A Novel Method of Lower Limbs Locomotion Transition and Steady Modes Classification Based on sEMG Signals

Accurate recognition of transitional and steady states in lower limb wearable exoskeletons using surface electromyography (sEMG) signals remains challenging due to the transition sEMG signals exhibiting short duration, strong nonlinearity, and similarity to the preceding and following steady states. This study proposed a novel classification method to classify various modes, including transition and steady modes. Specifically, an improved complete ensemble empirical mode decomposition adaptive noise (ICEEMDAN) method, combined with the crested porcupine optimization (CPO) algorithm, was proposed to enhance the ability to capture sEMG signal features. A dual-branch fusion recognition network was proposed, it extracts the correlation motion features between different channel. The multi-scale CNN was employed to extract motion features for steady and transition modes. The attention mechanism was applied to enhance the representation of fused features from the CNN. Finally, a DenseNet network was employed for precise classification. Eight participants were recruited for the experiment, and sEMG data were collected for five steady modes and eight transition modes. It was validated through extensive experiments that the proposed method can effectively classify transition and steady modes, achieving an average accuracy of 97.39%, which outperforms existing methods. The proposed method achieves a recognition time of 25.5108 ms, satisfying the requirements for real-time deployment. Additionally, it attains accuracies of 0.8616 on two publicly available datasets, demonstrating generalization capability in the field of lower limb motion recognition.