Improving Knee Joint Angle Prediction through Dynamic Contextual Focus and Gated Linear Units

TL;DR

This paper introduces FocalGatedNet, a deep learning model with Dynamic Contextual Focus and Gated Linear Units, significantly improving long-term knee joint angle predictions while maintaining computational efficiency.

Contribution

The paper presents a novel model combining DCF Attention and GLU for enhanced feature learning in knee angle prediction, outperforming existing models in accuracy and efficiency.

Findings

FocalGatedNet reduces MAE by up to 24% at 80 ms prediction.

The model achieves up to 14% lower RMSE compared to Transformer.

It maintains lower computational load than similar deep learning models.

Abstract

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.