  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.
