An activity pattern recognition method and system based on wavelet transform and CNN / transformer

Abstract

This invention discloses an activity pattern recognition method and system based on wavelet transform and CNN / Transformer. First, wavelet transform is used to perform multi-scale time-frequency decomposition on the original signal to enhance the perception of subtle local features and high-frequency components. Then, convolution operations are used to further extract and fuse effective features in the time-frequency domain. Finally, leveraging the self-attention mechanism of Transformer, global dependencies are captured in the enhanced feature sequence, thereby achieving efficient collaborative modeling of local information and global context. This invention overcomes the insensitivity of Transformer models to local details and high-frequency information, effectively avoiding the neglect or smoothing of crucial subtle action patterns and transiently changing high-frequency signals during recognition, significantly improving the model's recognition accuracy and enhancing the reliability and interpretability of the entire system in practical applications.

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