  Accurate knee joint angle prediction is crucial for biomechanical analysis
and rehabilitation. In this study, we introduce FocalGatedNet, a novel deep
learning model that incorporates Dynamic Contextual Focus (DCF) Attention and
Gated Linear Units (GLU) to enhance feature dependencies and interactions. Our
model is evaluated on a large-scale dataset and compared to established models
in multi-step gait trajectory prediction. Our results reveal that FocalGatedNet
outperforms existing models for long-term prediction lengths (20 ms, 60 ms, 80
ms, and 100 ms), demonstrating significant improvements in Mean Absolute Error
(MAE) and Root Mean Square Error (RMSE). Specifically for the case of 80 ms,
FocalGatedNet achieves a notable MAE reduction of up to 24\%, RMSE reduction of
up to 14\%, and MAPE reduction of up to 36\% when compared to Transformer,
highlighting its effectiveness in capturing complex knee joint angle patterns.
Moreover, FocalGatedNet maintains a lower computational load than most
equivalent deep learning models, making it an efficient choice for real-time
biomechanical analysis and rehabilitation applications.
