WiPE: Privacy-friendly WiFi-based Human Pose Estimation on Consumer Platform

With consumer electronics development, consumerelectronic 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 ofWiFi 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 subcarriers ofWiFi 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 floating point operations (FLOPs). Morphological information of the body is also obtained by WiPE.