A Novel Framework for sEMG Gesture Recognition Based on Soft Prompt Learning

Surface electromyography sEMG signals hold considerable promise for predicting human motion prior to its actual execution. However, a major challenge in sEMG-based intention recognition lies in the severe noise interference and high inter-subject variability inherent in traditional myoelectric time-series signals. These issues hinder accurate alignment with corresponding actions and constrain model learning capacity. To address these challenges, this study proposes a dual-modal contrastive learning framework based on Contrastive Language-Audio Pretraining CLAP . By introducing textual prompts as auxiliary guidance for interpreting sEMG signals, the proposed method enhances recognition accuracy while reducing redundant training. In addition, a k-layer hierarchical processing algorithm is developed to expand the training dataset to a quadratic scale of its original size, thereby mitigating the problem of limited data availability and facilitating integrated prediction. The proposed approach is evaluated on public benchmark datasets, including Ninapro DB1, DB2, DB5, and CapgMyo. Experimental results show that the model outperforms state-of-the-art SOTA methods by 2–3 .