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arXiv · 2512.05030

Dual-Path Region-Guided Attention Network for Ground Reaction Force and Moment Regression

Xuan Li·Samuel Bello
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Abstract

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.

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Xuan Li, Samuel Bello. 2025-12-04. Dual-Path Region-Guided Attention Network for Ground Reaction Force and Moment Regression. https://arxiv.org/abs/2512.05030

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