Two simulation experiments are presented to gauge the accuracy of a new inverse kinematics method based on Bayesian inference (BIK; Pataky et al., 2019) in more realistic models than were considered previously. The first application concerns planar kinematics in the presence of soft-tissue artefacts and the second application concerns rigid body kinematics in 3D with finite helical axes (FHA). The percentage of simulations in which BIK was more accurate than least-squares based methods was only high in cases of relatively large noise magnitudes (noise SD $>$5 mm) or when the rotation magnitude was very small ($⩽$5 deg) in the 3D FHA model. Correlated parameters are the likely culprit of the low performance of BIK. Also computation time is a major deficit of the BIK approach ($\pm$20 s for the movement between two time frames). These results indicate that more research will be necessary to improve the accuracy of BIK for complex biomechanical models at realistic noise levels and to reduce computation time.

提出了两个仿真实验，以评估比以前考虑的更现实的模型中基于贝叶斯推理的新逆运动学方法（BIK; Pataky等，2019）的准确性。第一个应用程序涉及存在软组织伪像的平面运动学，第二个应用程序涉及具有有限螺旋轴（FHA）的3D刚体运动学。BIK比基于最小二乘法的方法更准确的模拟百分比仅在噪声幅度相对较大的情况下（噪声SD$>$5毫米）或旋转幅度很小（$⩽$5D）在3D FHA模型中。相关参数是BIK性能低下的原因。同样，计算时间也是BIK方法的主要缺陷（$\pm$两个时间范围之间的移动时间为20秒）。这些结果表明，需要进行更多的研究来提高复杂生物力学模型在实际噪声水平下BIK的准确性，并减少计算时间。