  In this paper, a computer-vision-assisted simulation method is proposed to
address the issue of training dataset acquisition for wireless hand gesture
recognition. In the existing literature, in order to classify gestures via the
wireless channel estimation, massive training samples should be measured in a
consistent environment, consuming significant efforts. In the proposed CASTER
simulator, however, the training dataset can be simulated via existing videos.
Particularly, a gesture is represented by a sequence of snapshots, and the
channel impulse response of each snapshot is calculated via tracing the rays
scattered off a primitive-based hand model. Moreover, CASTER simulator relies
on the existing videos to extract the motion data of gestures. Thus, the
massive measurements of wireless channel can be eliminated. The experiments
demonstrate a 90.8% average classification accuracy of simulation-to-reality
inference.
