Abstract: Millimeter wave radar can be used for various sensing tasks,such as activity recognition,gesture recognition,heart rate perception.Among them,gesture recognition is a research hotspot,which can realize contactless human-computer interaction.Most existing studies on gesture recognition make use of point cloud or range-Doppler for pattern recognition through neural networks to achieve sensing.However,there are some problems.Firstly,the robustness of these methods is poor.The changes of the user and his/her location affect the received millimeter wave signals,causing the accuracy of the sensing model to reduce.Secondly,these methods input the complete range-Doppler map into the neural network,which makes the model complicated and makes it difficult for the model to focus on the sensing task,because there are many unrelated regions to the sensing task.To solve these problems,this paper first builds the gesture trajectory from multiple continuous frames of point cloud,and then cuts and compresses the multiple continuous range-Doppler maps to obtain the two-dimensional local Doppler map.Finally,the features are extracted from the point cloud trajectory and the two-dimensional local Doppler map respectively by the neural networks,concatenated and classified by a fully-connected neural network.Experiments show that the proposed method focuses on gestures and can achieve a recognition accuracy of 98%,and can achieve a recognition accuracy of 93% for new users and 92% for new locations in the cases of user changes and location changes,better than the state of the art.

Key words: Millimeter wave radar, Gesture recognition, Across scenarios, Location independent, User independent