/s13369-023-07918-2

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