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Gesture recognition is a pivotal technology in the realm of intelligenteducation, and millimeter-wave (mmWave) signals possess advantages such as highresolution and strong penetration capability. This paper introduces a highlyaccurate and robust gesture recognition method using mmWave radar. The methodinvolves capturing the raw signals of hand movements with the mmWave radarmodule and preprocessing the received radar signals, including Fouriertransformation, distance compression, Doppler processing, and noise reductionthrough moving target indication (MTI). The preprocessed signals are then fedinto the Convolutional Neural Network-Time Domain Convolutional Network(CNN-TCN) model to extract spatio-temporal features, with recognitionperformance evaluated through classification. Experimental results demonstratethat this method achieves an accuracy rate of 98.2% in domain-specificrecognition and maintains a consistently high recognition rate across differentneural networks, showcasing exceptional recognition performance and robustness.

Gesture Recognition in Millimeter-Wave Radar Based on Spatio-Temporal Feature Sequences