Continuous Joint Kinematics Prediction Using GAT-LSTM Framework Based on Muscle Synergy and Sparse sEMG

sEMG signals hold significant potential for motion prediction, with promising applications in areas such as rehabilitation, sports training, and human-computer interaction. However, achieving robust prediction accuracy remains a critical challenge, as even minor inaccuracies in motion prediction can severely affect the reliability and practical utility of sEMG-based systems. In this study, we propose a novel framework, muscle synergy (MS)-based graph attention networks (MSGAT-LSTM), specifically designed to address the challenges of continuous motion prediction using sparse sEMG electrodes. By leveraging MS theory and graph-based learning, the framework effectively compensates for the limitations of sparse sEMG setups and achieves significant improvements in prediction accuracy compared to existing methods. Based on MS theory, the framework calculates cosine similarity between sEMG signal features from different muscles to assign edge weights, effectively capturing their coordinated contributions to motion. The proposed framework integrates GAT for relational feature learning with LSTM networks for temporal dependency modeling, leveraging the strengths of both architectures. Experimental results on the public dataset Ninapro DB2 and a self-collected dataset demonstrate that MSGAT-LSTM achieves superior performance compared to state-of-the-art methods, including the muscle anatomy and MS-based 3DCNN, GCN-LSTM, and classic models such as CNN-LSTM, CNN, and LSTM, in terms of RMSE and R2. Furthermore, experimental results reveal that incorporating MS into GCN reduces training time by 13% compared to GCN-LSTM, significantly enhancing computational efficiency and scalability. This study highlights the potential of integrating MS theory with graph-based deep learning methods for motion prediction based on sEMG.