Vision Transformer with Convolutional Encoder–Decoder for Hand Gesture Recognition using 24-GHz Doppler Radar

Transformers combined with convolutional encoders have been recently used for hand gesture recognition (HGR) using micro-Doppler signatures. In this letter, we propose a vision-transformer-based architecture for HGR with multiantenna continuous-wave Doppler radar receivers. The proposed architecture consists of three modules: 1) a convolutional encoder–decoder, 2) an attention module with three transformer layers, and 3) a multilayer perceptron. The novel convolutional decoder helps to feed patches with larger sizes to the attention module for improved feature extraction. Experimental results obtained with a dataset corresponding to a two-antenna continuous-wave Doppler radar receiver operating at 24 GHz (published by Skaria et al.) confirm that the proposed architecture achieves an accuracy of 98.3% which substantially surpasses the state-of-the-art on the used dataset.