  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.
