Hand Gesture Echo Based on Millimeter Wave Radar; JUST; China

The millimeter-wave radar has the ability to sense the subtle movement of hand. However, the traditional hand gesture recognition methods are not robust in the scenario with dynamic interference. 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 network. In order to extract the feature from each frame sequence independently, a one-dimensional time-series neural network is built, and the time-distributed layer is used as the wrapper. 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 to 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 25% random dynamic interference.