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A Novel Approach to Solve the Missing Marker Problem in Marker-Based Motion Analysis That Exploits the Segment Coordination Patterns in Multi-Limb Motion Data

机译:解决基于标记的运动分析中缺少标记问题的新方法该方法利用了多肢运动数据中的段协调模式

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摘要

Marker-based human motion analysis is an important tool in clinical research and in many practical applications. Missing marker information caused by occlusions or a marker falling off is a common problem impairing data quality. The current paper proposes a conceptually new gap filling algorithm and presents results from a proof-of-principle analysis. The underlying idea of the proposed algorithm was that a multitude of internal and external constraints govern human motion and lead to a highly subject-specific movement pattern in which all motion variables are intercorrelated in a specific way. Two principal component analyses were used to determine how the coordinates of a marker with gaps correlated with the coordinates of the other, gap-free markers. Missing marker data could then be reconstructed through a series of coordinate transformations. The proposed algorithm was tested by reconstructing artificially created gaps in a 20-step walking trial and in an 18-s one-leg balance trial. The measurement accuracy’s dependence on the marker position, the length of the gap, and other parameters were evaluated. Even if only 2 steps of walking or 1.8 s of postural sway (10% of the whole marker data) were provided as input in the current study, the reconstructed marker trajectory differed on average no more than 11 mm from the originally measured trajectory. The reconstructed result improved further, on average, to distances below 5 mm if the marker trajectory was available more than 50% of the trial. The results of this proof-of-principle analysis supported the assumption that missing marker information can be reconstructed from the intercorrelations between marker coordinates, provided that sufficient data with complete marker information is available. Estimating missing information cannot be avoided entirely in many situations in human motion analysis. For some of these situations, the proposed reconstruction method may provide a better solution than what is currently available.
机译:基于标记的人体运动分析是临床研究和许多实际应用中的重要工具。由阻塞或标记脱落导致的标记信息丢失是损害数据质量的常见问题。当前论文提出了一种概念上新的缺口填充算法,并给出了原理验证分析的结果。所提出算法的基本思想是,许多内部和外部约束控制着人类的动作,并导致高度特定于对象的运动模式,其中所有运动变量都以特定方式相互关联。使用两个主成分分析来确定具有间隙的标记的坐标与其他无间隙标记的坐标如何相关。然后可以通过一系列坐标转换来重建丢失的标记数据。通过在20步步行试验和18秒单腿平衡试验中人工创建的间隙来测试提出的算法。测量精度取决于标记位置,间隙长度和其他参数。即使在当前研究中仅提供了2步步行或1.8 s的姿势摆动(占整个标记数据的10%)作为输入,重建的标记轨迹与原始测量的轨迹平均平均相差不超过11​​ mm。如果标记轨迹可用于试验的50%以上,则重建结果平均平均可进一步改善至5 mm以下的距离。该原理验证分析的结果支持这样的假设:只要有足够的具有完整标记信息的数据,就可以从标记坐标之间的相互关系中重建缺失的标记信息。在人体运动分析的许多情况下,无法完全避免估计丢失的信息。对于这些情况中的某些情况,所提出的重建方法可能会提供比当前可用方法更好的解决方案。

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  • 作者

    Peter Andreas Federolf;

  • 作者单位
  • 年(卷),期 -1(8),10
  • 年度 -1
  • 页码 e78689
  • 总页数 13
  • 原文格式 PDF
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