Article
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Preserved in Portico This version is not peer-reviewed
TRANS-CNN Based Gesture Recognition for mmWave Radar
Version 1
: Received: 4 February 2024 / Approved: 5 February 2024 / Online: 5 February 2024 (07:30:29 CET)
A peer-reviewed article of this Preprint also exists.
Zhang, H.; Liu, K.; Zhang, Y.; Lin, J. TRANS-CNN-Based Gesture Recognition for mmWave Radar. Sensors 2024, 24, 1800. Zhang, H.; Liu, K.; Zhang, Y.; Lin, J. TRANS-CNN-Based Gesture Recognition for mmWave Radar. Sensors 2024, 24, 1800.
Abstract
In order to improve real-time performance of gesture recognition by micro-Doppler map of mmWave Radar, the point cloud based gesture recognition for mmWave Radar is proposed in this paper.Two steps are carried out for mmWave Radar based gesture recognition. The first step is to estimate the point cloud of the gestures by 3D-FFT and the peak grouping. The second step is to train the TRANS-CNN model by combining the multi-head self-attention and the 1D-convolutional network, so as to extract the features in the point cloud data at a deeper level to categorize the gestures. In the experiments, TI mmWave Radar sensor IWR1642 is used as benchmark to evaluate the feasibility of the proposed approach. The results show that the accuracy of the gesture recognition reaches 98.5%. In order to prove the effectiveness of our approach, a simply 2Tx2Rx Radar sensor is developed in our lab and the accuracy of recognition reaches 97.1%. The results show that our proposed gesture recognition approach achieves the best performance in real-time with limited training data, in comparison with the existing methods.
Keywords
mmWave Radar; Dynamic Gesture Recognition; Multi-Head Self-Attention Mechanism; TRANS-CNN; Point Cloud
Subject
Engineering, Electrical and Electronic Engineering
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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