Abstract
While fulfilling communication tasks, wireless signals can also be used to sense the environment. Among various types of sensing media, WiFi signals offer advantages such as widespread availability, low hardware cost, and strong robustness to environmental conditions like light, temperature, and humidity. By analyzing Wi-Fi signals in the environment, it is possible to capture dynamic changes of the human body and accomplish sensing applications such as gesture recognition. Although many existing gesture sensing solutions perform well in-domain but lack cross-domain capabilities (i.e., recognition performance in untrained environments). To address this, we extract Doppler spectra from the channel state information (CSI) received by all receivers and concatenate each Doppler spectrum along the same time axis to generate fused images with multi-angle information as input features. Furthermore, inspired by the convolutional block attention module (CBAM), we propose a gesture recognition network that integrates a multi-semantic spatial attention mechanism with a self-attention-based channel mechanism. This network constructs attention maps to quantify the spatiotemporal features of gestures in images, enabling the extraction of key domain-independent features. Additionally, ResNet18 is employed as the backbone network to further capture deep-level features. To validate the network performance, we evaluate the proposed network on the public Widar3 dataset, and the results show that it not only maintains high in-domain accuracy of 99.72%, but also achieves high performance in cross-domain recognition of 97.61%, significantly outperforming existing best solutions.
Abstract (translated)
在执行通信任务的同时，无线信号也可以用来感知环境。在各种传感媒介中，WiFi信号具有广泛可用性、硬件成本低以及对光照、温度和湿度等环境条件的强健性等特点。通过分析环境中Wi-Fi信号，可以捕捉人体动态变化并实现手势识别等功能。然而，许多现有的手势感应解决方案虽然在特定领域内表现出色，但缺乏跨域能力（即，在未训练环境中进行识别的能力）。为了解决这一问题，我们从信道状态信息(CSI)中提取多接收器接收到的所有数据中的多普勒频谱，并将每个频谱沿同一时间轴连接起来以生成包含多角度信息的融合图像作为输入特征。此外，受卷积块注意模块（CBAM）启发，我们提出了一种集成了多语义空间注意力机制与基于自注意力的通道机制的手势识别网络。该网络构建了注意力图来量化手势在图像中的时空特性，从而提取出关键的领域无关特征。此外，还使用ResNet18作为骨干网络以进一步捕获深层次特征。为了验证网络性能，我们在公开的Widar3数据集上评估了所提出的网络，并且结果表明该方法不仅保持了99.72%的高度域内准确率，还在跨域识别中取得了97.61%的表现，远远超过了现有的最佳解决方案。
URL
https://arxiv.org/abs/2512.04521
