WiPE: Privacy-Friendly WiFi-Based Human Pose Estimation on Consumer Platform

With consumer electronics development, consumer-electronic WiFi-based human pose estimation (HPE) has been acknowledged as an emerging and privacy-friendly technology. Since WiFi signals do not directly capture the human image, WiFi offers a suitable solution for various scenarios. Such as security monitoring and privacy-preserving human monitoring. Existing studies have neglected the sparsity in the joint heatmaps of HPE, and also have not effectively utilized the diversity of WiFi signals. As a result, it is difficult to provide an accurate human pose estimation. To overcome the above challenges, we propose a sparse regularization method for WiFi-based HPE (WiPE). Considering the sparse nature of the joint heatmap, we design a sparse regularized neural network to focus output around the joint. Subsequently, through a three-dimensional streaming signal fusion modular, we sufficiently integrate diverse features in sub-carriers of WiFi signals, obtaining key frames. Experiment results on a real establishing system show that our WiPE outperforms state-of-the-art methods. WiPE achieved a PCK@0.2 score of 95.97%, indicating high accuracy. At the same time, our WiPE is more lightweight than other methods. WiPE has 43.14% less floating point operations (FLOPs). Morphological information of the body is also obtained by WiPE.