Simple and Efficient Gesture Recognition Based on Frequency-Modulated Continuous Wave Radar

With the development of technology, using radar for gesture recognition is feasible and valuable. However, ensuring that gesture recognition can be applied to a wide range of scenarios with sufficient accuracy is still challenging. Due to the lack of accuracy and efficiency of traditional methods, we propose a gesture recognition scheme based on deep learning. We converted radar signals into pictures and designed a lightweight network called a self-reparameterization network based on distance and velocity awareness and binary coding (SR-DVBNet) to match them. We use the Self-reparameterization Encoder of the signal as the baseline of the network and add distance- and velocity-aware embedding (DVAE) between different blocks to do weighting for different dimensions. Since gesture recognition by radar signals often uses 2-D data, such as RDM or chirp-arranged matrices, we designed the DVAE module, which can weigh the different dimensions of the data separately to enhance the interpretability and gesture recognition accuracy of the model. At the same time, we use binary descriptors as the final representation of feature vectors for classification, which can well reflect the features of images and improve classification accuracy. Finally, we verify the algorithm’s effectiveness on two publicly available datasets and achieve an accuracy rate of more than 98%, surpassing other known gesture recognition algorithms.