惯性测量单元传感器单独驱动的下肢有限元肌肉骨骼步态仿真框架的开发

Sentong Wang, K. Hase, S. Ota
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引用次数: 2

摘要

有限元肌肉骨骼(FEMS)方法使用由运动数据(如基于标记的运动轨迹)驱动的并行肌肉骨骼和有限元模型,可以在特定对象的生物力学研究中深入了解膝关节次级运动学、接触力学和肌肉力之间的相互作用。然而,这些数据驱动的FEMS系统有一个主要的缺点,使它们难以在临床环境中应用,即它们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传感器驱动的下肢FEMS模型,该传感器包括整个膝关节的组织几何形状,并将16块肌肉的建模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。目标输出(膝关节接触力学、二次运动学和肌肉力)是由肌肉力优化和膝关节接触力学迭代计算的收敛结果预测出来的。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。最大载荷响应时,最大接触压力(11.3 MPa)发生在软骨内侧。开发的框架结合了测量的便利性和精确的建模,并显示了旨在理解特定主题生物力学的临床应用前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development of a Lower Limb Finite Element Musculoskeletal Gait Simulation Framework Driven Solely by Inertial Measurement Unit Sensors
Finite element musculoskeletal (FEMS) approaches using concurrent musculoskeletal and finite element 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 a major disadvantage that makes them challenging to apply in clinical environments, i.e., 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 sensors that include the tissue geometries of the entire knee joint, and that combine modeling of 16 muscles into a single framework. The model requires only the angular velocities and accelerations measured by the sensors as input. 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 (11.3 MPa) occurred on the medial side of the cartilage at the maximum loading response. The developed framework combines measurement convenience and accurate modeling, and shows promise for clinical applications aimed at understanding subject-specific biomechanics.
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