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首页> 外文期刊>Communications in numerical methods in engineering >A model‐based motion capture marker location refinement approach using inverse kinematics from dynamic trials
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A model‐based motion capture marker location refinement approach using inverse kinematics from dynamic trials

机译：基于模型的运动捕捉标记位置优化方法，使用动态试验中的逆运动学方法

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摘要

Marker-based motion capture techniques are commonly used to measure human body kinematics. These techniques require an accurate mapping from physical marker position to model marker position. Traditional methods utilize a manual process to achieve marker positions that result in accurate tracking. In this work, we present an optimization algorithm for model marker placement to minimize marker tracking error during inverse kinematics analysis of dynamic human motion. The algorithm sequentially adjusts model marker locations in 3-D relative to the underlying rigid segment. Inverse kinematics is performed for a dynamic motion capture trial to calculate the tracking error each time a marker position is changed. The increase or decrease of the tracking error determines the search direction and number of increments for each marker coordinate. A final marker placement for the model is reached when the total search interval size for every coordinate falls below a user-defined threshold. Individual marker coordinates can be locked in place to prevent the algorithm from overcorrecting for data artifacts such as soft tissue artifact. This approach was used to refine model marker placements for eight able-bodied subjects performing walking trials at three stride frequencies. Across all subjects and stride frequencies, root mean square (RMS) tracking error decreased by 38.4% and RMS tracking error variance decreased by 53.7% on average. The resulting joint kinematics were in agreement with expected values from the literature. This approach results in realistic kinematics with marker tracking errors well below accepted thresholds while removing variance in the model-building procedure introduced by individual human tendencies.
机译：基于标记的运动捕捉技术通常用于测量人体运动学。这些技术需要从物理标记位置到模型标记位置的精确映射。传统方法利用手动过程来实现标记位置，从而实现精确的跟踪。在这项工作中，我们提出了一种用于模型标记放置的优化算法，以最小化动态人体运动的逆运动学分析期间的标记跟踪误差。该算法会相对于底层刚性线段按3D顺序调整模型标记的位置。对于动态运动捕捉试验执行逆运动学，以在每次更改标记位置时计算跟踪误差。跟踪误差的增加或减少确定每个标记坐标的搜索方向和增量数。当每个坐标的总搜索间隔大小低于用户定义的阈值时，将达到模型的最终标记放置位置。可以将各个标记坐标锁定在适当的位置，以防止算法对数据伪像（如软组织伪像）进行过度校正。该方法用于为八名身体健壮的受试者在三个步幅频率下进行步行试验完善模型标记的位置。在所有受试者和步幅频率上，均方根（RMS）跟踪误差平均降低了38.4％，RMS跟踪误差方差平均降低了53.7％。由此产生的联合运动学与文献中的预期值一致。这种方法产生了逼真的运动学，其标记跟踪误差大大低于可接受的阈值，同时消除了由人类倾向引起的模型构建过程中的差异。

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