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

TL;DR

This paper introduces a vision-transformer-based architecture with a convolutional encoder-decoder for hand gesture recognition using 24 GHz Doppler radar, achieving high accuracy and surpassing existing methods.

Contribution

The paper presents a novel combination of convolutional encoder-decoder and transformer modules specifically designed for radar-based hand gesture recognition.

Findings

Achieved 98.3% accuracy on the dataset.

Outperformed existing state-of-the-art methods.

Validated effectiveness of the convolutional decoder in feature extraction.

Abstract

Transformers combined with convolutional encoders have been recently used for hand gesture recognition (HGR) using micro-Doppler signatures. We propose a vision-transformer-based architecture for HGR with multi-antenna continuous-wave Doppler radar receivers. The proposed architecture consists of three modules: a convolutional encoderdecoder, an attention module with three transformer layers, and a multi-layer 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.