Attention-Based Gesture Recognition Using Commodity WiFi Devices

The broad spectrum of applications of WiFi sensing technology, such as gait and gesture recognition, has received widespread attention in recent years. Though most WiFi sensing systems may achieve impressive performance, the challenge lies in making good use of the amplitude and phase information of the channel state information (CSI) retrieved from commodity WiFi devices to carry out sensing tasks. To address this issue, we develop an attention-based framework to properly track the importance of amplitude and phase information to adaptively extract distinguishing features related to gestures. Specifically, we first use the CSI ratio instead of the original CSI as the basic signal, which not only eliminates most of the noise, but also contains the complete information of the CSI signal corresponding to human motion. Then, we use the self-attention module to learn the coarse attention weights of amplitude and phase information of the CSI ratio. Moreover, the relation-attention module is used to integrate features to further refine the attention weight. In this way, we proposed a framework that can adaptively learn distinctive feature representations and, thus, facilitate ubiquitous gesture recognition. Extensive experiments demonstrate the effectiveness of method for gesture recognition under various conditions on the open Widar3.0 dataset. The proposed method achieves 99.69% in-domain recognition accuracy, 96.95% cross-location recognition accuracy, and 93.71% cross-orientation recognition accuracy, outperforming the state-of-the-art solutions.