Gesture recognition is the latest human-computer interaction (HCI) technology, which allows users to naturally control electronic devices through the movement of fingers and palms without operating redundant devices. Radar gesture recognition technology offers significant advantages in terms of privacy and security, device reliability and design flexibility. In this paper, a model GestureNet suitable for radar gesture recognition is designed by using the smooth pseudo Wigner Ville processing of millimeter wave radar gesture echo and the knowledge of hybrid zero convolution neural network in deep learning. The results show that the recognition accuracy of the validation set of GestureNet reached 97.35% and the recognition accuracy of the test set reached 91.75%, indicating that the model has good generalisation ability, thus providing a strong guarantee for radar gesture recognition.
In order to effectively overcome the limitations of traditional gesture recognition technology, a method of gesture recognition using millimeter wave radar is proposed. First, according to the introduction of the millimeter-wave radar system and the description of the echo model, the millimeter-wave radar is used to collect the measured data; then the average cancellation method is used to suppress the clutter of the measured data, and the joint time-frequency analysis technology is used for effective feature extraction; The extracted features are used as the model input, and a lightweight convolutional neural network model is improved. Its recognition rate is over 96%, and it has good recognition performance.
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