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

TL;DR

This paper introduces a dual-path attention network that effectively estimates ground reaction forces and moments from insole sensor data, outperforming existing models in accuracy and robustness.

Contribution

The study presents a novel dual-path region-guided attention network that combines spatial and temporal priors for improved GRF/GRM estimation from insole sensors.

Findings

Outperforms CNN and CNN-LSTM baselines on two datasets.

Achieves lowest average NRMSE of 5.78% on insole dataset.

Achieves 1.42% NRMSE for vertical ground reaction force.

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.