  We present CaptAinGlove, a textile-based, low-power (1.15Watts),
privacy-conscious, real-time on-the-edge (RTE) glove-based solution with a tiny
memory footprint (2MB), designed to recognize hand gestures used for drone
control. We employ lightweight convolutional neural networks as the backbone
models and a hierarchical multimodal fusion to reduce power consumption and
improve accuracy. The system yields an F1-score of 80% for the offline
evaluation of nine classes; eight hand gesture commands and null activity. For
the RTE, we obtained an F1-score of 67% (one user).
