Center for Biomedical Engineering and School of Engineering, Brown University, Providence, RI 02912, United States.
Department of Orthopedics, The Warren Alpert Medical School of Brown University and Rhode Island Hospital, Providence, RI 02903, United States.
J Biomech. 2021 May 7;120:110362. doi: 10.1016/j.jbiomech.2021.110362. Epub 2021 Mar 6.
Optical motion capture (OMC) systems are commonly used to capture in-vivo three-dimensional joint kinematics. However, the skin-based markers may not reflect the underlying bone movement, a source of error known as soft tissue artifact (STA). This study examined STA during wrist motion by evaluating the agreement between OMC and biplanar videoradiography (BVR). Nine subjects completed 7 different wrist motion tasks: doorknob rotation to capture supination and pronation, radial-ulnar deviation, flexion-extension, circumduction, hammering, and pitcher pouring. BVR and OMC captured the motion simultaneously. Wrist kinematics were quantified using helical motion parameters of rotation and translation, and Bland-Altman analysis quantified the mean difference (bias) and 95% limit of agreement (LOA). The rotational bias of doorknob pronation, a median bias of -4.9°, was significantly larger than the flexion-extension (0.7°, p < 0.05) and radial-ulnar deviation (1.8°, p < 0.01) tasks. The rotational LOA range was significantly smaller in the flexion-extension task (5.9°) compared to pitcher (11.6°, p < 0.05) and doorknob pronation (17.9°, p < 0.05) tasks. The translation bias did not differ between tasks. The translation LOA range was significantly larger in circumduction (9.8°) compared to the radial-ulnar deviation (6.3°, p < 0.05) and pitcher (3.4°, p < 0.05) tasks. While OMC technology has a wide-range of successful applications, we demonstrated it has relatively poor agreement with BVR in tracking wrist motion, and that the agreement depends on the nature and direction of wrist motion.
光学运动捕捉(OMC)系统通常用于捕获体内三维关节运动学。然而,基于皮肤的标记物可能无法反映潜在的骨骼运动,这是一种称为软组织伪影(STA)的误差源。本研究通过评估 OMC 与双平面影像摄影(BVR)之间的一致性,来研究手腕运动中的 STA。9 名受试者完成了 7 种不同的手腕运动任务:门把手旋转以捕获旋前和旋后、桡尺偏、屈伸、回旋、锤击和投球。BVR 和 OMC 同时捕获运动。手腕运动学使用旋转和平移的螺旋运动参数进行量化,Bland-Altman 分析量化了平均差异(偏差)和 95%一致性界限(LOA)。门把手旋转的旋转偏差中位偏差为-4.9°,明显大于屈伸(0.7°,p<0.05)和桡尺偏(1.8°,p<0.01)任务。屈伸任务的旋转 LOA 范围(5.9°)明显小于投球(11.6°,p<0.05)和门把手旋转(17.9°,p<0.05)任务。平移偏差在任务之间没有差异。回旋的平移 LOA 范围(9.8°)明显大于桡尺偏(6.3°,p<0.05)和投球(3.4°,p<0.05)任务。虽然 OMC 技术有广泛的成功应用,但我们证明它在跟踪手腕运动方面与 BVR 的一致性相对较差,并且这种一致性取决于手腕运动的性质和方向。