GraSens: A Gabor Residual Anti-aliasing Sensing Framework for Action Recognition using WiFi
This work addresses action recognition for smart living and remote monitoring applications, but it is incremental as it builds on existing WiFi sensing methods with specific improvements.
The paper tackles the problem of WiFi-based human action recognition being affected by environmental changes and sub-carrier variability by proposing GraSens, an end-to-end Gabor residual anti-aliasing sensing network, which outperforms state-of-the-art methods in recognition accuracy on multiple datasets.
WiFi-based human action recognition (HAR) has been regarded as a promising solution in applications such as smart living and remote monitoring due to the pervasive and unobtrusive nature of WiFi signals. However, the efficacy of WiFi signals is prone to be influenced by the change in the ambient environment and varies over different sub-carriers. To remedy this issue, we propose an end-to-end Gabor residual anti-aliasing sensing network (GraSens) to directly recognize the actions using the WiFi signals from the wireless devices in diverse scenarios. In particular, a new Gabor residual block is designed to address the impact of the changing surrounding environment with a focus on learning reliable and robust temporal-frequency representations of WiFi signals. In each block, the Gabor layer is integrated with the anti-aliasing layer in a residual manner to gain the shift-invariant features. Furthermore, fractal temporal and frequency self-attention are proposed in a joint effort to explicitly concentrate on the efficacy of WiFi signals and thus enhance the quality of output features scattered in different subcarriers. Experimental results throughout our wireless-vision action recognition dataset (WVAR) and three public datasets demonstrate that our proposed GraSens scheme outperforms state-of-the-art methods with respect to recognition accuracy.