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Movement Retraining using Real-time Feedback of Performance
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用基于标记的视频方法对儿童面部运动不对称性的客观动态评估.

Dawid Danecki1, Agata Sage1, Zuzanna Miodońska1

  • 1Department of Medical Informatics and Artificial Intelligence, Faculty of Biomedical Engineering, Silesian University of Technology, 41-800 Zabrze, Poland.

Journal of clinical medicine
|March 14, 2026
PubMed
概括

这项研究引入了一种低成本的基于标记的方法,以定量评估面部运动对称性. 动态描述符有效地区分对称和不对称的面部运动,帮助诊断和治疗.

关键词:
生物医学信号处理面部不对称的情况面部运动分析分析客观评价 客观评价 客观评价儿科人口中的儿科人口.物理治疗,物理治疗.视频分析视频分析

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科学领域:

  • 生物医学工程 生物医学工程
  • 神经学 神经学
  • 计算机视觉 计算机视觉

背景情况:

  • 面部运动对称性对于评估神经肌肉功能至关重要,不对称性表明潜在的神经疾病,创伤或术后并发症.
  • 在临床实践中,定量对称性评估对于准确的诊断,有效的治疗监测和精确的手术规划至关重要.

研究的目的:

  • 开发和验证一种基于标记器的方法,用于稳定跟踪面部运动.
  • 调查动态面部运动描述器在区分对称和不对称的运动执行中的有效性.

主要方法:

  • 使用低成本的视频采集设置,75名患者进行了眉毛抬起和微笑的练习.
  • 在面部地标上放置了17个ArUco标记器,并使用了涉及标记器/面部检测和对称轴估计的处理管道.
  • 动态描述符, VertDist (垂直位移) 和 Ratio (相对位置),以及它们的衍生品,被提取并进行统计分析.

主要成果:

  • 确定了对称和不对称运动之间的统计学上显著差异,特别是在 VertDist 和 Ratio 的第一个衍生品中.
  • 在抬起眉毛的 VertDist 和微笑的 Ratio 中观察到较大的效果大小.
  • 对于抬起眉毛的Ratio和微笑的VertDist,观察到适度的效果大小.

结论:

  • 提出的基于标记物的方法为量化面部运动不对称性提供了可靠且具有成本效益的解决方案.
  • 来自 VertDist 和 Ratio 的动态描述符在区分对称和不对称的面部运动方面显示出显著的潜力,支持临床应用.