GraSens: A Gabor Residual Anti-aliasing Sensing Framework for Action Recognition using WiFi

TL;DR

GraSens is a novel WiFi-based action recognition framework that employs Gabor residual blocks and self-attention mechanisms to achieve robust, environment-invariant human activity recognition across diverse scenarios.

Contribution

The paper introduces GraSens, a new end-to-end neural network with Gabor residual blocks and self-attention for improved WiFi-based human action recognition.

Findings

Outperforms state-of-the-art methods in recognition accuracy.

Effective in diverse environmental scenarios.

Robust to changes in ambient conditions.

Abstract

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.