GenJAPNet: A Generalizable Joint Angle Prediction Network with Non-Redundant Muscle Synergy Features for Lower-Limb Exoskeletons

Abstract

Lower-limb exoskeleton robots play a significant role in both rehabilitation and assisted walking, where accurate prediction of lower-limb joint angles is crucial for achieving natural gait. However, due to inter-subject variability and differences across locomotion modes, achieving cross-task generalization in joint angle prediction remains a major challenge. This work proposes a novel framework for multi-joint angle prediction in the lower-limb, which includes a non-redundant muscle synergy feature extraction algorithm and a Generalizable Joint Angle Prediction Network (GenJAPNet) across speeds and subjects. The feature extraction algorithm employs Non-negative Matrix Factorization (NMF) to extract activation coefficient matrix from Surface Electromyography (sEMG) signals, followed by further dimensionality reduction using Uniform Manifold Approximation and Projection (UMAP) to obtain more discriminative and non-redundant features. GenJAPNet leverages pre-trained shared features and few-shot fine-tuning to rapidly adapt to new task. Through feature extraction algorithm comparison experiments, cross-speed and cross-subject experiments, and exoskeleton-assisted walking physical experiments, the effectiveness and generalizability of this method are validated, demonstrating its potential for enhancing the performance of lower-limb exoskeleton rehabilitation and assistive applications.