用Transformer去噪补全康复动作数据，实时识别异常动作
Transformer-Based Framework for Motion Capture Denoising and Anomaly Detection in Medical Rehabilitation
- 基于Transformer建模动作时序，自动修复噪声和缺失数据
- 在中风与骨科康复数据集上重建精度优于现有方法
- 适合远程康复场景，降低现场监护成本
本文提出一种端到端深度学习框架，融合光学运动捕捉与基于Transformer的模型，以提升医疗康复效果。该框架解决因遮挡和环境因素导致的数据噪声与缺失问题，同时实现实时异常动作检测，保障患者安全。通过时序建模，框架可对运动捕捉数据进行去噪与补全，增强系统鲁棒性。在中风及骨科康复数据集上的评估显示，其在数据重建与异常检测方面表现更优，为远程康复提供可扩展、低成本的解决方案，减少现场监督需求。
原文摘要 · Abstract (English)
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.
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