  Human gesture recognition using millimeter-wave (mmWave) signals provides
attractive applications including smart home and in-car interfaces. While
existing works achieve promising performance under controlled settings,
practical applications are still limited due to the need of intensive data
collection, extra training efforts when adapting to new domains, and poor
performance for real-time recognition. In this paper, we propose DI-Gesture, a
domain-independent and real-time mmWave gesture recognition system.
Specifically, we first derive signal variations corresponding to human gestures
with spatial-temporal processing. To enhance the robustness of the system and
reduce data collecting efforts, we design a data augmentation framework for
mmWave signals based on correlations between signal patterns and gesture
variations. Furthermore, a spatial-temporal gesture segmentation algorithm is
employed for real-time recognition. Extensive experimental results show
DI-Gesture achieves an average accuracy of 97.92\%, 99.18\%, and 98.76\% for
new users, environments, and locations, respectively. We also evaluate
DI-Gesture in challenging scenarios like real-time recognition and sensing at
extreme angles, all of which demonstrate the superior robustness and
effectiveness of our system.
