Dynamic Gesture Recognition Model Based on Millimeter-Wave Radar With ResNet-18 and LSTM - Zhang, Yongqiang; Peng, Lixin; Ma, Guilei; Man, Menghua; Liu, Shanghe (Frontiers in Neurorobotics, 2022)
In this paper, a deep learning-based gesture recognition model is proposed for recognizing millimeter-wave radar Radio Frequency (RF) signals. The radar dataset used 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 identifying human intentions and realizing Human-Computer Interaction (HCI). A set of velocity-range Doppler images transformed from the original signal is used as the input of the model. The rich features are extracted through the multi-layer convolutional neural network (ResNet-18), and then the dynamic process is modeled through the Long Short-term Memory NetworkS (LSTM). In the feature input network, the classification and recognition of gestures are finally realized through a Softmax layer. As a whole, the model realizes end-to-end training and obtains 92.55% accuracy on the training set using the public data set.