IFIANet: Frequency Attention Network for Time-Frequency in sEMG-Based Motion Intent Recognition

Lower limb exoskeleton systems require accurate recognition of the wearer's movement intentions prior to action execution in order to achieve natural and smooth human-machine interaction. Surface electromyography (sEMG) signals can reflect neural activation of muscles before movement onset, making them a key physiological source for movement intention recognition. To improve sEMG-based recognition performance, this study proposes an innovative deep learning framework, IFIANet. First, a CNN-TCN-based spatiotemporal feature learning network is constructed, which efficiently models and represents multi-scale temporal-frequency features while effectively reducing model parameter complexity. Second, an IFIA (Frequency-Informed Integration Attention) module is designed to incorporate global frequency information, compensating for frequency components potentially lost during time-frequency transformations, thereby enhancing the discriminability and robustness of temporal-frequency features. Extensive ablation and comparative experiments on the publicly available MyPredict1 dataset demonstrate that the proposed framework maintains stable performance across different prediction times and achieves over 82% average recognition accuracy in within-experiments involving nine participants. The results indicate that IFIANet effectively fuses local temporal-frequency features with global frequency priors, providing an efficient and reliable approach for sEMG-based movement intention recognition and intelligent control of exoskeleton systems.