Multiscale Feature Fusion for Gesture Recognition Using Commodity Millimeter-Wave Radar

Gestures are one of the most natural and intuitive approach for human-computer interaction. Compared with traditional camera-based or wearable sensors-based solutions, gesture recognition using the millimeter wave radar has attracted growing attention for its characteristics of contact-free, privacy-preserving and less environmentdependence. Although there have been many recent studies on hand gesture recognition, the existing hand gesture recognition methods still have recognition accuracy and generalization ability shortcomings in shortrange applications. In this paper, we present a hand gesture recognition method named multiscale feature fusion (MSFF) to accurately identify micro hand gestures. In MSFF, not only the overall action recognition of the palm but also the subtle movements of the fingers are taken into account. Specifically, we adopt hand gesture multiangle Doppler-time and gesture trajectory range-angle map multi-feature fusion to comprehensively extract hand gesture features and fuse high-level deep neural networks to make it pay more attention to subtle finger movements. We evaluate the proposed method using data collected from 10 users and our proposed solution achieves an average recognition accuracy of 99.7%. Extensive experiments on a public mmWave gesture dataset demonstrate the superior effectiveness of the proposed system.