  Our hands serve as a fundamental means of interaction with the world around
us. Therefore, understanding hand poses and interaction context is critical for
human-computer interaction. We present EchoWrist, a low-power wristband that
continuously estimates 3D hand pose and recognizes hand-object interactions
using active acoustic sensing. EchoWrist is equipped with two speakers emitting
inaudible sound waves toward the hand. These sound waves interact with the hand
and its surroundings through reflections and diffractions, carrying rich
information about the hand's shape and the objects it interacts with. The
information captured by the two microphones goes through a deep learning
inference system that recovers hand poses and identifies various everyday hand
activities. Results from the two 12-participant user studies show that
EchoWrist is effective and efficient at tracking 3D hand poses and recognizing
hand-object interactions. Operating at 57.9mW, EchoWrist is able to
continuously reconstruct 20 3D hand joints with MJEDE of 4.81mm and recognize
12 naturalistic hand-object interactions with 97.6% accuracy.
