Robust Dynamic Hand Gesture Recognition Based on Millimeter Wave Radar Using Atten-TsNN

Abstract

The millimeter-wave radar can sense the subtle movement of the hand. However, the traditional hand gesture recognition methods do not work well in dynamic interference scenarios. To address this issue, a robust hand gesture recognition method is proposed based on the self-attention time-series neural networks. Firstly, the original radar echo is constructed in terms of frame, sequence, and channel at the input terminal of the network. A one-dimensional time-series neural network is built, and the time-distributed layer is used as the wrapper to extract the feature from each frame sequence independently. Then the self-attention mechanism is employed to assign the adequate weights to the sequence of frames entered in parallel to obtain the inter-frame correlation and suppress the random interference. Finally, the Global AvgPooling layer is used to reduce the number of channels, and the fully connected layer outputs the label of the gesture. The experimental results show that the proposed method can achieve a high recognition rate in the presence of random dynamic interference.