This paper describes a technique for using magnetic motion capture data to determine the joint parameters of an articulated hierarchy. This technique makes it possible to determine limb lengths, joint locations, and sensor placement for a human subject without external measurements. Instead, the joint parameters are inferred with high accuracy from the motion data acquired during the capture session. The parameters are computed by performing a linear least squares fit of a rotary joint model to the input data. A hierarchical structure for the articulated model can also be determined in situations where the topology of the model is not known. Once the system topology and joint parameters have been recovered, the resulting model can be used to perform forward and inverse kinematic procedures. We present the results of using the algorithm on human motion capture data, as well as validation results obtained with data from a simulation and a wooden linkage of known dimensions.

翻译：本文描述了一种利用磁性动作捕获数据确定关节参数的技术。该技术可以在不需要外部测量的情况下，准确地推断出人体主体的肢体长度、关节位置和传感器位置。通过将转动关节模型的线性最小二乘拟合应用于输入数据，计算出关节参数的值。在关节模型的拓扑结构未知的情况下，还可以确定关节模型的分层结构。一旦系统拓扑结构和关节参数被恢复，得到的模型就可以用于实现正向和反向运动学过程。我们展示了该算法在人体动作捕获数据上的结果，以及使用模拟和已知尺寸的木制链接的数据进行的验证结果。