Millimeter wave (mmWave) based gesture recognition technology provides a good human computer interaction (HCI) experience. Prior works focus on the close-range gesture recognition, but fall short in range extension, i.e., they are unable to recognize gestures more than one meter away from considerable noise motions. In this paper, we design a long-range gesture recognition model which utilizes a novel data processing method and a customized artificial Convolutional Neural Network (CNN). Firstly, we break down gestures into multiple reflection points and extract their spatial-temporal features which depict gesture details. Secondly, we design a CNN to learn changing patterns of extracted features respectively and output the recognition result. We thoroughly evaluate our proposed system by implementing on a commodity mmWave radar. Besides, we also provide more extensive assessments to demonstrate that the proposed system is practical in several real-world scenarios.
@article{arxiv.2002.02591,
title = {Long-Range Gesture Recognition Using Millimeter Wave Radar},
author = {Yu Liu and Yuheng Wang and Haipeng Liu and Anfu Zhou and Jianhua Liu and Ning Yang},
journal= {arXiv preprint arXiv:2002.02591},
year = {2020}
}