Abstract: Surface electromyography (sEMG)-based gesture recognition is a key technology in the feld of human–computer interaction. However, existing gesture recognition methods face challenges in efectively integrating discriminative temporal feature representations from sEMG signals. In this paper, we propose a deep learning framework named TFN-FICFM comprises a Temporal Fusion Network (TFN) and Fuzzy Integral-Based Classifer Fusion method (FICFM) to improve the accuracy and robustness of gesture recognition. Firstly, we design a TFN module, which utilizes an attention-based recurrent multi-scale convolutional module to acquire multi-level temporal feature representations and achieves deep fusion of temporal features through a feature pyramid module. Secondly, the deep-fused temporal features are utilized to generate multiple sets of gesture category prediction confdences through a feedback loop. Finally, we employ FICFM to perform fuzzy fusion on prediction confdences, resulting in the ultimate decision. This study conducts extensive comparisons and ablation studies using the publicly available datasets Ninapro DB2 and DB5. Results demonstrate that the TFN-FICFM model outperforms state-ofthe-art methods in classifcation performance. This research can serve as a benchmark for sEMG-based gesture recognition and related deep learning modeling.

Key words: Gesture recognition , · sEMG , · Deep learning , · Temporal fusion , · Fuzzy fusion