Radar Gesture Recognition System in Presence of Interference using Self-Attention Neural Network

Abstract

Gesture recognition provides an easy, convenient and intuitive way of remotely controlling several consumer electronics devices such as audio devices, television sets, projector or gaming consoles. In recent years, radar sensors have been shown to be effective sensing modality to sense and recognize fine-grained dynamic finger-gestures in watch or smartphone and thus offers an user-friendly human-computer interface in ultrashort range applications. However, hand-gesture recognition from a farther distance such as to control consumer devices like TV or projector pose challenge particularly arising due to interferences from multiple humans in the field of view. In this paper, we present a novel unguided spatio-Doppler attention mechanism to enable hand-gesture recognition in presence of multiple humans using a low power, compact 60-GHz FMCW radar operated in 500MHz ISM frequency band. The spatio-Doppler mechanism in 2D deep convolutional neural network with long short term memory (2D CNN-LSTM) makes use of the range-Doppler images and range-angle images. We experimentally present the classification accuracy of 94.75% of our proposed system on test dataset using eight gestures, namely wave, push forward, pull, left swipe, right swipe, clockwise rotate, anti-clockwise rotate, cross, in presence of interfering people, such as walking or arbitrary movements.