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15 pages, 7020 KB  
Technical Note
New Application: A Hand Air Writing System Based on Radar Dual View Sequential Feature Fusion Idea
by Yinan Zhao, Tao Liu, Xiang Feng, Zhanfeng Zhao, Wenqing Cui and Yu Fan
Remote Sens. 2022, 14(20), 5177; https://doi.org/10.3390/rs14205177 - 16 Oct 2022
Cited by 6 | Viewed by 2364
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Doppler Radar: Signal, Data and Applications)
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