New Application: A Hand Air Writing System Based on Radar Dual View Sequential Feature Fusion Idea

In recent years, non-contact human-computer interactions have aroused much attention. In this paper, we mainly propose a dual view observation system based on the frontal and side millimeter-wave radars (MWR) to collect echo data of the Air writing digits "0-9", simultaneously. Additionally, we also propose a novel distance approximation method to make the trajectory reconstruction more efficient. To exploit these characteristics of spatial-temporal adjacency in handwriting digits, we propose a novel clustering algorithm, named the constrained density-based spatial clustering of application with noise (CDBSCAN), to remove background noise or clutter. Moreover, we also design a robust gesture segmentation method by using twice-difference and high-low thresholds. In our trials and comparisons, based on the trajectories formulated by echo data series of time-distance and time-velocity of dual views, we present a lightweight-based convolution neural network (CNN) to realize these digits recognition. Experiment results show that our system has a relatively high recognition accuracy, which would provide a feasible application for future human-computer interaction scenarios.