Shoulder–Elbow Joint Angle Prediction Using COANN with Multi-Source Information Integration

To address the precision challenges in upper-limb joint motion prediction, this study proposes a novel artificial neural network (COANN) enhanced by the Cheetah Optimization Algorithm (COA). The model integrates surface electromyography (sEMG) signals with joint angle data through multi-source information fusion, effectively resolving the local optima issue in neural network training and improving the accuracy limitations of single sEMG predictions. Experimental results demonstrate that the COANN achieves significant performance improvements: compared with RBF neural networks, it reduces the root mean square error (RMSE) by 24.32% (ΔR2 + 18.75%) with a 22.6% shorter system runtime; relative to conventional ANNs, it decreases the RMSE by 31.59% (ΔR2 + 12.15%) while reducing computational time by 35.1%; compared with CNN neural networks, it reduces the root mean square error (RMSE) by 14.9% (ΔR2 + 3.84%); and relative to conventional LSTM, it decreases the RMSE by 15.31% (ΔR2 + 4.86%). Multi-source integration enhanced elbow joint prediction accuracy by 5.7% and shoulder joint accuracy by 6.9% compared with single sEMG approaches. This methodology provides theoretical foundations for human–robot interaction systems in upper-limb rehabilitation robotics and motion-assistive devices.