Equipe Robotique, Biomécanique, Sport, Santé, Institut PPRIME, UPR3346 CNRS Université de Poitiers ENSMA, Poitiers, 11, Boulevard Marie et Pierre Curie, Site du Futuroscope, F-86073 POITIERS CEDEX 9, France.
Complexity, Innovation, Sports & Motor Activities (CIAMS) laboratory, Université Paris-Saclay, 91405 Orsay cedex, France.
Med Eng Phys. 2023 Jan;111:103927. doi: 10.1016/j.medengphy.2022.103927. Epub 2022 Dec 30.
Kinematics obtained using Inertial Measurement Units (IMUs) still present significant differences when compared to those obtained using optoelectronic systems. Multibody Optimization (MBO) might diminish these differences by reducing soft-tissue artefacts - probably emphasized when using IMUs - as established for optoelectronic-based kinematics. To test this hypothesis, 15 subjects were equipped with 7 IMUs and 38 reflective markers tracked by 18 optoelectronic cameras. The subjects walked, ran, cycled on an ergocycle, and performed a task which induced joint movements in the transverse and frontal planes. In addition to lower-body kinematics computed using the optoelectronical system data, three IMU-based kinematics were computed: from IMU orientations without MBO; from MBO performed using the OpenSense add-on of the OpenSim software (OpenSim 4.2, Stanford, USA); as outputs from the commercialised MVN MBO (Xsens, Netherlands). Root Mean Square Errors (RMSE), coefficients of correlations, and differences in range of motion were calculated between the three IMU-based methods and the reference kinematics. MVN MBO seems to present a slight advantage over Direct kinematics or OpenSense MBO, since it presents 34 times out of 48 (12 degrees of freedom * 4 sports activities) a mean RMSE inferior to the Direct and OpenSense kinematics. However, it was not always significant and the differences rarely exceeded 2°. This study does not therefore conclude on a significant contribution of MBO in improving lower-body kinematics obtained using IMUs. This lack of results can partly be explained by the weakness of both the kinematic constraints applied to the kinematic chain and segment stiffening. Personalization of the kinematic chain, the use of more than one IMU by segment in order to provide information redundancy, or the use of other approaches based on the Kalman Filter might increase this MBO impact.
使用惯性测量单元（IMU）获得的运动学数据与使用光电系统获得的数据相比仍然存在显著差异。多体优化（MBO）可能会通过减少软组织伪影来缩小这些差异 - 当使用 IMU 时可能会强调这些差异，正如基于光电的运动学所建立的那样。为了验证这一假设，15 名受试者配备了 7 个 IMU 和 38 个反射标记，由 18 个光电摄像机跟踪。受试者进行了步行、跑步、在测功自行车上骑行以及执行一项在横切面和额状面引起关节运动的任务。除了使用光电系统数据计算的下半身运动学外，还计算了三种基于 IMU 的运动学：不使用 MBO 的 IMU 方向；使用 OpenSim 软件的 OpenSense 附加组件（OpenSim 4.2，美国斯坦福）执行的 MBO；以及商业化的 MVN MBO（Xsens，荷兰）的输出。在三种基于 IMU 的方法和参考运动学之间计算了均方根误差（RMSE）、相关系数和运动范围的差异。MVN MBO 似乎比直接运动学或 OpenSense MBO 略有优势，因为在 48 次（12 自由度*4 项运动）中有 34 次平均 RMSE 优于直接和 OpenSense 运动学。然而，这并不总是显著的，差异很少超过 2°。因此，本研究并未得出 MBO 在改善使用 IMU 获得的下半身运动学方面有显著贡献的结论。这种缺乏结果的情况部分可以解释为运动链应用的运动学约束和节段刚度增强的强度较弱。运动链的个性化、每个节段使用多个 IMU 以提供信息冗余，或使用基于卡尔曼滤波器的其他方法可能会增加这种 MBO 影响。
