Dual-Path Region-Guided Attention Network for Ground Reaction Force and Moment Regression
This work addresses the problem of precise GRF/GRM estimation for biomechanics researchers and clinicians, representing an incremental improvement over existing methods.
The paper tackled accurate estimation of ground reaction forces and moments (GRFs/GRMs) for biomechanics and rehabilitation, proposing a Dual-Path Region-Guided Attention Network that achieved a six-component average NRMSE of 5.78% on an insole dataset and 1.42% for vertical force on a public dataset, outperforming baseline models.
Accurate estimation of three-dimensional ground reaction forces and moments (GRFs/GRMs) is crucial for both biomechanics research and clinical rehabilitation evaluation. In this study, we focus on insole-based GRF/GRM estimation and further validate our approach on a public walking dataset. We propose a Dual-Path Region-Guided Attention Network that integrates anatomy-inspired spatial priors and temporal priors into a region-level attention mechanism, while a complementary path captures context from the full sensor field. The two paths are trained jointly and their outputs are combined to produce the final GRF/GRM predictions. Conclusions: Our model outperforms strong baseline models, including CNN and CNN-LSTM architectures on two datasets, achieving the lowest six-component average NRMSE of 5.78% on the insole dataset and 1.42% for the vertical ground reaction force on the public dataset. This demonstrates robust performance for ground reaction force and moment estimation.