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TFN-FICFM:sEMG-Based Gesture Recognition Using temporal fusion Network and Fuzzy Integral-based Classifier fusion

Journal of Bionic Engineering 2024年第4期21卷 1878-1891页

作者： Fo Hu Kailun He Mengyuan Qian Mohamed Amin Gouda College of Information Engineering Zhejiang University of TechnologyHangzhou310023China Department of Mechanical Engineering and Automation Northeastern UniversityShenyang110819China

Surface electromyography(sEMG)-based gesture recognition is a key technology in the field of human–computer ***,existing gesture recognition methods face challenges in effectively integrating discriminative temporal feature representations from sEMG *** 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 ***,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 ***,the deep-fused temporal features are utilized to generate multiple sets of gesture category prediction confidences through a feedback ***,we employ FICFM to perform fuzzy fusion on prediction confidences,resulting in the ultimate *** study conducts extensive comparisons and ablation studies using the publicly available datasets Ninapro DB2 and *** demonstrate that the TFN-FICFM model outperforms state-of-the-art methods in classification *** 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

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Self-adaptive combination method for temporal evidence based on negotiation strategy

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Science China(Information Sciences) 2020年第11期63卷 56-68页

作者： Yafei SONG Jingwei ZHU Lei LEI Xiaodan WANG College of Air and Missile Defense Air Force Engineering University Institute of High Technology Air Force Engineering University College of Information and Navigation Air Force Engineering University

In temporal information fusion, the information collected by sensors is obtained dynamically with the passage of time. Unlike the spatial information fusion, temporal fusion should be dynamic. Evidence theory has been applied to fuse temporal and spatial information; however, existing temporal fusion methods do not treat conflicting and non-conflicting evidence sources distinctively. Moreover, unlike spatial evidence sources, which are obtained simultaneously, temporal evidence sources cannot be evaluated simultaneously to obtain their degree of reliability. Thus, it is necessary to develop a method for temporal evidence *** this paper, a self-adaptive combination method for temporal evidence is proposed based on the negotiation strategy. In the proposed method, a set called an evidence set is constructed by the cumulative temporal fusion results of the previous moment, current moment, and future moment. The evidence set is evaluated as conflicting or non-conflicting according to the maximum power pignistic probability distance between each pair of evidence sources in the set. Then, temporal evidence sources are self-adaptively combined by different methods according to the degree of conflict. Numerical experiments were conducted to evaluate the performance of the proposed method. The results indicated that the proposed method is sufficiently effective and robust to support decision making.

关键词： information fusion evidence theory temporal fusion self-adaptive combination negotiation strategy

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