Dual-Path Region-Guided Attention Network for Ground Reaction Force and Moment Regression
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arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908694323658752 |
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| author | Li, Xuan Bello, Samuel |
| author_facet | Li, Xuan Bello, Samuel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_05030 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Dual-Path Region-Guided Attention Network for Ground Reaction Force and Moment Regression Li, Xuan Bello, Samuel Machine Learning Signal Processing 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. |
| title | Dual-Path Region-Guided Attention Network for Ground Reaction Force and Moment Regression |
| topic | Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2512.05030 |