TFN-FICFM:sEMG-Based Gesture Recognition Using Temporal Fusion Network and Fuzzy Integral-based Classifier Fusion  

作　　者：Fo Hu Kailun He Mengyuan Qian Mohamed Amin Gouda 

机构地区：[1]College of Information Engineering,Zhejiang University of Technology,Hangzhou,310023,China [2]Department of Mechanical Engineering and Automation,Northeastern University,Shenyang,110819,China

出　　处：《Journal of Bionic Engineering》2024年第4期1878-1891,共14页仿生工程学报（英文版）

基　　金：supported in part by the Natural Science Foundation of Zhejiang Province(LQ23F030015);the Key Laboratory of Intelligent Processing Technology for Digital Music(Zhejiang Conservatory of Music),Ministry of Culture and Tourism(2023DMKLC013).

摘　　要：Surface electromyography(sEMG)-based gesture recognition is a key technology in the field of human–computer interaction.However,existing gesture recognition methods face challenges in effectively 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 Classifier 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 confidences through a feedback loop.Finally,we employ FICFM to perform fuzzy fusion on prediction confidences,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-of-the-art methods in classification performance.This research can serve as a benchmark for sEMG-based gesture recognition and related deep learning modeling.

关 键 词：Gesture recognition SEMG Deep learning Temporal fusion Fuzzy fusion 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]