Gesture recognition based on millimeter-wave radar with pure self-attention mechanism

In the context of building intelligent control and the internet of everything, remote control of devices through hand gestures for human-computer interaction has gradually become a research hotspot. For this, a gesture recognition method based on pure self-attention mechanism model with millimeter-wave radar as sensor is proposed. Firstly, the time sequence echo data of 13 kinds of gestures from the front-view direction is collected. Then, three-dimension fast Fourier transform (3D-FFT), moving target indication (MTI) and constant false alarm rate (CFAR) detector operations are carried out on the data and fixed type Feature extraction. These features arc introduced into radar feature transformer (RFT) based network. Finally, based on the measured data, the steps of data feature extraction, network training, gesture recognition and so on are completed. The experimental results show that the accuracy rate of the proposed method in the test dataset is 95. 38%. Moreover, it has the characteristics of short metwork training time, low model complexity and good generalization, which provides a new research idea for the existing research. © 2024 Chinese Institute of Electronics. All rights reserved.