Human pose estimation is fundamental to intelligent perception in the Internet of Things (IoT), enabling applications ranging from smart healthcare to human-computer interaction. While WiFi-based methods have gained traction, they often struggle with continuous motion and high computational overhead. This work presents WiFlow, a novel framework for continuous human pose estimation using WiFi signals. Unlike vision-based approaches such as two-dimensional deep residual networks that treat Channel State Information (CSI) as images, WiFlow employs an encoder-decoder architecture. The encoder captures spatio-temporal features of CSI using temporal and asymmetric convolutions, preserving the original sequential structure of signals. It then refines keypoint features of human bodies to be tracked and capture their structural dependencies via axial attention. The decoder subsequently maps the encoded high-dimensional features into keypoint coordinates. Trained on a self-collected dataset of 360,000 synchronized CSI-pose samples from 5 subjects performing continuous sequences of 8 daily activities, WiFlow achieves a Percentage of Correct Keypoints (PCK) of 97.00% at a threshold of 20% (PCK@20) and 99.48% at PCK@50, with a mean per-joint position error of 0.008m. With only 4.82M parameters, WiFlow significantly reduces model complexity and computational cost, establishing a new performance baseline for practical WiFi-based human pose estimation. Our code and datasets are available at https://github.com/DY2434/WiFlow-WiFi-Pose-Estimation-with-Spatio-Temporal-Decoupling.git.

翻译：人体姿态估计是物联网智能感知的基础技术，在智慧医疗到人机交互等应用中具有重要作用。尽管基于WiFi的方法已受到关注，但常面临连续运动处理困难和高计算开销的问题。本文提出WiFlow，一种利用WiFi信号进行连续人体姿态估计的新型框架。与将信道状态信息视为图像的二维深度残差网络等视觉方法不同，WiFlow采用编码器-解码器架构。编码器通过时序卷积与非对称卷积捕获CSI的时空特征，保留信号的原始序列结构；随后通过轴向注意力机制精炼待追踪的人体关键点特征并捕捉其结构依赖性。解码器将编码后的高维特征映射为关键点坐标。在自主采集的数据集（包含5名受试者执行8种日常活动的连续序列，共36万组同步CSI-姿态样本）上训练后，WiFlow在阈值为20%时的正确关键点百分比达到97.00%（PCK@20），PCK@50达到99.48%，平均关节位置误差为0.008米。该模型仅包含482万个参数，显著降低了模型复杂度与计算成本，为实际应用中的基于WiFi的人体姿态估计建立了新的性能基准。代码与数据集已开源：https://github.com/DY2434/WiFlow-WiFi-Pose-Estimation-with-Spatio-Temporal-Decoupling.git。