Prediction of Exoskeleton Knee Angle Based on Unilateral Lower Limb Information

Unilateral lower-limb exoskeletons improve system convenience and cost-effectiveness through reduced sensor and mechanical components. Unilateral lower-limb biomechanical signals alone pose challenges for precise, continuous gait perception in exoskeleton research. An LSTM-TPA (Long Short-Term Memory with Temporal Pattern Attention) model is proposed for continuous joint angle prediction based on unilateral lower limb information. Built on the LSTM framework, the model integrates a temporal pattern attention mechanism that adaptively focuses on critical time intervals within historical gait sequences crucial for current predictions, efficiently capturing the dynamic evolution of movement patterns. Offline prediction results based on patient experiments show a Pearson Correlation Coefficient of 0.9713 for the 200 ms prediction task, with MAE and RMSE reduced to 1.9149°and 3.4737°, respectively. This corresponds to 29.97% and 30.02% reductions in MAE and RMSE compared with the CNN-LSTM model. The proposed method exhibits effectiveness for continuous joint angle prediction under unilateral signal conditions, offering a reliable technical pathway for intelligent control of lightweight, low-cost lower-limb exoskeletons.