文献类型：期刊文献

英文题名：Multi-attention Augmented Spatio-Temporal Graph Convolution Network for Gait Recognition Based on IMUs Data

作者：Yan, Jianjun[1]; Yang, Zhihao[2]; Lin, Yue[2]; Zhou, Wei[2]

机构：[1] Shanghai Key Laboratory of Intelligent Sensing and Detection Technology, East China University of Science and Technology, Shanghai, China; [2] School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai, China

年份：2025

外文期刊名：EEiSS 2025 - 2025 2nd International Conference on Electronic Engineering and Information Systems

收录：EI(收录号：20253318982948)

语种：英文

外文关键词：Biological organs - Gait analysis - Graphic methods - Joints (anatomy)

摘要：Gait Recognition is considered crucial for controlling Lower Limb Exoskeletons (LLEs). IMUs are widely utilized due to their portability and lightweight nature; however, existing methods often fail to capture the spatial connections among sensors. To address this issue, the Multi-attention Augmented Spatio Temporal Graph Convolution Network (MA-ST-GCN) is proposed for IMU-based Gait Recognition. This approach constructs a spatial graph based on human skeleton information and is refined through a Spatial Attention mechanism, which captures dependencies among joint nodes. A Temporal Attention mechanism is employed to identify key gait phases, thereby enhancing spatial graph convolution, while Channel Attention is leveraged to modulate different sensor channels, improving overall performance selectively. MA-ST-GCN was compared with RNN, LSTM, TCN, TST, ST-GCN, DGNN, and CA-MSN, demonstrating superior performance in integrating IMU data with skeleton information, confirming its effectiveness in gait recognition. ? 2025 IEEE.