  Wireless signal-based gesture recognition has promoted the developments of VR
game, smart home, etc. However, traditional approaches suffer from the
influence of the domain gap. Low recognition accuracy occurs when the
recognition model is trained in one domain but is used in another domain.
Though some solutions, such as adversarial learning, transfer learning and
body-coordinate velocity profile, have been proposed to achieve cross-domain
recognition, these solutions more or less have flaws. In this paper, we define
the concept of domain gap and then propose a more promising solution, namely
DI, to eliminate domain gap and further achieve domain-independent gesture
recognition. DI leverages the sign map of the gradient map as the domain gap
eliminator to improve the recognition accuracy. We conduct experiments with ten
domains and ten gestures. The experiment results show that DI can achieve the
recognition accuracies of 87.13%, 90.12% and 94.45% on KNN, SVM and CNN, which
outperforms existing solutions.
