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首页> 外文期刊>Journal of Biomechanics >Determining anatomical frames via inertial motion capture: A survey of methods
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Determining anatomical frames via inertial motion capture: A survey of methods

机译：通过惯性运动捕获确定解剖结构：方法调查

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Despite the exponential growth in using inertial measurement units (IMUs) for biomechanical studies, future growth in "inertial motion capture" is stymied by a fundamental challenge - how to estimate the orientation of underlying bony anatomy using skin-mounted IMUs. This challenge is of paramount importance given the need to deduce the orientation of the bony anatomy to estimate joint angles. This paper systematically surveys a large number (N = 112) of studies from 2000 to 2018 that employ four broad categories of methods to address this challenge across a range of body segments and joints. We categorize these methods as: (1) Assumed Alignment methods, (2) Functional Alignment methods, (3) Model Based methods, and (4) Augmented Data methods. Assumed Alignment methods, which are simple and commonly used, require the researcher to visually align the IMU sense axes with the underlying anatomical axes. Functional Alignment methods, also commonly used, relax the need for visual alignment but require the subject to complete prescribed movements. Model Based methods further relax the need for prescribed movements but instead assume a model for the joint. Finally, Augmented Data methods shed all of the above assumptions, but require data from additional sensors. Significantly different estimates of the underlying anatomical axes arise both across and within these categories, and to a degree that renders it difficult, if not impossible, to compare results across studies. Consequently, a significant future need remains for creating and adopting a standard for defining anatomical axes via inertial motion capture to fully realize this technology's potential for biomechanical studies. (C) 2020 Elsevier Ltd. All rights reserved.
机译：尽管使用惯性测量单位（IMU）对生物力学研究的指数增长，但“惯性运动捕获”的未来增长是由基本挑战所延迟的，如何使用皮肤安装的IMU来估计底层骨骼解剖学的取向。如果需要推断骨骼解剖学以估计关节角度的需要，这一挑战是至关重要的。本文系统地从2000年到2018年系统地调查了大量（n = 112）的研究，该研究雇用了四种广泛类别的方法来解决一系列身体细分和关节的挑战。我们将这些方法分类为：（1）假设对齐方法，（2）功能对准方法，（3）基于模型的方法，和（4）增强数据方法。假设是简单且常用的对准方法，要求研究人员用底层的解剖轴视觉上对准IMU感应轴。功能对准方法，也常用，放松视觉对准的需要，但需要受试者完成规定的运动。基于模型的方法进一步放松了规定运动的需要，而是假设关节的模型。最后，增强数据方法揭示了所有上述假设，但需要来自额外传感器的数据。在这些类别中和在这些类别中产生显着不同的底层解剖轴的估计，并且在难度越来越困难的程度上，可以在跨研究中比较结果。因此，仍有重要的需求来创造和采用通过惯性运动捕获来定义解剖轴来充分实现这种技术对生物力学研究的可能性的标准。 （c）2020 elestvier有限公司保留所有权利。

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