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

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.