AdaPose: Towards Cross-Site Device-Free Human Pose Estimation with Commodity WiFi

TL;DR

AdaPose is a domain adaptation algorithm that improves WiFi-based human pose estimation across different environments by aligning domain discrepancies, making it more robust and practical for real-world applications.

Contribution

The paper introduces AdaPose, a novel weakly-supervised domain adaptation method with a Mapping Consistency Loss for WiFi-based pose estimation.

Findings

AdaPose effectively reduces domain shift in WiFi pose estimation.

Experimental results show improved accuracy across different scenes.

The method enhances robustness against environmental dynamics.

Abstract

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 CSI is highly sensitive to environmental variables, and direct application of a pre-trained model to a new environment may yield suboptimal results due to domain shift. In this paper, we proposes a domain adaptation algorithm, AdaPose, designed specifically for weakly-supervised WiFi-based pose estimation. The proposed method aims to identify consistent human poses that are highly resistant to environmental dynamics. To achieve this goal, we introduce a Mapping Consistency Loss that aligns the domain discrepancy of source and target domains based on inner consistency between input and output at the mapping level. We conduct extensive experiments on domain adaptation in two different scenes using our self-collected pose estimation dataset containing WiFi CSI frames. 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.