MMHTSR: In-Air Handwriting Trajectory Sensing and Reconstruction Based on mmWave Radar

计算机科学 手势 计算机视觉 人工智能 手势识别 平滑的 卡尔曼滤波器 雷达 弹道 语音识别 电信 物理 天文
作者
Qin Chen,Zongyong Cui,Zheng Zhou,Yu Tian,Zongjie Cao
出处
期刊:IEEE Internet of Things Journal [Institute of Electrical and Electronics Engineers]
卷期号:11 (6): 10069-10083 被引量:1
标识
DOI:10.1109/jiot.2023.3325258
摘要

In-air Handwriting necessitates consistent motion tracking, in contrast to millimeter-wave (mmWave) radar-based simple gesture recognition techniques. However, during long-duration gesture tracking, challenges such as body motion interference and environmental clutter become more pressing. Moreover, due to the lack of a supporting surface in in-air handwriting, slight arm tremors also can result in unsmooth trajectories. To address these challenges, this paper proposes a two-stage processing framework called MMHTSR. In the first stage, the state-space equations are reestablished, and a locally correlated two-dimensional Gaussian process regression algorithm is employed for inter-frame prediction. By incorporating uncertainty estimation, weights are assigned to the next frame data, effectively suppressing interference from non-gestural targets. In the second stage, real-time smoothing and tracking of gesture trajectories are accomplished using a Kalman filter, followed by mapping the trajectories onto the Cartesian coordinate system. Finally, an end-to-end signal processing framework is deployed on a low-cost 60GHz mmWave radar prototype, and gesture trajectory recognition is achieved using deep learning methods. Experimental results demonstrate that MMHTSR can accurately track motion gestures within the range of approximately 5cm~40cm and successfully recognize 30 classes of in-air gesture trajectories, including uppercase letters A-Z and four interactive gesture actions. Furthermore, the proposed framework exhibits robust performance across various scenarios which shows its adaptability.
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