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

arXiv: https://arxiv.org/abs/2512.05030 · HTML: https://mlsanity.com/paper/2512.05030/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 35 references that are in the map.
- First posted on arXiv 4 Dec 2025.

## Builds on (most cited first, 3 of 3)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Estimating Ground Reaction Forces from Inertial Sensors](https://mlsanity.com/paper/2311.02287/) (2023)
- [A dataset of high-resolution plantar pressures for gait analysis across varying footwear and walking speeds](https://mlsanity.com/paper/2502.17244/) (2025)

## Abstract (arXiv)

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.