動的文脈フォーカスとゲート付き線形ユニットによる膝関節角度予測の改善
Improving Knee Joint Angle Prediction through Dynamic Contextual Focus and Gated Linear Units
動的文脈フォーカス注意とゲート付き線形ユニットを組み込んだFocalGatedNetを提案し、歩行軌道の多段予測で膝関節角度を高精度かつ低計算コストで予測できることを示した。
著者: Lyes Saad Saoud, Humaid Ibrahim, Ahmad Aljarah, Irfan Hussain
分類: cs.RO
原文アブストラクト
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.
