Dynamic Gesture Recognition Model Based on Millimeter-Wave Radar With ResNet-18 and LSTM

• Main Authors: Liu, S. (Author), Ma, G. (Author), Man, M. (Author), Peng, L. (Author), Zhang, Y. (Author)
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2022
• Subjects: article; Convolutional neural network; explosion; gesture; gesture recognition; Gesture recognition; Gestures recognition; human; human computer interaction; Human computer interaction; Human-Computer Interaction; long short term memory network; Long short-term memory; Long short-term memory network; LSTM; Memory network; Millimeter waves; millimeter-wave radar; Millimeter-wave radar; Millimetre-wave radar; Model-based OPC; Multilayer neural networks; Multi-layers; Network layers; Radar; Recognition models; residual neural network; Resnet-18; ResNet-18; telecommunication; validity
• Online Access: View Fulltext in Publisher

 In this article, a multi-layer convolutional neural network (ResNet-18) and Long Short-Term Memory Networks (LSTM) model is proposed for dynamic gesture recognition. The Soli dataset is based on the dynamic gesture signals collected by millimeter-wave radar. As a gesture sensor radar, Soli radar has high positional accuracy and can recognize small movements, to achieve the ultimate goal of Human-Computer Interaction (HCI). A set of velocity-range Doppler images transformed from the original signal is used as the input of the model. Especially, ResNet-18 is used to extract deeper spatial features and solve the problem of gradient extinction or gradient explosion. LSTM is used to extract temporal features and solve the problem of long-time dependence. The model was implemented on the Soli dataset for the dynamic gesture recognition experiment, where the accuracy of gesture recognition obtained 92.55%. Finally, compare the model with the traditional methods. The result shows that the model proposed in this paper achieves higher accuracy in dynamic gesture recognition. The validity of the model is verified by experiments. Copyright © 2022 Zhang, Peng, Ma, Man and Liu.