3D Human Pose Estimation for Free-from and Moving Activities Using WiFi

TL;DR

GoPose is a novel WiFi-based 3D human pose estimation system that uses reflected signals and deep learning to accurately track body movements without specialized hardware or sensors.

Contribution

The paper introduces a WiFi-based 3D pose estimation method that leverages AoA spectrum and deep learning, enabling environment-independent tracking without sensors.

Findings

Achieves 4.7cm accuracy in various scenarios

Works with unseen activities and NLoS conditions

Uses existing WiFi devices for home-based pose estimation

Abstract

This paper presents GoPose, a 3D skeleton-based human pose estimation system that uses WiFi devices at home. Our system leverages the WiFi signals reflected off the human body for 3D pose estimation. In contrast to prior systems that need specialized hardware or dedicated sensors, our system does not require a user to wear or carry any sensors and can reuse the WiFi devices that already exist in a home environment for mass adoption. To realize such a system, we leverage the 2D AoA spectrum of the signals reflected from the human body and the deep learning techniques. In particular, the 2D AoA spectrum is proposed to locate different parts of the human body as well as to enable environment-independent pose estimation. Deep learning is incorporated to model the complex relationship between the 2D AoA spectrums and the 3D skeletons of the human body for pose tracking. Our evaluation results show GoPose achieves around 4.7cm of accuracy under various scenarios including tracking unseen activities and under NLoS scenarios.