DGSCR: Double-Target Gesture Separation and Classification Recognition Based on Deep Learning and Millimeter-Wave Radar

In the field of human–computer interaction, millimeter-wave radar has attracted considerable attention as a contactless and private approach to hand gesture recognition. However, single-target gesture recognition scenes are generally simplistic and not representative of real human–computer interactions. Therefore, this research examines the feasibility of using a single millimeter-wave radar to recognize hand gestures in double-target scenes by combining radar theory with deep learning. First, a dynamic range angle image (DRAI) of the double-target gesture is composed using the weights and DRAIs of two single-target gestures. Thus, a gesture separation network is studied to separate double-target gestures into two single-target gestures. Then, a convolutional neural network and long short-term memory (CNN + LSTM) model is applied to classification recognition. Finally, experiments are conducted to show that the proposed double-target gesture separation and classification recognition (DGSCR) system has a high recognition accuracy for gestures in new environments and positions. The viability of this method is verified using a public dataset. The CNN + LSTM model is validated using test set, which shows that the maximum accuracy across different positions is 99%. Moreover, the average accuracy after separation across the different environments for two targets in staggered arrangements is 93%. Furthermore, when facing unknown gestures in all samples with double targets, the gesture separation network also has good adaptability, with an average accuracy of 81.6%.