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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2507.13371 |
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| _version_ | 1866911063128145920 |
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| author | Cai, Yeming Wang, Yang Li, Zhenglin |
| author_facet | Cai, Yeming Wang, Yang Li, Zhenglin |
| contents | This paper proposes an end-to-end deep learning framework integrating optical motion capture with a Transformer-based model to enhance medical rehabilitation. It tackles data noise and missing data caused by occlusion and environmental factors, while detecting abnormal movements in real time to ensure patient safety. Utilizing temporal sequence modeling, our framework denoises and completes motion capture data, improving robustness. Evaluations on stroke and orthopedic rehabilitation datasets show superior performance in data reconstruction and anomaly detection, providing a scalable, cost-effective solution for remote rehabilitation with reduced on-site supervision. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_13371 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Transformer-Based Framework for Motion Capture Denoising and Anomaly Detection in Medical Rehabilitation Cai, Yeming Wang, Yang Li, Zhenglin Computer Vision and Pattern Recognition Artificial Intelligence This paper proposes an end-to-end deep learning framework integrating optical motion capture with a Transformer-based model to enhance medical rehabilitation. It tackles data noise and missing data caused by occlusion and environmental factors, while detecting abnormal movements in real time to ensure patient safety. Utilizing temporal sequence modeling, our framework denoises and completes motion capture data, improving robustness. Evaluations on stroke and orthopedic rehabilitation datasets show superior performance in data reconstruction and anomaly detection, providing a scalable, cost-effective solution for remote rehabilitation with reduced on-site supervision. |
| title | Transformer-Based Framework for Motion Capture Denoising and Anomaly Detection in Medical Rehabilitation |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2507.13371 |