  This paper presents HandFi, which constructs hand skeletons with practical
WiFi devices. Unlike previous WiFi hand sensing systems that primarily employ
predefined gestures for pattern matching, by constructing the hand skeleton,
HandFi can enable a variety of downstream WiFi-based hand sensing applications
in gaming, healthcare, and smart homes. Deriving the skeleton from WiFi signals
is challenging, especially because the palm is a dominant reflector compared
with fingers. HandFi develops a novel multi-task learning neural network with a
series of customized loss functions to capture the low-level hand information
from WiFi signals. During offline training, HandFi takes raw WiFi signals as
input and uses the leap motion to provide supervision. During online use, only
with commercial WiFi, HandFi is capable of producing 2D hand masks as well as
3D hand poses. We demonstrate that HandFi can serve as a foundation model to
enable developers to build various applications such as finger tracking and
sign language recognition, and outperform existing WiFi-based solutions.
Artifacts can be found: https://github.com/SIJIEJI/HandFi
