PDF(Pubmed)
Abstract：
BACKGROUND: Systems that capture motion under laboratory conditions limit validity in real-world environments. Mobile motion capture solutions such as Inertial Measurement Units (IMUs) can progress our understanding of \"real\" human movement. IMU data must be validated in each application to interpret with clinical applicability; this is particularly true for diverse populations. Our IMU analysis method builds on the OpenSim IMU Inverse Kinematics toolkit integrating the Versatile Quaternion-based Filter and incorporates realistic constraints to the underlying biomechanical model. We validate our processing method against the reference standard optical motion capture in a case report with participants with transfemoral amputation fitted with a Percutaneous Osseointegrated Implant (POI) and without amputation walking over level ground. We hypothesis that by using this novel pipeline, we can validate IMU motion capture data, to a clinically acceptable degree.
RESULTS: Average RMSE (across all joints) between the two systems from the participant with a unilateral transfemoral amputation (TFA) on the amputated and the intact sides were 2.35° (IQR = 1.45°) and 3.59° (IQR = 2.00°) respectively. Equivalent results in the non-amputated participant were 2.26° (IQR = 1.08°). Joint level average RMSE between the two systems from the TFA ranged from 1.66° to 3.82° and from 1.21° to 5.46° in the non-amputated participant. In plane average RMSE between the two systems from the TFA ranged from 2.17° (coronal) to 3.91° (sagittal) and from 1.96° (transverse) to 2.32° (sagittal) in the non-amputated participant. Coefficients of Multiple Correlation (CMC) results between the two systems in the TFA ranged from 0.74 to > 0.99 and from 0.72 to > 0.99 in the non-amputated participant and resulted in \'excellent\' similarity in each data set average, in every plane and at all joint levels. Normalized RMSE between the two systems from the TFA ranged from 3.40% (knee level) to 54.54% (pelvis level) and from 2.18% to 36.01% in the non-amputated participant.
CONCLUSIONS: We offer a modular processing pipeline that enables the addition of extra layers, facilitates changes to the underlying biomechanical model, and can accept raw IMU data from any vendor. We successfully validate the pipeline using data, for the first time, from a TFA participant using a POI and have proved our hypothesis.
RESULTS: Average RMSE (across all joints) between the two systems from the participant with a unilateral transfemoral amputation (TFA) on the amputated and the intact sides were 2.35° (IQR = 1.45°) and 3.59° (IQR = 2.00°) respectively. Equivalent results in the non-amputated participant were 2.26° (IQR = 1.08°). Joint level average RMSE between the two systems from the TFA ranged from 1.66° to 3.82° and from 1.21° to 5.46° in the non-amputated participant. In plane average RMSE between the two systems from the TFA ranged from 2.17° (coronal) to 3.91° (sagittal) and from 1.96° (transverse) to 2.32° (sagittal) in the non-amputated participant. Coefficients of Multiple Correlation (CMC) results between the two systems in the TFA ranged from 0.74 to > 0.99 and from 0.72 to > 0.99 in the non-amputated participant and resulted in \'excellent\' similarity in each data set average, in every plane and at all joint levels. Normalized RMSE between the two systems from the TFA ranged from 3.40% (knee level) to 54.54% (pelvis level) and from 2.18% to 36.01% in the non-amputated participant.
CONCLUSIONS: We offer a modular processing pipeline that enables the addition of extra layers, facilitates changes to the underlying biomechanical model, and can accept raw IMU data from any vendor. We successfully validate the pipeline using data, for the first time, from a TFA participant using a POI and have proved our hypothesis.
摘要：
背景：在实验室条件下捕获运动的系统限制了现实环境中的有效性。诸如惯性测量单元（IMU）之类的移动运动捕获解决方案可以提高我们对“真实”人类运动的理解。IMU数据必须在每个应用程序中进行验证，以解释临床适用性；对于不同的人群尤其如此。我们的IMU分析方法建立在OpenSimIMU逆运动学工具包上，该工具包集成了基于多功能四元数的过滤器，并将现实的约束纳入了基础生物力学模型。在病例报告中，我们根据参考标准的光学运动捕获来验证我们的处理方法，该病例报告中的参与者患有经股截肢，并配备了经皮骨整合植入物（POI），而没有截肢者在平坦的地面上行走。我们假设通过使用这种新颖的管道，我们可以验证IMU运动捕捉数据，达到临床可接受的程度。
结果：单侧经股截肢（TFA）的参与者和完整侧的两个系统之间的平均RMSE（跨所有关节）分别为2.35°（IQR=1.45°）和3.59°（IQR=2.00°）。非截肢参与者的等效结果为2.26°（IQR=1.08°）。在未截肢的参与者中，TFA的两个系统之间的联合水平平均RMSE范围为1.66°至3.82°，范围为1.21°至5.46°。在非截肢参与者中，TFA的两个系统之间的平面平均RMSE范围为2.17°（冠状）至3.91°（矢状）和1.96°（横向）至2.32°（矢状）。TFA中两个系统之间的多重相关系数（CMC）结果在非截肢参与者中的范围为0.74至>0.99，在0.72至>0.99之间，并且在每个数据集平均值中都具有出色的相似性，在每架飞机和所有关节级别。来自TFA的两个系统之间的归一化RMSE范围为3.40％（膝盖水平）至54.54％（骨盆水平），在未截肢的参与者中为2.18％至36.01％。
结论：我们提供模块化处理管道，可以增加额外的层，促进对底层生物力学模型的改变，并且可以接受来自任何供应商的原始IMU数据。我们使用数据成功验证了管道，第一次,来自使用POI的TFA参与者，并证明了我们的假设。
结果：单侧经股截肢（TFA）的参与者和完整侧的两个系统之间的平均RMSE（跨所有关节）分别为2.35°（IQR=1.45°）和3.59°（IQR=2.00°）。非截肢参与者的等效结果为2.26°（IQR=1.08°）。在未截肢的参与者中，TFA的两个系统之间的联合水平平均RMSE范围为1.66°至3.82°，范围为1.21°至5.46°。在非截肢参与者中，TFA的两个系统之间的平面平均RMSE范围为2.17°（冠状）至3.91°（矢状）和1.96°（横向）至2.32°（矢状）。TFA中两个系统之间的多重相关系数（CMC）结果在非截肢参与者中的范围为0.74至>0.99，在0.72至>0.99之间，并且在每个数据集平均值中都具有出色的相似性，在每架飞机和所有关节级别。来自TFA的两个系统之间的归一化RMSE范围为3.40％（膝盖水平）至54.54％（骨盆水平），在未截肢的参与者中为2.18％至36.01％。
结论：我们提供模块化处理管道，可以增加额外的层，促进对底层生物力学模型的改变，并且可以接受来自任何供应商的原始IMU数据。我们使用数据成功验证了管道，第一次,来自使用POI的TFA参与者，并证明了我们的假设。
