Lightweight Midrange Arm-Gesture Recognition System From mmWave Radar Point Clouds

Abstract

This article considers arm-gesture recognition in the form of a point cloud in mid-range scenarios. A 2-D steer vector is established according to the structure of the radar array antenna, and the average power of the array is added to the point cloud image as a feature. In the data processing stage, the proposed system aggregates multiple measurements on all frames, overcoming the issues of the spatial sparsity and ambiguous temporal variation of the point cloud image of the millimeter-wave (mmWave) radar, as a new arm-gesture point cloud image. In the arm-gesture recognition stage, a lightweight PointNet-based classifier is custom-designed to recognize and classify arm-gestures point cloud images. Extensive experiments are carried out on custom-built radar gesture datasets with eight human subjects performing 19 arm-gestures. The experimental results demonstrate that the proposed arm-gesture recognition system can accurately distinguish 19 different arm-gestures with an average accuracy of 99.69% in two indoor environments and outperform five related arm-gesture classifiers. We further analyze the effect of the scenarios and distance on recognition performance.