高技术通讯2026，Vol.36Issue(7)：677-687,11.DOI:10.3772/j.issn.1002-0470.2026.07.002
基于反残差自注意力模块与多模态、多特征融合的步态识别算法研究
A novel gait recognition algorithm based on inverted residual self-attention module and multi-modality,multi-feature fusion
摘要
Abstract
Gait recognition is fundamental to human-machine interaction for lower-limb wearable devices.Considering that a single modality and unitary feature cannot provide comprehensive information for gait recognition,and that there is limited number of researches incorporating attention mechanisms,this paper proposes a lightweight multi-modal gait recognition algorithm that integrates deep learning features with traditional time-domain features and ex-plores a new fusion paradigm of the two types of features.The proposed algorithm extracts time-domain features and deep learning features from both posture signals and electromyography(EMG)signals simultaneously and then adopts a hierarchical fusion method to integrate them step by step.A lightweight feature extraction module based on the self-attention mechanism and inverted residual structure is designed to retain the extraction capability of convo-lutional neural networks and increase the capacity for remote information interaction.Additionally,a fusion module is designed to integrate features from different modalities,enhancing the useful information in the features while ig-noring the unimportant information.The proposed algorithm achieves accuracies of 98.16%and 95.38%on a pub-lic dataset ENABL3S and a self-collected dataset,respectively.Moreover,the contribution of inertial measurement unit(IMU)and EMG features to the gait recognition accuracy is analyzed,providing references for researchers in the subsequent gait recognition algorithm research.关键词
步态识别/肌电/惯性测量单元/自注意力/时域特征Key words
gait recognition/electromyography/inertial measurement unit/self-attention/time domain fea-ture引用本文复制引用
赵昶辰,刘凯,黄斌,冯远静..基于反残差自注意力模块与多模态、多特征融合的步态识别算法研究[J].高技术通讯,2026,36(7):677-687,11.基金项目
国家自然科学基金面上项目(62573171)和国家自然科学基金青年基金(62203037)资助项目. （62573171）
