Adversary Helps: Gradient-based Device-Free Domain-Independent Gesture Recognition

TL;DR

This paper introduces DI, a gradient-based method that effectively eliminates domain gaps in wireless signal-based gesture recognition, significantly improving cross-domain accuracy across multiple classifiers.

Contribution

The paper proposes a novel domain gap elimination technique using gradient sign maps, advancing cross-domain gesture recognition without prior domain adaptation.

Findings

DI achieves over 87% accuracy across classifiers.

Outperforms existing cross-domain recognition solutions.

Effective in ten different gesture and domain scenarios.

Abstract

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.