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| Autores principales: | , , , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2512.14329 |
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| _version_ | 1866917182443618304 |
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| author | Dai, Yanning Tang, Chenyu Zhang, Ruizhi Yang, Wenyu Zhang, Yilan Wang, Yuhui Chen, Junliang Chen, Xuhang Xie, Ruimou Cao, Yangyue Li, Qiaoying Cao, Jin Li, Tao Zhao, Hubin Pan, Yu Nathan, Arokia Gao, Xin Smielewski, Peter Gao, Shuo |
| author_facet | Dai, Yanning Tang, Chenyu Zhang, Ruizhi Yang, Wenyu Zhang, Yilan Wang, Yuhui Chen, Junliang Chen, Xuhang Xie, Ruimou Cao, Yangyue Li, Qiaoying Cao, Jin Li, Tao Zhao, Hubin Pan, Yu Nathan, Arokia Gao, Xin Smielewski, Peter Gao, Shuo |
| contents | Dynamic prediction of locomotor capacity after stroke could enable more individualized rehabilitation, yet current assessments largely provide static impairment scores and do not indicate whether patients can perform specific tasks such as slope walking or stair climbing. Here, we present a wearable-informed data-physics hybrid generative framework that reconstructs a stroke survivor's locomotor control from wearable inertial sensing and predicts task-conditioned post-stroke locomotion in new environments. From a single 20 m level-ground walking trial recorded by five IMUs, the framework personalizes a physics-based digital avatar using a healthy-motion prior and hybrid imitation learning, generating dynamically feasible, patient-specific movements for inclined walking and stair negotiation. Across 11 stroke inpatients, predicted postures reached 82.2% similarity for slopes and 69.9% for stairs, substantially exceeding a physics-only baseline. In a multicentre pilot randomized study (n = 21; 28 days), access to scenario-specific locomotion predictions to support task selection and difficulty titration was associated with larger gains in Fugl-Meyer lower-extremity scores than standard care (mean change 6.0 vs 3.7 points; $p < 0.05$). These results suggest that wearable-informed generative digital avatars may augment individualized gait rehabilitation planning and provide a pathway toward dynamically personalized post-stroke motor recovery strategies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_14329 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Wearable-informed generative digital avatars predict task-conditioned post-stroke locomotion Dai, Yanning Tang, Chenyu Zhang, Ruizhi Yang, Wenyu Zhang, Yilan Wang, Yuhui Chen, Junliang Chen, Xuhang Xie, Ruimou Cao, Yangyue Li, Qiaoying Cao, Jin Li, Tao Zhao, Hubin Pan, Yu Nathan, Arokia Gao, Xin Smielewski, Peter Gao, Shuo Computational Engineering, Finance, and Science Artificial Intelligence Dynamic prediction of locomotor capacity after stroke could enable more individualized rehabilitation, yet current assessments largely provide static impairment scores and do not indicate whether patients can perform specific tasks such as slope walking or stair climbing. Here, we present a wearable-informed data-physics hybrid generative framework that reconstructs a stroke survivor's locomotor control from wearable inertial sensing and predicts task-conditioned post-stroke locomotion in new environments. From a single 20 m level-ground walking trial recorded by five IMUs, the framework personalizes a physics-based digital avatar using a healthy-motion prior and hybrid imitation learning, generating dynamically feasible, patient-specific movements for inclined walking and stair negotiation. Across 11 stroke inpatients, predicted postures reached 82.2% similarity for slopes and 69.9% for stairs, substantially exceeding a physics-only baseline. In a multicentre pilot randomized study (n = 21; 28 days), access to scenario-specific locomotion predictions to support task selection and difficulty titration was associated with larger gains in Fugl-Meyer lower-extremity scores than standard care (mean change 6.0 vs 3.7 points; $p < 0.05$). These results suggest that wearable-informed generative digital avatars may augment individualized gait rehabilitation planning and provide a pathway toward dynamically personalized post-stroke motor recovery strategies. |
| title | Wearable-informed generative digital avatars predict task-conditioned post-stroke locomotion |
| topic | Computational Engineering, Finance, and Science Artificial Intelligence |
| url | https://arxiv.org/abs/2512.14329 |