通过模拟康复动作错误生成数据，提升运动评估准确率。
Error-Guided Pose Augmentation: Enhancing Rehabilitation Exercise Assessment through Targeted Data Generation
- 基于临床常见错误生成骨骼数据，针对性增强模型训练
- 误差分类准确率提升45.8%，平均绝对误差降低27.6%
- 适合关注康复评估与可解释性的人工智能研究者
有效的康复评估对监测患者进展至关重要，尤其在居家场景中。现有系统常面临数据不平衡和细微动作错误难以检测的问题。本文提出误差引导的姿态增强（EGPA），通过模拟临床相关的运动错误生成合成骨骼数据。与基于注意力的图卷积网络结合，EGPA在多个评估指标上表现更优。实验表明，平均绝对误差降低27.6%，误差分类准确率提升45.8%。注意力可视化显示模型聚焦于临床关键关节和运动阶段，提升了准确性和可解释性。EGPA为临床及居家康复中的自动化运动质量评估提供了有效方案。
原文摘要 · Abstract (English)
Effective rehabilitation assessment is essential for monitoring patient progress, particularly in home-based settings. Existing systems often face challenges such as data imbalance and difficulty detecting subtle movement errors. This paper introduces Error-Guided Pose Augmentation (EGPA), a method that generates synthetic skeleton data by simulating clinically relevant movement mistakes. Unlike standard augmentation techniques, EGPA targets biomechanical errors observed in rehabilitation. Combined with an attention-based graph convolutional network, EGPA improves performance across multiple evaluation metrics. Experiments demonstrate reductions in mean absolute error of up to 27.6 percent and gains in error classification accuracy of 45.8 percent. Attention visualizations show that the model learns to focus on clinically significant joints and movement phases, enhancing both accuracy and interpretability. EGPA offers a promising approach for improving automated movement quality assessment in both clinical and home-based rehabilitation contexts.
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