WiFi human gesture recognition based on SSC-SGRU

Yulang He; Zhibiao Zhao; Shanshan Li; Zhen Li

doi:10.1117/12.3030992

19 July 2024 WiFi human gesture recognition based on SSC-SGRU

Yulang He, Zhibiao Zhao, Shanshan Li, Zhen Li

Author Affiliations +

Proceedings Volume 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024); 131810I (2024) https://doi.org/10.1117/12.3030992
Event: Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 2024, Beijing, China

Abstract

The Wi-Fi-based wireless sensing technology has emerged as a research hotspot in the field of perception in recent years, enabling intelligent sensing of human activities and the surrounding environment. However, existing wireless sensing models exhibit a high number of parameters, making real-time perception challenging, especially in scenarios with limited computational resources such as mobile devices. To address this issue, this paper proposes a classification recognition algorithm based on a hybrid approach that combines Spatially Separable Convolution (SSC) and Stacked Gate Recurrent Unit (SGRU). In the shallow layers of the hybrid model, the algorithm utilizes a feature extraction module composed of spatially separable convolution to capture spatial features of human gestures while maintaining the temporal consistency of features. In the deeper layers, SGRU network is employed to learn the spatiotemporal features of gestures. The SGRU consists of two layers, where the output of the first layer serves as the input to the second layer. Through validation on the open-source Widar dataset of human gestures, this paper demonstrates that the proposed SSC-SGRU, when compared to the comprehensive ResNet18 with a reduction of approximately 11.0M parameters, achieves an accuracy improvement of approximately 7.2%.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yulang He, Zhibiao Zhao, Shanshan Li, and Zhen Li "WiFi human gesture recognition based on SSC-SGRU", Proc. SPIE 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 131810I (19 July 2024); https://doi.org/10.1117/12.3030992

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KEYWORDS

Gesture recognition

Data modeling

Education and training

Feature extraction

Machine learning

Performance modeling

Convolution

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