用双路径注意力网络提升鞋垫传感器的三维力矩估计精度
Dual-Path Region-Guided Attention Network for Ground Reaction Force and Moment Regression
- 引入解剖结构与时间先验，通过区域级注意力聚焦关键传感区域
- 在鞋垫数据集上六维平均归一化误差低至5.78%，公开数据集垂直力误差1.42%
- 适合运动生物力学、康复评估领域的研究人员使用
准确估计三维地面反作用力与力矩（GRFs/GRMs）对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验证方法有效性。提出一种双路径区域引导注意力网络，将解剖学启发的空间先验与时间先验融合进区域级注意力机制，另一路径则捕获全传感器场的上下文信息。两条路径联合训练，输出融合生成最终预测结果。结果表明，该模型优于多种强基线模型（包括CNN与CNN-LSTM），在鞋垫数据集上实现六维平均归一化均方误差最低为5.78%，在公开数据集上垂直地面反作用力误差为1.42%，展现出优异的地面反作用力与力矩估计性能。
原文摘要 · Abstract (English)
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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