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.

翻译：以WiFi为基础的人类行动识别(HAR)被认为是在诸如智能生活和远程监测等应用中的一种大有希望的解决办法,因为WiFi信号具有普遍和不受干扰的性质,但是WiFi信号的功效很容易受到环境变化的影响,而且在不同子容器中各不相同。为了解决这个问题,我们提议一个端到端加博剩余反反反反反欺骗感应网络(GraSens)直接承认在不同情况下使用WiFi信号的行动,特别是设计一个新的加博残余区,以应对周围环境变化的影响,重点是学习WiFi信号的可靠和稳健的时频表现。在每个区,Gabor层以残余的方式与反丑化层融合,以获得变换的特性。此外,我们提议了一个边际时间和频率自控网,以明确集中于WiFi信号的功效,从而提高分散在不同子容器中的产出特性的质量。在我们无线-视野行动确认系统中,将结果实验性结果与我们的拟议的Graft-Arst 数据确认方法结合起来。