Traditional Wi-Fi based action recognition models often utilize only amplitude information from CSI, with phase information simply discarded due to phase error problems. Such a design decision inevitably limits the information utilization, thus the overall performance. These models also strive at high recognition accuracy, invoking complicated model designs with high computational complexity, rendering them unsuitable for resource-constrained applications or swift migration. To address these issues, a novel lightweight action recognition model based on amplitude-phase fusion is proposed in this paper. To achieve maximum fusion benefit, the phase error is first corrected before information extraction. A lightweight multi-data fusion network termed APFNet is then designed and applied to fuse amplitude and phase information before extracting features for action recognition. Extensive experiments confirmed that comparing to schemes utilizing only amplitude information, APFNet can not only adapt to data with a low sampling frequency (30 Hz) but also significantly reduce the computational complexity, while maintaining a high recognition accuracy (96.7%).

基于传统Wi-Fi的动作识别模型通常仅使用来自CSI的幅度信息，由于相位误差问题，相位信息被简单地丢弃。这样的设计决定不可避免地限制了信息的利用，从而限制了整体性能。这些模型还追求高识别精度，调用具有高计算复杂性的复杂模型设计，使其不适合资源受限的应用程序或快速迁移。为了解决这些问题，本文提出了一种基于幅度-相位融合的轻量级动作识别模型。为了获得最大的融合优势，在提取信息之前首先要校正相位误差。然后，设计了一个轻量级的称为APFNet的多数据融合网络，并将其应用于融合幅度和相位信息，然后再提取用于动作识别的特征。大量实验证实，与仅使用幅度信息的方案相比，APFNet不仅可以适应低采样频率（30 Hz）的数据，而且可以显着降低计算复杂性，同时保持较高的识别精度（96.7％）。