LAUSR

Multi-stream signals are increasingly being used in robot-assisted rehabilitation training, where the timely and accurate prediction of a patient's motor intentions is frequently required to provide simultaneous and proportional control strategies. However, existing methods for motion intent prediction typically isolate signals from different modalities, resulting in a loss of signal traceability and interpretability. Therefore, we propose a multi-stream signal-fusion strategy based on knowledge tracing (MSKT). First, we collected surface electromyography, force myography, and vibroarthrography signals from the lower extremity and used different encoders to map these signals into a problem component, concept component, and identity token component. The process of human learning was then simulated using a knowledge-tracing strategy to reconstruct these components into new features. Finally, the reconstructed set of depth features was mapped to joint angles. We validated the performance of the proposed method using motion data from eight participants in different locomotion modes and compared the results to those of a temporal convolution network and long short-term memory networks. The results indicate that the MSKT achieves the lowest root-mean-squared error. This study demonstrates that the proposed knowledge-tracing-based strategy enables accurate continuous estimation for lower-limb exoskeleton rehabilitation robots.