Xsens Technologies B.V., Pantheon 6-8, Enschede 7521 PR, the Netherlands; Department of Biomedical Signals and Systems, Faculty of Electrical Engineering, Mathematics and Computer Science, Technical Medical Centre, University of Twente, Enschede 7500 AE, the Netherlands.
AnyBody Technology A/S, Aalborg 9220, Denmark.
Med Eng Phys. 2019 Mar;65:68-77. doi: 10.1016/j.medengphy.2018.12.021. Epub 2019 Feb 5.
Inverse dynamic analysis using musculoskeletal modeling is a powerful tool, which is utilized in a range of applications to estimate forces in ligaments, muscles, and joints, non-invasively. To date, the conventional input used in this analysis is derived from optical motion capture (OMC) and force plate (FP) systems, which restrict the application of musculoskeletal models to gait laboratories. To address this problem, we propose the use of inertial motion capture to perform musculoskeletal model-based inverse dynamics by utilizing a universally applicable ground reaction force and moment (GRF&M) prediction method. Validation against a conventional laboratory-based method showed excellent Pearson correlations for sagittal plane joint angles of ankle, knee, and hip (ρ=0.95, 0.99, and 0.99, respectively) and root-mean-squared-differences (RMSD) of 4.1 ± 1.3°, 4.4 ± 2.0°, and 5.7 ± 2.1°, respectively. The GRF&M predicted using IMC input were found to have excellent correlations for three components (vertical: ρ=0.97, RMSD = 9.3 ± 3.0 %BW, anteroposterior: ρ=0.91, RMSD = 5.5 ± 1.2 %BW, sagittal: ρ=0.91, RMSD = 1.6 ± 0.6 %BWBH), and strong correlations for mediolateral (ρ=0.80, RMSD = 2.1 ± 0.6 %BW) and transverse (ρ=0.82, RMSD = 0.2 ± 0.1 %BWBH). The proposed IMC-based method removes the complexity and space restrictions of OMC and FP systems and could enable applications of musculoskeletal models in either monitoring patients during their daily lives or in wider clinical practice.
使用肌肉骨骼建模进行逆动力学分析是一种强大的工具,它被广泛应用于各种应用中,以无创方式估计韧带、肌肉和关节中的力。迄今为止,该分析中使用的传统输入源自光学运动捕捉 (OMC) 和力板 (FP) 系统,这限制了肌肉骨骼模型在步态实验室中的应用。为了解决这个问题,我们提出使用惯性运动捕捉来通过使用通用的地面反作用力和力矩 (GRF&M) 预测方法来执行基于肌肉骨骼模型的逆动力学。与传统的基于实验室的方法进行验证表明,踝关节、膝关节和髋关节矢状面关节角度的 Pearson 相关性非常好 (ρ=0.95、0.99 和 0.99),均方根差 (RMSD) 分别为 4.1±1.3°、4.4±2.0°和 5.7±2.1°。使用 IMC 输入预测的 GRF&M 在三个分量上具有很好的相关性(垂直:ρ=0.97,RMSD=9.3±3.0%BW,前后:ρ=0.91,RMSD=5.5±1.2%BW,矢状面:ρ=0.91,RMSD=1.6±0.6%BWBH),在横向(ρ=0.80,RMSD=2.1±0.6%BW)和横向(ρ=0.82,RMSD=0.2±0.1%BWBH)上具有较强的相关性。所提出的基于 IMC 的方法消除了 OMC 和 FP 系统的复杂性和空间限制,并可以使肌肉骨骼模型在监测患者日常生活或更广泛的临床实践中的应用。