Abstract
Human pose estimation and action recognition have received attention due to their critical roles in healthcare monitoring, rehabilitation, and assistive technologies. In this study, we proposed a novel architecture named Transformer based Encoder Decoder Network (TED Net) designed for estimating human skeleton poses from WiFi Channel State Information (CSI). TED Net integrates convolutional encoders with transformer based attention mechanisms to capture spatiotemporal features from CSI signals. The estimated skeleton poses were used as input to a customized Directed Graph Neural Network (DGNN) for action recognition. We validated our model on two datasets: a publicly available multi modal dataset for assessing general pose estimation, and a newly collected dataset focused on fall related scenarios involving 20 participants. Experimental results demonstrated that TED Net outperformed existing approaches in pose estimation, and that the DGNN achieves reliable action classification using CSI based skeletons, with performance comparable to RGB based systems. Notably, TED Net maintains robust performance across both fall and non fall cases. These findings highlight the potential of CSI driven human skeleton estimation for effective action recognition, particularly in home environments such as elderly fall detection. In such settings, WiFi signals are often readily available, offering a privacy preserving alternative to vision based methods, which may raise concerns about continuous camera monitoring.
Abstract (translated)
人体姿态估计和动作识别因其在医疗监护、康复及辅助技术中的关键作用而受到了广泛关注。本研究提出了一种名为基于变换器的编码解码网络（TED Net）的新架构，用于从WiFi信道状态信息（CSI）中估算人体骨骼姿势。TED Net结合了卷积编码器与基于变换器的注意力机制，以捕捉来自CSI信号的时空特征。所估计的人体姿态作为输入被送入一种定制化的有向图神经网络（DGNN），用于动作识别。我们在两个数据集上验证了我们的模型：一个是公开可用的多模态数据集，用于评估一般姿势估计；另一个是新收集的数据集，重点关注涉及20名参与者的与跌倒相关的场景。实验结果显示，TED Net在姿态估计方面超越了现有方法，并且DGNN能够使用基于CSI的人体骨骼进行可靠的动作分类，性能与基于RGB的方法相当。值得注意的是，TED Net在跌倒和非跌倒情况下都保持了稳健的性能。这些发现突显了CSI驱动的人体骨架估算在有效动作识别中的潜力，尤其是在如老人跌倒检测这样的家庭环境中。在这种环境下，WiFi信号往往易于获取，并提供了一种隐私保护的方法来替代基于视觉的方法，后者可能因持续摄像监控而引起担忧。
URL
https://arxiv.org/abs/2504.16655
