EMG-Driven Musculoskeletal Model Calibration With Wrapping Surface Personalization.
EMG-Driven Musculoskeletal Model Calibration With Wrapping Surface Personalization.
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DOI：
10.1109/tnsre.2023.3323516
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发表时间：
2023
影响因子：
4.9
通讯作者：
Fregly, Benjamin J.
中科院分区：
文献类型：
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作者：
Ao, Di;Li, Geng;Shourijeh, Mohammad S.;Patten, Carolynn;Fregly, Benjamin J.
关键词：
由肌电图（EMG）驱动的肌肉骨骼模型估计的肌肉力和关节力矩对定义肌肉肌腱长度和力臂的包裹表面几何形状很敏感。尽管具有这种敏感性，但包装表面的特性通常不能根据受试者的运动数据进行个性化设置。该研究开发了一种在肌电驱动模型校准过程中个性化OpenSim圆柱包络面的新方法。为了避免重复OpenSim肌肉分析的高计算成本，该方法使用了两级多项式代理模型。外层模型将时变的肌腱长度和力臂作为关节角度的函数，内层模型将时不变的外层多项式系数作为包裹面参数的函数。为了评估该方法，我们使用了两名中风后患者的步行数据，并进行了四种肌电图驱动的下肢模型校准：1)不校准有尺度的通用包皮面（NGA）， 2)校准所有肌肉的外水平多项式系数（SGA）， 3)仅校准具有包皮面的肌肉的外水平多项式系数（LSGA）， 4)校准具有包皮面的肌肉的圆柱形包皮面参数（PGA）。与NGA相比，SGA将下肢关节力矩匹配误差降低了31%，LSGA降低了24%，PGA降低了12%，其中髋部的误差降低幅度最大。此外，PGA将髋关节接触力峰值降低了体重的47%，这与已发表的体内测量结果最为一致。所提出的肌电驱动模型的包络面个性化校准方法产生了物理逼真的OpenSim模型，减少了关节力矩匹配误差，同时改善了髋关节接触力的预测。
Muscle forces and joint moments estimated by electromyography (EMG)-driven musculoskeletal models are sensitive to the wrapping surface geometry defining muscle-tendon lengths and moment arms. Despite this sensitivity, wrapping surface properties are typically not personalized to subject movement data. This study developed a novel method for personalizing OpenSim cylindrical wrapping surfaces during EMG-driven model calibration. To avoid the high computational cost of repeated OpenSim muscle analyses, the method uses two-level polynomial surrogate models. Outer-level models specify time-varying muscle-tendon lengths and moment arms as functions of joint angles, while inner-level models specify time-invariant outer-level polynomial coefficients as functions of wrapping surface parameters. To evaluate the method, we used walking data collected from two individuals post-stroke and performed four variations of EMG-driven lower extremity model calibration: 1) no calibration of scaled generic wrapping surfaces (NGA), 2) calibration of outer-level polynomial coefficients for all muscles (SGA), 3) calibration of outer-level polynomial coefficients only for muscles with wrapping surfaces (LSGA), and 4) calibration of cylindrical wrapping surface parameters for muscles with wrapping surfaces (PGA). On average compared to NGA, SGA reduced lower extremity joint moment matching errors by 31%, LSGA by 24%, and PGA by 12%, with the largest reductions occurring at the hip. Furthermore, PGA reduced peak hip joint contact force by 47% bodyweight, which was the most consistent with published in vivo measurements. The proposed method for EMG-driven model calibration with wrapping surface personalization produces physically realistic OpenSim models that reduce joint moment matching errors while improving prediction of hip joint contact force.