AdaPose: Toward Cross-Site Device-Free Human Pose Estimation With Commodity WiFi

WiFi-based pose estimation is a technology with great potential for the development of smart homes and metaverse avatar generation. However, current WiFi-based pose estimation methods are predominantly evaluated under controlled laboratory conditions with sophisticated vision models to acquire accurately labeled data. Furthermore, WiFi channel state information (CSI) is highly sensitive to environmental variables, and direct application of a pretrained model to a new environment may yield suboptimal results due to domain shift. In this article, we propose a domain adaptation algorithm, AdaPose, designed specifically for WiFi-based pose estimation. The proposed method aims to identify consistent human poses that are highly resistant to environmental dynamics and WiFi signal noises. To achieve this goal, we introduce instance-wise consistency alignment loss that aligns domain shifts considering instance-wise pose distribution variance, and cross-environment channel enhancement module that enhances WiFi CSI feature representation by emphasizing channel-wise similarity between source and target domains. We conduct extensive experiments on both our self-collected pose estimation data set and a large public MM-Fi data set. The results demonstrate the effectiveness and robustness of AdaPose in eliminating domain shift, thereby facilitating the widespread application of WiFi-based pose estimation in smart cities.