Zhiqiang Bao
ABSTRACT
Aiming at the defects of convolutional neural network that it is difficult to extract high-level visual semantic information and ignore inter-channel information, a millimeter wave radar fall detection algorithm based on improved Transformer is proposed. By combining the channel attention mechanism with the Transformer network structure to form a pyramid structure, the temporal information and spatial information of the signal are effectively extracted, the feature extraction ability of the deep learning network model is enhanced, and the problem of overfitting of the Transformer structure under small samples is improved. The fall detection of millimeter wave radar signal is realized. The experimental results show that the classification accuracy of the algorithm is 96.8%, which verifies the feasibility and effectiveness of the model.\
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Index Terms
- Millimeter Wave Radar Fall Detection Algorithm Based on Improved Transformer
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