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.

翻译：准确估计三维地面反作用力与力矩（GRFs/GRMs）对于生物力学研究和临床康复评估至关重要。本研究聚焦于基于鞋垫的GRF/GRM估计，并在公开步行数据集上进一步验证了我们的方法。我们提出了一种双路径区域引导注意力网络，该网络将解剖学启发的空间先验和时间先验整合到区域级注意力机制中，同时通过互补路径捕获全传感器场的上下文信息。两条路径联合训练，其输出结合后生成最终的GRF/GRM预测。结论：我们的模型在两个数据集上均优于包括CNN和CNN-LSTM架构在内的强基线模型，在鞋垫数据集上实现了最低的六分量平均归一化均方根误差（NRMSE）5.78%，在公开数据集上垂直地面反作用力的NRMSE为1.42%。这证明了该模型在地面反作用力与力矩估计方面具有鲁棒性能。