A computationally efficient lower limb finite element musculoskeletal framework directly driven solely by inertial measurement unit sensors.
A computationally efficient lower limb finite element musculoskeletal framework directly driven solely by inertial measurement unit sensors.
复制标题
计算高效的下肢有限元肌肉骨骼框架，仅由惯性测量单元传感器直接驱动。
DOI：
10.1115/1.4053211
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Ota S
中科院分区：
文献类型：
--
作者：
Wang S;Hase K;Ota S
通过运动数据（如基于标记的运动轨迹）驱动的同步肌肉骨骼（MS）和有限元（FE）模型驱动的有限元肌肉骨骼（FEMS）方法可以深入了解膝关节次级运动学、接触力学和特定受试者生物力学研究中肌肉力之间的相互作用。然而，这些数据驱动的FEMS系统有两个主要缺点，这使得它们难以在临床环境中应用：它们的计算成本很高，并且需要昂贵且不方便的数据采集设备。在这项研究中，我们开发了一个仅由惯性测量单元（IMU）传感器驱动的下肢FEMS模型，该模型包括完整膝关节的组织几何形状，并将肌肉建模和基于弹性基础（EF）理论的膝关节接触分析结合到一个单一框架中。该模型只需要传感器测量的角速度和加速度作为输入，并根据肌肉力优化和膝关节接触力学迭代计算的收敛结果预测目标输出（膝关节接触力学、二次运动学和肌肉力）。为了评估其准确性，将该模型与体内步态实验数据进行了比较。在最大加载响应时，刚体接触分析中最大接触压力（12.6 MPa）发生在软骨内侧。与传统的可变形有限元分析相比，所提出的计算效率高的框架大大减少了计算时间（减少97.5%）。开发的框架结合了测量的便利性和计算效率，并显示了临床应用的希望，旨在了解膝关节次级运动学，接触力学和肌肉力之间的特定相互作用。
Finite element musculoskeletal (FEMS) approaches using concurrent musculoskeletal (MS) and finite element (FE) models driven by motion data such as marker-based motion trajectory can provide insight into the interactions between the knee joint secondary kinematics, contact mechanics, and muscle forces in subject-specific biomechanical investigations. However, these data-driven FEMS systems have two major disadvantages that make them challenging to apply in clinical environments: they are computationally expensive and they require expensive and inconvenient equipment for data acquisition. In this study, we developed an FEMS model of the lower limb, driven solely by inertial measurement unit (IMU) sensors, that includes the tissue geometries of the intact knee joint and combines muscle modeling and elastic foundation (EF) theory-based contact analysis of a knee into a single framework. The model requires only the angular velocities and accelerations measured by the sensors as input, and the target outputs (knee contact mechanics, secondary kinematics, and muscle forces) are predicted from the convergence results of iterative calculations of muscle force optimization and knee contact mechanics. To evaluate its accuracy, the model was compared with in vivo experimental data during gait. The maximum contact pressure (12.6 MPa) in the rigid body contact analysis occurred on the medial side of the cartilage at the maximum loading response. The proposed computationally efficient framework drastically reduced the computational time (97.5% reduction) in comparison with the conventional deformable FE analysis. The developed framework combines measurement convenience and computational efficiency and shows promise for clinical applications aimed at understanding subject-specific interactions between the knee joint secondary kinematics, contact mechanics, and muscle forces.