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Transformers combined with convolutional encoders have been recently used forhand gesture recognition (HGR) using micro-Doppler signatures. We propose avision-transformer-based architecture for HGR with multi-antennacontinuous-wave Doppler radar receivers. The proposed architecture consists ofthree modules: a convolutional encoderdecoder, an attention module with threetransformer layers, and a multi-layer perceptron. The novel convolutionaldecoder helps to feed patches with larger sizes to the attention module forimproved feature extraction. Experimental results obtained with a datasetcorresponding to a two-antenna continuous-wave Doppler radar receiver operatingat 24 GHz (published by Skaria et al.) confirm that the proposed architectureachieves an accuracy of 98.3% which substantially surpasses thestate-of-the-art on the used dataset.

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