用多视角视频和贝叶斯模型自动评估中风康复手臂动作，准确率达89%
Automated ARAT Scoring Using Multimodal Video Analysis, Multi-View Fusion, and Hierarchical Bayesian Models: A Clinician Study
- 融合多视角视频与关键点数据，结合慢速快速网络和Transformer进行动作分析
- 采用分层贝叶斯模型推断动作质量，验证集准确率达89.0%且与临床评分高度一致
- 为医生提供可视化仪表盘，适合康复评估场景的可解释自动化系统
中风康复中上肢动作研究臂测验（ARAT）的手动评分耗时且存在主观差异。本文提出一种自动化评分系统，整合多模态视频分析，使用SlowFast、I3D和基于Transformer的模型，并结合OpenPose关键点与物体位置信息。通过患侧、健侧及顶视三个视角获取数据，采用早期与晚期融合策略融合多视图特征。分层贝叶斯模型（HBMs）用于推断动作质量维度，提升结果可解释性。系统生成包含任务得分、执行时间与质量评估的临床仪表盘。五名临床医师对500段系统生成的视频评分进行评审，反馈其准确性与可用性。在中风康复数据集上的评估显示，晚融合策略达到89.0%的验证准确率，且HBMs结果与人工评分高度吻合。该工作推动了自动化康复评估的发展，提供了一种可扩展、可解释且经临床验证的解决方案。
原文摘要 · Abstract (English)
Manual scoring of the Action Research Arm Test (ARAT) for upper extremity assessment in stroke rehabilitation is time-intensive and variable. We propose an automated ARAT scoring system integrating multimodal video analysis with SlowFast, I3D, and Transformer-based models using OpenPose keypoints and object locations. Our approach employs multi-view data (ipsilateral, contralateral, and top perspectives), applying early and late fusion to combine features across views and models. Hierarchical Bayesian Models (HBMs) infer movement quality components, enhancing interpretability. A clinician dashboard displays task scores, execution times, and quality assessments. We conducted a study with five clinicians who reviewed 500 video ratings generated by our system, providing feedback on its accuracy and usability. Evaluated on a stroke rehabilitation dataset, our framework achieves 89.0% validation accuracy with late fusion, with HBMs aligning closely with manual assessments. This work advances automated rehabilitation by offering a scalable, interpretable solution with clinical validation.
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