Prediction of Lower Limb Joint Angles Based on Muscle Synergy Theory

Abstract

Rapid and accurate prediction of lower limb joint angles is crucial for exoskeleton control. This paper introduces a multidimensional Gumbel Copula function during preprocessing to enhance the statistical dependency among synergistic muscles, and proposes a method for predicting lower limb joint angles based on muscle synergy theory. In the experiments, surface electromyography (sEMG) signals were collected during squatting, running, level walking, and uphill walking. The collected data underwent preprocessing, and features were extracted from the sEMG signals using a sliding window method. Muscle synergy theory reflects the movement mechanisms and regulatory mechanisms of muscles during human motion. By utilizing muscle synergy weights to perform dimensionality reduction on the sEMG signal features, the number of features input into the Convolutional Neural Networks–iTransformer(CNN–iTransformer) prediction model was selected based on the magnitude of the synergy weights. Finally, the trained regression model was evaluated using the metrics root-mean-square error (RMSE), R2, mean absolute error (MAE), and Pearson correlation coefficient (PCC). For the 16 participants, the average RMSE and MAE for knee joint angle prediction were 0.61° and 0.45°, respectively, while the average R2 and PCC were 0.998 and 0.996, respectively. Compared with results from currently common muscle synergy-based dimensionality reduction methods, the method proposed in this article achieved the best prediction accuracy within an optimal prediction time. The results demonstrate the superior performance of the proposed method in knee joint angle prediction, which can facilitate the future development of lower limb exoskeletons and provide more precise assistance to users in medical and industrial fields.