  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.
