Wi-Fi-Based Human Fall and Activity Recognition Using Transformer-Based Encoder–Decoder and Graph Neural Networks

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 Wi-Fi channel state information (CSI). The 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 multimodal dataset for assessing general pose estimation, and a newly collected dataset focused on fall-related scenarios involving 20 participants. Experimental results demonstrated that the 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, the TED-Net maintains robust performance across both fall and nonfall 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, Wi-Fi signals are often readily available, offering a privacy-preserving alternative to vision-based methods, which may raise concerns about continuous camera monitoring.