Gesture recognition is a pivotal technology in the realm of intelligent education, and millimeter-wave (mmWave) signals possess advantages such as high resolution and strong penetration capability. This paper introduces a highly accurate and robust gesture recognition method using mmWave radar. The method involves capturing the raw signals of hand movements with the mmWave radar module and preprocessing the received radar signals, including Fourier transformation, distance compression, Doppler processing, and noise reduction through moving target indication (MTI). The preprocessed signals are then fed into the Convolutional Neural Network-Time Domain Convolutional Network (CNN-TCN) model to extract spatio-temporal features, with recognition performance evaluated through classification. Experimental results demonstrate that this method achieves an accuracy rate of 98.2% in domain-specific recognition and maintains a consistently high recognition rate across different neural networks, showcasing exceptional recognition performance and robustness.

翻译：手势识别是智能教育领域的一项关键技术，毫米波信号具有高分辨率和强穿透能力等优势。本文提出了一种利用毫米波雷达实现高精度、高鲁棒性手势识别的方法。该方法通过毫米波雷达模块采集手部运动的原始信号，并对接收的雷达信号进行预处理，包括傅里叶变换、距离压缩、多普勒处理以及通过运动目标指示（MTI）进行降噪。预处理后的信号被输入卷积神经网络-时间域卷积网络（CNN-TCN）模型以提取时空特征，并通过分类评估识别性能。实验结果表明，该方法在特定领域识别中达到98.2%的准确率，且在不同神经网络中保持一致的识别率，展现出卓越的识别性能和鲁棒性。