Abstract
WiFi-based perception systems can realize various gesture recognition in theory, but they cannot realize large-scale applications in practice. Later, some work solved the problem of cross-domain identification of the WiFi system, and promoted the possibility of the practical application of WiFi perception. However, the existing cross-domain recognition work requires a large number of calculations to extract motion features and recognition through a complex network, which determines that it cannot be deployed directly on edge devices. In addition, some hardware limitations of edge devices (for example, the network card is a single antenna), the amount of data we obtain is far less than that of the general network card. If the original data is not calibrated, the error information carried by the data will have a huge impact on the recognition result. Therefore, in order to solve the above problems, we propose WiRD, a system that can accurately calibrate the amplitude and phase in the case of a single antenna, and can be deployed on edge devices to achieve real-time detection. Experimental results show that WiRD is comparable to existing methods for gesture and body recognition within the domain, and has 87% accuracy for gesture recognition cross the domain, but the overall system processing time is reduced by 9\(\times \) and the model inference time is reduced by 50\(\times \).
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Acknowledgment
This work was supported in part by International Cooperation Project of Shaanxi Province (No. 2020KW-004), the China Postdoctoral Science Foundation (No. 2017M613187), and the Shaanxi Science and Technology Innovation Team Support Project under grant agreement (No. 2018TD-026).
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Yang, Q., Xing, T., Jiang, Z., Wang, J., He, J. (2022). WiRD: Real-Time and Cross Domain Detection System on Edge Device. In: Lai, Y., Wang, T., Jiang, M., Xu, G., Liang, W., Castiglione, A. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2021. Lecture Notes in Computer Science(), vol 13156. Springer, Cham. https://doi.org/10.1007/978-3-030-95388-1_23
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