从弱注释的数据集学习基于视频的实时评估中风康复练习的框架级分类器
概括
这项研究引入了使用弱注释视频实时评估中风康复练习的新框架. 这种方法简化了对新患者的虚拟教练定制,
科学领域:
- 康复工程
- 计算机视觉
- 机器学习
背景情况:
- 虚拟教练需要实时反,
- 收集完整的注释数据集进行评估是昂贵且耗时的.
- 当前的方法在实时补偿运动评估方面面临挑战.
研究的目的:
- 开发一种用于实时评估中风康复练习中的补偿运动的新框架.
- 通过生成伪标签来使用弱注释的视频来实现级分类.
- 用于新练习和有限的标记数据的患者.
主要方法:
- 使用源数据集进行框架级分类器训练的基线方法.
- 使用目标视频级别标签和源数据集参数的转移学习方法.
- 一种半监督的方法,利用目标视频级标签和一小组级标签.
- 用于框架级分类的伪标签生成.
主要成果:
- 基线方法对新数据集 (SERE) 的概括性有限,F1得分为72. 87%.
- 转移学习和半监督方法的F1得分分别为80.47%和78.93%.
- 与基线相比,伪标签生成提高了目标数据集的框架级分类准确性.
结论:
- 建议的框架有效地使用弱注释的视频评估中风康复练习中的补偿运动.
- 转移学习和半监督方法显著改善了对新数据集,练习和患者的概括性.
- 这种方法减少了数据注释的费用,简化了虚拟康复教练的定制.
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